ORIGINAL_ARTICLE
A Modified Metaheuristic Algorithm Integrated ELM model for Cancer Classification
Background: In the rapidly defiled environment, cancer has emerged out as the most threatening disease to human species. Therefore, a robust classification model is required to diagnose cancer with high accuracy and less computational complexity.Method: Here, random parameters of Extreme Learning Machine (ELM) are optimized by Self Adaptive Multi-Population-based Elite strategy Jaya (SAMPEJ) algorithm. This strategy constructs a robust ELM classifier named as SAMPEJ-ELM model. This model is tested on Breast cancer, Cervical cancer and Lung cancer datasets. Here, a comparative analysis is presented between the proposed model and basic ELM, Jaya optimized ELM (Jaya-ELM), Teaching Learning Based Optimization (TLBO) optimized ELM (TLBO-ELM), SAMPEJ optimized Neural Network (SAMPEJ-NN), SAMPEJ optimized Functional Link Artificial Neural Network (SAMPEJ-FLANN) models. Numerous performance metrices viz. accuracy, specificity, Gmean, sensitivity, F-score with receiver operating characteristic (ROC) curve are used to estimate the proposed model. Moreover, this model is compared with eleven existing models.Results: SAMPEJ-ELM model resulted the highest degree of accuracy, sensitivity and specificity in Breast Cancer (.9895, 1, .9853), Cervical Cancer (.9822, .9948, .9828), Lung cancer (.9787, 1, 1) datasets. Conclusion: The experimental results reveal that SAMPEJ-ELM model classifies both the positive and negative samples of cancer datasets significantly better than others.
https://scientiairanica.sharif.edu/article_22627_e923c4ff8aad2aaaba557195db0f8e7c.pdf
2022-04-01
613
631
10.24200/sci.2022.56265.4630
Self-adaptive multi-population-based Elite Jaya algorithm
extreme learning machine
Functional Link Artificial Neural Network
classification model
P. Paramita
Debata
c117007@iiit-bh.ac.in
1
Department of Computer Science and Engineering, International Institute of Information Technology Bhubaneswar, Odisha, India, 751019
LEAD_AUTHOR
P.
Mohapatra
puspanjali@iiit-bh.ac.in
2
Department of Computer Science and Engineering, International Institute of Information Technology Bhubaneswar, Odisha, India, 751019
AUTHOR
References:
1
[1] Parkin, D. Maxwell, Freddie Bray, Jacques Ferlay, and Paola Pisani. “Estimating the world cancer burden: Globocan 2000”, International journal of cancer 94(2), pp. 153-156 (2001).
2
[2] Cooper, J.S., Porter, K., Mallin, K., Hoffman, H.T., Weber, R.S., Ang, K.K., Gay, E.G. and Langer, C.J. “National Cancer Database report on cancer of the head and neck: 10‐year update”, Head & Neck: Journal for the Sciences and Specialties of the Head and Neck, 31(6), pp.748-758 (2009).
3
[3] Duda, R.O. and Hart, P.E. “Pattern classification”, John Wiley & Sons (2006).
4
[4] Zurada, J.M. “Introduction to artificial neural systems’’ (Vol. 8), St. Paul: West (1992).
5
[5] Naik, B., Nayak, J., Behera, H.S. and Abraham, A. “A harmony search based gradient descent learning-FLANN (HS-GDL-FLANN) for classification”, In Computational Intelligence in Data Mining, (Vol. 2), pp. 525-539. Springer, New Delhi (2015).
6
[6] Bahrololoum, A., Nezamabadi-Pour, H., Bahrololoum, H. and Saeed, M. “A prototype classifier based on gravitational search algorithm”, Applied Soft Computing, 12(2), pp.819-825 (2012).
7
[7] Fernández-Navarro, F., Hervás-Martínez, C., Ruiz, R. and Riquelme, J.C. “Evolutionary generalized radial basis function neural networks for improving prediction accuracy in gene classification using feature selection”, Applied Soft Computing, 12(6), pp.1787-1800 (2012).
8
[8] Aydogan, E.K., Karaoglan, I. and Pardalos, P.M. “hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems”, Applied Soft Computing, 12(2), pp.800-806 (2012).
9
[9] Heermann, P.D. and Khazenie, N. “Classification of multispectral remote sensing data using a back-propagation neural network”, IEEE Transactions on geoscience and remote sensing, 30(1), pp.81-88 (1992).
10
[10] Malathi, V., Marimuthu, N.S. and Baskar, S. “Intelligent approaches using support vector machine and extreme learning machine for transmission line protection”, Neurocomputing, 73(10-12), pp.2160-2167 (2010).
11
[11] Cristianini, N. and Shawe-Taylor, J. “An introduction to support vector machines and other kernel-based learning methods”, Cambridge university press (2000).
12
[12] Huang, G. B., Zhu, Q. Y., and Siew, C. K. “Extreme learning machine: theory and applications”, Neurocomputing, 70(1-3), 489-501 (2006).
13
[13] Huang, G.B., Zhu, Q.Y. and Siew, C.K. “Extreme learning machine: a new learning scheme of feedforward neural networks”, IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541) vol. 2, pp. 985-990 (2004).
14
[14] Huang, G.B., Zhou, H., Ding, X. and Zhang, R. “Extreme learning machine for regression and multiclass classification”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), pp.513-529 (2011).
15
[15] Huang, G. B., Wang, D. H., and Lan, Y. “Extreme learning machines: a survey. International journal of machine learning and cybernetics”, 2(2), 107-122 (2011).
16
[16] Wang, D., and Alhamdoosh, M. “Evolutionary extreme learning machine ensembles with size control”, Neurocomputing, 102, 98-110 (2013).
17
[17] Huang, G. B., and Wang, D. “Advances in extreme learning machines (ELM2010)”, Neurocomputing, 16(74), 2411-2412 (2011).
18
[18] Feng, G., Huang, G.B., Lin, Q. and Gay, R. “Error minimized extreme learning machine with growth of hidden nodes and incremental learning”, IEEE Transactions on Neural Networks, 20(8), pp.1352-1357 (2009).
19
[19] Zhao, G., Shen, Z., Miao, C., and Man, Z. “On improving the conditioning of extreme learning machine: a linear case”, In 2009 7th International Conference on Information, Communications and Signal Processing (ICICS), pp. 1-5, IEEE (2009).
20
[20] Chen, C. P., and Wan, J. Z. “A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(1), 62-72 (1999).
21
[21] Pacifico, L. D., and Ludermir, T. B. “Improved evolutionary extreme learning machines based on particle swarm optimization and clustering approaches”, International Journal of Natural Computing Research (IJNCR), 3(3), 1-20 (2012).
22
[22] Han, F., Yao, H. F., and Ling, Q. H. “An improved evolutionary extreme learning machine based on particle swarm optimization”, Neurocomputing, 116, 87-93 (2013).
23
[23] Baliarsingh, S. K., and Vipsita, S. “Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification”, IET Syst Biol, 14(2), 85-95 (2020).
24
[24] Feng, G., Huang, G.B., Lin, Q. and Gay, R. “Error minimized extreme learning machine with growth of hidden nodes and incremental learning”, IEEE Transactions on Neural Networks, 20(8), pp.1352-1357 (2009).
25
[25] Zhu, Q.Y., Qin, A.K., Suganthan, P.N. and Huang, G.B. “Evolutionary extreme learning machine. Pattern recognition”, 38(10), pp.1759-1763 (2005).
26
[26] Yang, X. S., and Deb, S. “Multiobjective cuckoo search for design optimization”, Computers & Operations Research, 40(6), 1616-1624 (2013)..
27
[27] Aha, D. W. “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms”, International Journal of Man-Machine Studies, 36(2), 267-287 (1992).
28
[28] Rashno, A., Nazari, B., Sadri, S. and Saraee, M. “Effective pixel classification of mars images based on ant colony optimization feature selection and extreme learning machine”, Neurocomputing, 226, pp.66-79 (2017).
29
[29] Mohapatra, P., Chakravarty, S., and Dash, P. K. “An improved cuckoo search based extreme learning machine for medical data classification”, Swarm and Evolutionary Computation, 24, 25-49 (2015).
30
[30] Mohapatra, P., Chakravarty, S., and Dash, P. K. “Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system”, Swarm and Evolutionary Computation, 28, 144-160 (2016).
31
[31] Rao, R. “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems”, International Journal of Industrial Engineering Computations, 7(1), 19-34 (2016).
32
[32] Rao, R. V., Savsani, V. J., and Vakharia, D. P. “Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems”, Information sciences, 183(1), 1-15 (2012).
33
[33] Rao, R.V., More, K., Taler, J. and Ocłoń, P. “Dimensional optimization of a micro-channel heat sink using Jaya algorithm”, Applied Thermal Engineering, 103, 572-582 (2016)..
34
[34] Warid, W., Hizam, H., Mariun, N. and Abdul-Wahab, N.I. “Optimal power flow using the Jaya algorithm”, Energies, 9(9), p.678 (2016).
35
[35] Wang, S.H., Phillips, P., Dong, Z.C. and Zhang, Y.D. “Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm”, Neurocomputing, 272, pp.668-676 (2018).
36
[36] Wang, S., Rao, R.V., Chen, P., Zhang, Y., Liu, A. and Wei, L. “Abnormal breast detection in mammogram images by feed-forward neural network trained by Jaya algorithm”, Fundamenta Informaticae, 151(1-4), pp.191-211 (2017).
37
[37] Das, S.R., Mishra, D. and Rout, M. “A hybridized ELM-Jaya forecasting model for currency exchange prediction”, Journal of King Saud University-Computer and Information Sciences, 32(3), pp.345-366 (2020).
38
[38] Li, C., Nguyen, T.T., Yang, M., Yang, S. and Zeng, S. “Multi-population methods in unconstrained continuous dynamic environments: The challenges”, Information Sciences, 296, pp.95-118 (2015).
39
[39] Branke, J., Kaußler, T., Smidt, C. and Schmeck, H. “A multi-population approach to dynamic optimization problems”, In Evolutionary design and manufacture (pp. 299-307). Springer, London (2000).
40
[40] Du, W. and Li, B. “Multi-strategy ensemble particle swarm optimization for dynamic optimization”, Information sciences, 178(15), pp.3096-3109 (2008).
41
[41] Yang, S. and Li, C. “A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments”, IEEE Transactions on Evolutionary Computation, 14(6), 959-974 (2010).
42
[42] Rao, R. and Patel, V. “An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems”, international journal of industrial engineering computations, 3(4), pp.535-560 (2012).
43
[43] Rao, R. V. and Saroj, A. “An elitism-based self-adaptive multi-population Jaya algorithm and its applications”, Soft Computing, 23(12), 4383-4406 (2019).
44
[44] Venkata Rao, R., Saroj, A. and Bhattacharyya, S. “Design optimization of heat pipes using elitism-based self-adaptive multipopulation Jaya algorithm”, Journal of Thermophysics and Heat Transfer, 32(3), 702-712 (2018).
45
[45] https://archive.ics.uci.edu/ml/index.php
46
[46] Dehuri, S., Roy, R., Cho, S.B. and Ghosh, A. “An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification”, Journal of Systems and Software, 85(6), pp.1333-1345 (2012).
47
[47] Rao, R. V. and Kalyankar, V. D. “Parameters optimization of advanced machining processes using TLBO algorithm”, EPPM, Singapore, 20, 21-31(2011).
48
[48] Aruna, S., Rajagopalan, S. P. and Nandakishore, L. V. “Knowledge based analysis of various statistical tools in detecting breast cancer”, Computer Science & Information Technology, 2(2011), 37-45 (2011).
49
[49] Ceylan, Z. and Pekel, E. “Comparison of multi-label classification methods for prediagnosis of cervical cancer”, graphical models, 21, 22 (2017).
50
[50] Christobel, Angeline, and Y. Sivaprakasam. “An empirical comparison of data mining classification methods”, International Journal of Computer Information Systems 3.2, pp.24-28 (2011).
51
[51] Luukka, P. “Similarity classifier using similarity measure derived from Yu's norms in classification of medical data sets”, Computers in Biology and Medicine, 37(8), 1133-1140 (2007).
52
[52] Yang, W., Gou, X., Xu, T., Yi, X. and Jiang, M. “May. Cervical cancer risk prediction model and analysis of risk factors based on machine learning”, In Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology, pp. 50-54 (2019).
53
[53] Lavanya, D. and Rani, D. K. U. “Analysis of feature selection with classification: Breast cancer datasets”, Indian Journal of Computer Science and Engineering (IJCSE), 2(5), 756-763 (2011).
54
[54] Naseriparsa, M. and Kashani, M. M. R. “Combination of PCA with SMOTE resampling to boost the prediction rate in lung cancer dataset”, arXiv preprint arXiv:1403.1949 (2014).
55
[55] Nasution, M. Z. F., Sitompul, O. S. and Ramli, M. “PCA based feature reduction to improve the accuracy of decision tree c4. 5 classification”, In Journal of Physics: Conference Series, 978(1), pp. 012058 (2018).
56
[56] Salama, G. I., Abdelhalim, M. and Zeid, M. A. E. “Breast cancer diagnosis on three different datasets using multi-classifiers”, Breast Cancer (WDBC), 32(569), 2 (2012).
57
[57] Alharbi, A. “An Automated Computer System Based on Genetic Algorithm and Fuzzy Systems for Lung Cancer Diagnosis”, International Journal of Nonlinear Sciences and Numerical Simulation, 19(6), 583-594 (2018).
58
[58] Prabadevi, B., Deepa, N., Krithika, L.B. and Vinod, V. “Analysis of machine learning algorithms on cancer dataset”, In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), IEEE, pp. 1-10 (2020).
59
ORIGINAL_ARTICLE
Ground Vehicle and UAV Collaborative Routing and Scheduling for Humanitarian logistics using Random Walk Based Ant Colony Optimization
A well-planned humanitarian logistics involving rescuing people and providing on-time lifesaving facilities to disaster-affected areas can significantly mitigate the aftermath of disasters. However, damaged bridges and blocked roads can hinder last-mile deliveries in disaster-affected areas by ground vehicles only. So, in this paper, we propose a ground vehicle (GV) and unmanned air vehicle (UAV) collaborative delivery system in such areas. Here, a fleet of homogenous ground vehicles each equipped with a certain number of UAVs is deployed for last-mile deliveries. UAVs make the flight from GVs, deliver to end locations and return to the GV for battery replacement and/or start another flight. The objective of the model is to minimize the total delivery time within UAV flight endurance and payload constraints. Firstly K-means clustering algorithm has been used to cluster the disaster-affected region into different sectors. Then GV_Touring and UAV_Routing have been scheduled using nearest neighbor heuristic to serve ground approachable locations and UAV served locations respectively. Finally, the random walk based ant colony optimization-based (ACS_RW) has been developed to further optimize the overall travel time. Experimentation results show the potential benefits of the proposed algorithm over other available truck-drone collaborative transportation models.
https://scientiairanica.sharif.edu/article_22550_f5a3bd280b7c1cdce83783aab62814ea.pdf
2022-04-01
632
644
10.24200/sci.2021.58309.5664
Humanitarian logistics
UAV
Truck-Drone delivery
ant colony optimization
S.
Bansal
sandhya12bansal@gmail.com
1
Computer Science and Engineering Department, Maharishi Markandeshwar (Deemed) University, Mullana, India
AUTHOR
R.
Goel
rcse123@gmail.com
2
Computer Science Department, Government College, Naraingarh, Ambala, India
LEAD_AUTHOR
R.
Maini
research_raman@yahoo.com
3
Computer Science Engineering Department, Punjabi University, Patiala, India
AUTHOR
References
1
[1] Luo,Z., Liu, Z., and Shi, J. , “A Two-Echelon Cooperated Routing Problem for a Ground Vehicle and Its Carried Unmanned Aerial Vehicle,” Sensors (Basel)., vol. 17, no. 5, 2017.
2
[2] Ferrandez , S. M., Harbison, T., Weber, T., Sturges, R., and Rich, R., “Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm,” J. Ind. Eng. Manag., vol. 9, no. 2, pp. 374–388, 2016.
3
[3] Murray, C. C. and Chu, A. G., “The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery,” Transp. Res. Part C Emerg. Technol., vol. 54, pp. 86–109, 2015.
4
[4] Kenneth, S., “Metaheuristics — the metaphor exposed,” Int. Trans. Oper. Res., vol. 22, no. 1, pp. 3–18, 2015.
5
[5] Akbarpour, N., Kia,R., and Hajiaghaei-keshteli, M., “A new bi-objective integrated vehicle transportation model considering simultaneous pick-up and split delivery,” Sci. Iran., pp. 1–32, 2020.
6
[6] Yegane, B. Y., Kamalabadi, I. N., and Farughi, H., “Influence of two different producers in a competitive location problem,” Sci. Iran., vol. 27, 2020.
7
[7] Peng, H., Ying, C., Tan, S., Hu, B., and Sun, Z., “An Improved Feature Selection Algorithm Based on Ant Colony Optimization,” IEEE Access, vol. 6, pp. 69203–69209, 2018.
8
[8] Deng, W., Xu, J., and Zhao, H., “An improved ant colony optimization algorithm based on immunization strategy,” IEEE Access, vol. 7, pp. 20281–20292, 2019.
9
[9] Goel, R. K. and Bansal, S. Rani, Hybrid algorithms for rich vehicle routing problems: a survey. Elsevier Inc., 2020.
10
[10] Jovanovic, R., Tuba, M., and Voß, S., “An efficient ant colony optimization algorithm for the blocks relocation problem,” Eur. J. Oper. Res., vol. 274, no. 1, pp. 78–90, 2019.
11
[11] American Red Cross, “Drones for Disaster Response and Relief Operations,” no. April, p. 51, 2015.
12
[12] Mosterman, P. J., Sanabria, D. E., Bilgin, E., Zhang, K., and Zander, J., “A heterogeneous fleet of vehicles for automated humanitarian missions,” Comput. Sci. Eng., vol. 16, no. 3, pp. 90–95, 2014.
13
[13] Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, S. G., and Bian, L., “Drones for disaster response and relief operations: A continuous approximation model,” Int. J. Prod. Econ., vol. 188, no. February, pp. 167–184, 2017.
14
[14] Rabta, B., Wankmüller, C., and Reiner, G., “A drone fleet model for last-mile distribution in disaster relief operations,” Int. J. Disaster Risk Reduct., vol. 28, no. August 2017, pp. 107–112, 2018.
15
[15] Silva, L. de O., Bandeira, R. A. de M. and Campos, V. B. G. “Proposal to planning facility location using UAV and geographic information systems in a post-disaster scenario,” Int. J. Disaster Risk Reduct., vol. 36, no. February, p. 101080, 2019.
16
[16] Chauhan, D., Unnikrishnan, A., and Figliozzi, M., “Maximum coverage capacitated facility location problem with range constrained drones,” Transp. Res. Part C Emerg. Technol., vol. 99, no. December, pp. 1–18, 2019.
17
[17] Kitjacharoenchai, P., Ventresca, M., Moshref-Javadi, M., Lee, S., Tanchoco, J. M. A., and Brunese, P. A., “Multiple traveling salesman problem with drones: Mathematical model and heuristic approach,” Comput. Ind. Eng., vol. 129, no. June 2018, pp. 14–30, 2019.
18
[18] Murray, C. C. and Raj,R., “The multiple flying sidekicks traveling salesman problem: Parcel delivery with multiple drones,” Transp. Res. Part C Emerg. Technol., vol. 110, no. February 2019, pp. 368–398, 2020.
19
[19] Liu,Y., Liu,Z., Shi,J., Wu,G., and Pedrycz,W., “Two-Echelon Routing Problem for Parcel Delivery by Cooperated Truck and Drone,” IEEE Trans. Syst. Man, Cybern. Syst., no. September 2016, pp. 1–16, 2020.
20
[20] Abbatecola,L., Fanti, M. P., Fellow,I., Pedroncelli,G., Ukovich,W. and Ieee,M.,“A New Cluster-Based Approach for the Vehicle Routing Problem with Time Windows,” in 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018, pp. 744–749.
21
[21] Nalepa, J. and Blocho, M., “Adaptive guided ejection search for pickup and delivery with time windows,” J. Intell. Fuzzy Syst., vol. 32, no. 2, pp. 1547–1559, 2017.
22
[22] Alparslan, A. and Science, T., “COMPARISON OF DIFFERENT CLUSTERING,” Int j simul Model, vol. 18, no. 4, pp. 574–585, 2019.
23
[23] Liu, J., Yang, J., Liu, H., Tian, X. and Gao, M., “An improved ant colony algorithm for robot path planning,” Soft Comput., vol. 21, no. 19, pp. 5829–5839, 2017.
24
[24] Kamaruzaman, A. F. and Zain, A. M. “Levy Flight Algorithm for Optimization Problems – A Literature Review Levy Flight Algorithm for Optimization Problems – A Literature Review,” no. September, 2013.
25
[25] Goel, R. and Maini, R. “A hybrid of ant colony and firefly algorithms ( HAFA ) for solving vehicle routing problems,” vol. 25, pp. 28–37, 2018.
26
[26] Goel, R. and Maini, R. and Bansal, S. “Vehicle routing problem with time windows having stochastic customers demands and stochastic service times : Modelling and solution,” J. Comput. Sci., vol. 34, pp. 1–10, 2019.
27
[27] Zhu, H., You, X. and Liu, S. “Multiple Ant Colony Optimization Based on Pearson Correlation Coefficient,” IEEE Access, vol. 7, pp. 61628–61638, 2019.
28
[28] Metawa, U. J, N., Shankar, K. and Lakshmanaprabu, S. K., “Financial crisis prediction model using ant colony optimization,” Int. J. Inf. Manage., vol. 50, no. November 2018, pp. 538–556, 2020.
29
[29] Bansal, S. Rani, Goel, R. K., and Maini, R., “An improved ant colony algorithm based on levy flight distribution,” Adv. Math. Sci. J., vol. 9, no. 6, pp. 3907–3916, 2020.
30
[30] Li, Y., Soleimani, H. and Zohal, M. “An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives,” J. Clean. Prod., 2019.
31
[31] Rao, T. S., “An Ant Colony and Simulated Annealing Algorithm with Excess Load VRP in a FMCG Company,” in IOP Conference Series: Materials Science and Engineering, Volume 577, International Conference on Advances in Materials and Manufacturing Applications (IConAMMA-2018) 16–18 August 2018, Bengaluru, India, 2019.
32
ORIGINAL_ARTICLE
Expansion planning of automated sub-transmission and distribution networks integrated by distributed generations
This paper presents sub-transmission and distribution network expansion planning (S&DEP) including distributed generation (DG) and distribution automation (DA) considering reliability indexes. The objective function is to minimize investment, operation, maintenance and reliability costs subjected to AC power flow, system operation and generating units and DG limits, reliability, and distribution automation constraints (including the constraints of protection devices and volt/VAr control mechanism). The proposed model is a mixed integer non-linear programming (MINLP) model which is hard to solve. For this reason, a MINLP problem is transformed to mixed integer linear programming (MILP) model. The validity of the proposed method is investigated in the two synthetic test networks.
https://scientiairanica.sharif.edu/article_21666_61feaa29bec054d91839c82432a4a1ba.pdf
2022-04-01
645
659
10.24200/sci.2019.5435.1270
Sub-transmission and distribution network expansion planning (S&DNEP)
Distributed generation (DG)
Distributed automation (DA)
Reliability indexes
and mixed integer linear programming (MILP)
M.
Zohour-Attar
zohuratar@yahoo.com
1
Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran
AUTHOR
J.
Aghaei
aghaei@sutech.ac.ir
2
- Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran - School of Energy Systems, Lappeenranta-Lahti University of Technology (LUT), Lappeenranta, Finland
LEAD_AUTHOR
T.
Niknam
niknam@sutech.ac.ir
3
Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran
AUTHOR
A.
Nikoobakht
a.nikoobakht@eghlid.ac.ir
4
Higher Education Center of Eghlid, Eghlid, Iran
AUTHOR
References:
1
[1] Gao, Y., Hu, X., Yang, W., Liang, H. and Li, P. “Multi-Objective Bilevel Coordinated Planning of Distributed Generation and Distribution Network Frame Based on Multi-scenario Technique Considering Timing Characteristics”, IEEE Transactions on Sustainable Energy, 8(4), pp. 1415-1429 (2017).
2
[2] Heidari, S., Fotuhi-Firuzabad, M. and Lehtonen, M. “Planning to Equip the Power Distribution Networks with Automation System”, IEEE Transactions on Power Systems, 32(5), pp. 3451-3460 (2017).
3
[3] Moradi, S., Ghaffarpour, R., Ranjbar, A., Mozaffari, B. “Optimal integrated sizing and planning of hubs with midsize/large CHP units considering reliability of supply”, Energy Conversion and Management, 148, pp. 974-992 (2017).
4
[4] Mazhari, S. M., Monsef H. and Romero, R. "A Multi-Objective Distribution System Expansion Planning Incorporating Customer Choices on Reliability", IEEE Transactions on Power Systems, 31(2), pp. 1330-1340 (2016).
5
[5] Koutsoukis, N. C., Georgilakis P. S. and Hatziargyriou, N. D. "Multistage Coordinated Planning of Active Distribution Networks", IEEE Transactions on Power Systems, 33(1), pp. 32-44 (2018).
6
[6] Ahmadigorji, M., Amjady, N. “A new evolutionary solution method for dynamic expansion planning of DG-integrated primary distribution networks”, Energy Conversion and Management, 82, pp. 61-70 (2014).
7
[7] Naderi E. and Seifi H. “A Dynamic Approach for Distribution System Planning Considering Distributed Generation”, IEEE Trans Power Delivery, 27(3), pp. 1313-1322 (2012). [8] Zou, K., Prakash, A., Muttaqi, K.M. “Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties” , IEEE Trans. Sustainable Energy, 3(1), pp. 112-123 (2012).
8
[9] Bagheri, A., Monsef, H., Lesani, H. “Integrated distribution network expansion planning incorporating distributed generation considering uncertainties, reliability, and operational conditions” , Int. J. Electrical Power and Energy Systems, 73, pp. 56–70 (2015).
9
[10] Saboori, H., Hemmati R. and Abbasi V. “Multistage distribution network expansion planning considering the emerging energy storage systems”, Energy Conversion and Management, 105, pp. 938–945 (2015).
10
[11] Heidari, S., Fotuhi-Firuzabad M. and Kazemi, S. “Power distribution network expansion planning considering distribution automation” , IEEE Trans. Power Syst., 30(3), pp. 1261-1269 (2015).
11
[12] Munoz-Delgado, G., Contreras J. and Arroyo, J. M. “Joint expansion planning of distributed generation and distribution networks”, IEEE Trans. Power Syst., 30(5), pp. 2579-2590 (2015).
12
[13] AlKaabi, S. S., Zeineldin H. H. and Khadkikar, V. “Planning active distribution networks considering multi-DG configurations”, IEEE Trans. Power Syst., 29(9), pp. 785-793 (2014).
13
[14] Gonecalves, R. R., Franco, J. F., Rider, M. J. “Short-term expansion planning of radial electrical distribution sustems using mixed-integer liner programming”, IET Gener. Transm. Distrib., 9(3), pp. 256-266 (2015).
14
[15] Karimi M. and Haghifam, M. R. "Risk based multi-objective dynamic expansion planning of sub-transmission network in order to have eco-reliability, environmental friendly network with higher power quality”, IET Generation, Transmission & Distribution, 11(1), pp. 261-271 (2017).
15
[16] Shen, X., Shahidehpour, M., Han, Y., Zhu, S. and Zheng, J. “Expansion Planning of Active Distribution Networks With Centralized and Distributed Energy Storage Systems”, IEEE Transactions on Sustainable Energy, 8(1), pp. 126-134 (2017).
16
[17] Xing, H., Cheng, H., Zhang Y. and Zeng, P. “Active distribution network expansion planning integrating dispersed energy storage systems”, IET Generation, Transmission & Distribution, 10(3), pp. 638-644 (2016).
17
[18] Muñoz-Delgado, G., Contreras, J. and Arroyo, J. M. "Multistage Generation and Network Expansion Planning in Distribution Systems Considering Uncertainty and Reliability”, IEEE Transactions on Power Systems, 31(5), pp. 3715-3728 (2016).
18
[19] Shivaie, M., Ameli, M.T., Sepasian, M.S., Weinsier, P.D., Vahidinasab, V. “A multistage framework for reliability-based distribution expansion planning considering distributed generations by a self-adaptive global-based harmony search algorithm“, Reliability Engineering & System Safety, 139, pp. 68-81 (2015).
19
[20] EPE, Relatório EPE-DEE-RE-081/2013, “Estudo de Suprimento à Região Metropolitana de Cuiabá – Mato Grosso”, August (2013).
20
[21] Kermanshahi, B. and Kamel, R.M. “Optimal Size and Location of Distributed Generations for Minimizing Power Losses in a Primary Distribution Network”, Scientia Iranica, 16(2), pp. 137-144 (2009).
21
[22] Memarzadeh, G., Esmaeili, S. “Voltage and reactive power control in distribution network considering optimal network configuration and voltage security constraints”, Scientia Iranica, (), pp. -. (2018) doi: 10.24200/sci.2018.20565
22
[23] Billinton, R. and Grover, M. S. “Reliability evaluation in distribution and transmission systems”, PROC. IEE, 122(5), pp. 517-524 (1975).
23
[24] Garey, M.R. and Johnson, D.S. “Computers and intractability: a guide to the theory of NP completeness”, Freeman, 1979.
24
[25] Papadimitriou, C.H. and Steiglitz, K. “Combinatorial Optimization: Algorithms and Complexity”, Dover, 1998.
25
[26] Pirouzi, S., Aghaei, J., Vahidinasab, V., Niknam, T., and Khodaei, A. “Robust linear architecture for active/reactive power scheduling of EV integrated smart distribution networks”, Electric Power System Research, 155, pp. 8-20 (2018).
26
[27] Pirouzi, S., Aghaei, Niknam, T., Farahmand, H. and Korpås, M. “Proactive Operation of Electric Vehicles in Harmonic Polluted Smart Distribution Networks”, IET Generation, Transmission and distribution, 12, pp. 967-975 (2018).
27
[28] Pirouzi, S., Aghaei, Niknam, T., Shafie-khah, M., Vahidinasab, V. and Catalão, J.P.S. “Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks”, Energy, 141, pp. 635-652 (2017).
28
[29] Generalized Algebraic Modeling Systems (GAMS). [Online]. Available: http://www.gams.com.
29
ORIGINAL_ARTICLE
Tuning the implementable structures of fractional-order PID controllers for control of FOPDT processes
This study presents a set of rules for optimal tuning a class of integer-order controllers, known as implementable fractional-order PID controllers, to be applied in control of first-order-plus-dead-time (FOPDT) processes. To this aim, the approach of so-called “tuning based on the implementable form of the controller” is applied instead of the common approach of “tuning based on the ideal form of the controller”. Consequently, no contradiction is found between the behavior of the tuned controller and that of the implemented controller. Also, algebraic relations between the values of cost functions, which are defined based on integral square error (ISE) and integral square time error (ISTE) performance indices, and free parameters of the implementable controller are established. Tuning implementable fractional-order PID controllers via the proposed rules guarantees that the values of performance indices are reduced in comparison with the case of using optimal PID controllers. In addition to numerical results, experimental results are also provided to demonstrate the effectiveness of the proposed tuning rules in practical applications.
https://scientiairanica.sharif.edu/article_21686_0c8f5d2641661d98c6f50ffa23f849f9.pdf
2022-04-01
660
675
10.24200/sci.2019.51703.2321
Optimal tuning, Implementable fractional-order PID controller
Integer-order approximation
optimization
ISE performance index
ISTE performance index
M.
Ashjaee
mehrdadashjaee@gmail.com
1
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
M. S.
Tavazoei
tavazoei@sharif.edu
2
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
LEAD_AUTHOR
REFERENCES:
1
[1] Dzieliński, A., Sarwas, G. and Sierociuk, D. “Comparison and validation of integer and fractional order ultracapacitor models”, Advances in Difference Equations, 2011(1), pp. 11 (2011).
2
[2] Radwan, A.G., Soliman, A.M. and Elwakil, A.S. “Design equations for fractional‐order sinusoidal oscillators: Four practical circuit examples”, International Journal of Circuit Theory and Applications, 36(4), pp. 473-492 (2008).
3
[3] Gabano, J.D., Poinot, T. and Kanoun, H. “Identification of a thermal system using continuous linear parameter-varying fractional modelling”, IET Control Theory and Applications, 5(7), pp. 889-899 (2011).
4
[4] Xu, J. and Li, J. “Stochastic dynamic response and reliability assessment of controlled structures with fractional derivative model of viscoelastic dampers”, Mechanical Systems and Signal Processing, 72, pp. 865-896 (2016).
5
[5] Kumar, S. “A new fractional analytical approach for treatment of a system of physical models using Laplace transform”, Scientia Iranica, 21(5), pp. 1693-1699 (2014).
6
[6] Ionescu, C.M. and De Keyser, R.. “Relations between fractional-order model parameters and lung pathology in chronic obstructive pulmonary disease”, IEEE Transactions on Biomedical Engineering, 56(4), pp. 978-987 (2009).
7
[7] Taghavian, H. and Tavazoei, M.S. “Analytic solution of a system of linear distributed order differential equations in the Reimann-Liouville sense”, Scientia Iranica, DOI: 10.24200/SCI.2018.20335, (2018).
8
[8] Podlubny, I. “Fractional-order systems and PI D controllers”, IEEE Transactions on Automatic Control,
9
44(1), pp. 208-214 (1999).
10
[9] Luo, Y. and Chen, Y. “Fractional order [proportional derivative] controller for a class of fractional order systems”, Automatica, 45(10), pp. 2446-2450 (2009).
11
[10] Padula, F. and Visioli, A. “Tuning rules for optimal PID and fractional-order PID controllers”, Journal of Process Control, 21(1), pp. 69-81 (2011).
12
[11] Fergani, N. and Charef, A. “Process step response based fractional PI D controller parameters tuning for
13
desired closed loop response”, International Journal of Systems Science, 47(3), pp. 521-532 (2016).
14
[12] Rahimian, M.A. and Tavazoei, M.S. “Improving integral square error performance with implementable fractional‐order PI controllers”, Optimal Control Applications and Methods, 35(3), pp. 303-323 (2014).
15
[13] Monje, C.A., Vinagre, B.M., Feliu, V., et al. “Tuning and auto-tuning of fractional order controllers for industry applications”, Control Engineering Practice, 16(7), pp. 798-812 (2008).
16
[14] Li, H., Luo, Y. and Chen, Y. “A fractional order proportional and derivative (FOPD) motion controller: tuning rule and experiments”, IEEE Transactions on Control Systems Technology, 18(2), pp. 516-520 (2010).
17
[15] Roy, P. and Roy, B.K. “Fractional order PI control applied to level control in coupled two tank MIMO system with experimental validation”, Control Engineering Practice, 48, pp. 119-135 (2016).
18
[16] Khubalkar, S., Chopade, A., Junghare, A., et al. “Design and realization of stand-alone digital fractional order PID controller for buck converter fed DC motor”, Circuits, Systems and Signal Processing, 35(6), pp. 2189-2211 (2016).
19
[17] Sayyaf, N. and Tavazoei, M.S. “Robust Fractional-Order Compensation in the Presence of Uncertainty in a Pole/Zero of the Plant”, IEEE Transactions on Control Systems Technology, 26(3), pp. 797- 812 (2018).
20
[18] Sayyaf, N. and Tavazoei, M.S. “Desirably Adjusting Gain Margin, Phase Margin and Corresponding Crossover Frequencies Based on Frequency Data”, IEEE Transactions on Industrial Informatics, 13(5), pp. 2311-2321 (2017).
21
[19] Badri, V. and Tavazoei, M.S. “Some Analytical Results on Tuning Fractional-Order [Proportional-Integral] Controllers for Fractional-Order Systems”, IEEE Transactions on Control Systems Technology, 24(3), pp. 1059-1066 (2016).
22
[20] Karaboga, D. and Akay, B. “Proportional-integral-derivative controller design by using artificial bee colony, harmony search and the bees algorithms”, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 224(7), pp. 869-883 (2010).
23
[21] Kesarkar, A.A. and Selvaganesan, N. “Tuning of optimal fractional-order PID controller using an artificial bee colony algorithm”, Systems Science and Control Engineering, 3(1), pp. 99-105 (2015).
24
[22] Padula, F. and Visioli, A. “Optimal tuning rules for proportional-integral-derivative and fractional-order proportional-integral-derivative controllers for integral and unstable processes”, IET Control Theory and Applications, 6(6), pp. 776-786 (2012).
25
[23] Chang, L.Y. and Chen, H.C. “Tuning of fractional PID controllers using adaptive genetic algorithm for active magnetic bearing system”, WSEAS Transactions on Systems, 8(1), pp. 158-167 (2009).
26
[24] Cao, J.Y. and Cao, B.G. “Design of fractional order controllers based on particle swarm optimization”, 1ST IEEE Conference on Industrial Electronics and Applications, pp. 1-6 (2006).
27
[25] Ateş, A. and Yeroglu, C. “Optimal fractional order PID design via Tabu Search based algorithm”, ISA Transactions, 60, pp. 109-118 (2016).
28
[26] Das, S., Pan, I. and Das, S. “Multi-objective LQR with optimum weight selection to design FOPID controllers for delayed fractional order processes”, ISA Transactions, 58, pp. 35-49 (2015).
29
[27] Tavazoei, M.S. and Haeri, M. “Rational approximations in the simulation and implementation of fractional-order dynamics: A descriptor system approach”, Automatica, 46(1), pp. 94-100 (2010).
30
[28] Rahimian, M.A. and Tavazoei, M.S. “Optimal tuning for fractional-order controllers: an integer-order approximating filter approach”, ASME Journal of Dynamic Systems, Measurement and Control, 135(2), (2013).
31
[29] Kinney, T.B. “Tuning process controllers”, Chemical Engineering, 90(19), pp. 67-72 (1983).
32
[30] Podlubny, I., Petraš, I., Vinagre, B.M., et al. “Analogue realizations of fractional-order controllers”, Nonlinear Dynamics, 29(1), pp. 281-296 (2002). [31] Machado, J.A. “Delay approximation of fractional integrals”, Asian Journal of Control, 15(3), pp. 713-722 (2013).
33
[32] Ogata, K. “Modern Control Engineering”, Prentice Hall of India, New Delhi (1982).
34
[33] Sundaravadivu, K., Arun, B. and Saravanan, K. “Design of fractional order PID controller for liquid level control of spherical tank”, IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 291-295 (2011). [34] Walton, K. and Marshall, J.E. “Closed form solution for time delay systems' cost functionals”, International Journal of Control, 39(5), pp. 1063-1071 (1984).
35
[35] Kealy, T. and O'Dwyer, A. “Analytical ISE calculation and optimum control system design”, In Proceedings of the Irish Signals and Systems Conference, Limerick, Ireland, pp.418-423 (2003).
36
[36] Shmakov, S.L. “A universal method of solving quartic equations”, International Journal of Pure and Applied Mathematics, 71(2), pp. 251-259 (2011).
37
[37] Kreyszig, E. and Norminton, E.J. “Advanced engineering mathematics”, 4th Edn., Wiley, New York (1993).
38
[38] Walton, K., Ireland, B. and Marshall, J.E. “Evaluation of weighted quadratic functional for time-delay systems”, International Journal of Control, 44(6), pp. 1491-1498 (1986).
39
[39] Nocedal, J., Wright, S.J. “Numerical optimization”, Springer-Verlag, New York.
40
[40] Courant, R. “Differential and integral calculus”, 2th Edn., John Wiley and Sons.
41
[41] Zhuang, M. and Atherton, D.P. “Automatic tuning of optimum PID controllers”, In IEE Proceedings D (Control Theory and Applications), 140(3), pp. 216-224 (1993).
42
[42] Nash, J.C. “Compact numerical methods for computers: linear algebra and function minimization”, CRC press.
43
[43] Padula, F. and Visioli, A. “Advances in Robust Fractional Control”, Springer (2015).
44
ORIGINAL_ARTICLE
A comparative study of economic load dispatch with complex non-linear constraints using salp swarm algorithm
Economic Load Dispatch (ELD) is an important part of cost minimization procedure in power system operation. Different derivative and probabilistic methods are used to solve ELD problems. This paper proposes a powerful Salp Swarm Algorithm (SSA) to explain the ELD issue including equality and inequality restrictions. The main aim of ELD is to satisfy the entire electric load at minimum cost. The SSA is a population based probabilistic method which guides its search agents that are randomly placed in the search space, towards an optimal point using their fitness function and also keeps a track of the best solution achieved by each search agent. SSA is being used to solve the ELD problem with their high exploration and local optima escaping technique. This algorithm confirms that the promising areas of the search space are exploited to have a smooth transition from exploration to exploitation using the movement of Salps in the sea. Simulation results prove that the proposed algorithm surpasses other existing optimization techniques in terms quality of solution obtained and computational efficiency. The final results also prove the robustness of the SSA.
https://scientiairanica.sharif.edu/article_21817_64d322752160e975a62fc2370b18c327.pdf
2022-04-01
676
692
10.24200/sci.2020.52145.2562
Economic Load Dispatch
optimization
Prohibited operating zone
Salp Swarm Algorithm
Valve-point loading
K.
Bhattacharjee
kuntal.bhattacharjee@nirmauni.ac.in
1
Institute of Technology, Electrical Engineering Department, Nirma University, Ahmedabad, Gujarat, India
LEAD_AUTHOR
N.
Patel
16meee18@nirmauni.ac.in
2
Institute of Technology, Electrical Engineering Department, Nirma University, Ahmedabad, Gujarat, India
AUTHOR
References:
1
[1] Dhar R. N. and Mukherjee P. K., “Reduced-gradient method for economic dispatch” Electrical Engineers, Proceedings of the Institution of, Volume: 120, Issue: 5 Pages: 608 – 610 (1973).
2
[2] Aoki K. and Satoh T., “Economic Dispatch with Network Security Constraints Using Parametric Quadratic Programming” IEEE Power Engineering Review, Volume: PER-2, Issue: 12 Pages: 37 – 38 (1982).
3
[3] El-Keib AA and Ma H, Hart J.L., “Environmentally constrained economic dispatch using the Lagrangian relaxation method” IEEE Trans Power Syst; 9(4):1723–1729 (1994).
4
[4] Su C. T. and Lin C. T., “New approach with a Hopfield modeling framework to economic dispatch” IEEE Transactions on Power Systems, Volume: 15, Issue: 2, Pages: 541 – 545 (2000).
5
[5] Jabr R. A., Coonick A. H. and Cory B. J., “A homogeneous linear programming algorithm for the security constrained economic dispatch problem” IEEE Transactions on Power Systems, Volume: 15, Issue: 3 Pages: 930 – 936 (2000).
6
[6] Swamp K. S. and Natarajan A., “Constrained optimization using evolutionary programming for dynamic economic dispatch” Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, 2005, Pages: 314 – 319 (2005).
7
[7] Farooqi M. Jain R, P. and Niazi K. R.., “Using Hopfield neural network for economic dispatch of power systems” Proceedings. National Power Engineering Conference, PECon 2003. Pages: 5 – 10 (2003).
8
[8] Reis Nascimento M. H., Nunes M. V. A., Rodriguez J. L. M., Leite J. C., "A new solution to the economical load dispatch of power plants and optimization using differential evolution." Electrical Engineering 99.2: 561-571 (2017).
9
[9] Khatir A. A., Motamedi A., Sadati N.et al, “Fuzzy economic dispatch and spinning reserve allocation using evolutionary programming” 2008 40th North American Power Symposium, Pages: 1 – 5 (2008).
10
[10] Bavafa M., Monsef H. and Navidi N., “A New Hybrid Approach for Unit Commitment Using Lagrangian Relaxation Combined with Evolutionary and Quadratic Programming” 2009 Asia-Pacific Power and Energy Engineering Conference, Pages: 1 – 6 (2009).
11
[11] Wang Y., Zhou J., Xiao W., Zhang Y., “Economic Load Dispatch of Hydroelectric Plant Using a Hybrid Particle Swarm Optimization Combined Simulation Annealing Algorithm” Second WRI Global Congress on Intelligent Systems, Volume: 2 Pages: 231 – 234 (2010).
12
[12] Anand H. and Narang N., “Civilized swarm optimization for combined heat and power economic emission dispatch” 2016 7th India International Conference on Power Electronics (IICPE), Pages: 1 – 6 (2016).
13
[13] Chaturvedi K.T., Pandit M. and Srivastava L., “Particle swarm optimization with crazy particles for nonconvex economic dispatch” Appl Soft Comput., 9:962–969 (2009).
14
[14] Lu H., Sriyanyong P., Song Y.H. and Dillon T., “Experimental study of a new hybrid PSO with mutation for economic dispatch with non smooth cost function” Int J Electr Power Energy Syst.;32:921–935 (2010).
15
[15] Niknam T., Golestane F. and Bahmanifirouzi B., “Modified adaptive PSO algorithm to solve dynamic economic dispatch” 2011 IEEE Power Engineering and Automation Conference, Volume: 1 Pages: 108 – 111 (2011).
16
[16] King D. J. and Oezveren Warsono C. S., “A Genetic Algorithm Based Economic Dispatch (GAED) with Environmental Constraint Optimisation Universities” Power Engineering Conference (UPEC), Proceedings of 2011 46th International, Pages: 1 – 6 (2011).
17
[17] Jamal S. Alsumait and Sykulski Jan K., “Solving economic dispatch problem using hybrid GA-PS-SQP method” IEEE EUROCON 2009, Pages: 333 – 338 (2009).
18
[18] Amjady N. and Nasiri-Rad H., “Solution of non-convex and non-smooth economic dispatch by a new adaptive real coded genetic algorithm” Expert Syst Appl.; 37:5239–5245 (2010).
19
[19] Hazra J. and Sinha A.K., “Environmental Constrained Economic Dispatch using Bacteria Foraging Optimization” 2008 Joint International Conference on Power System Technology and IEEE Power India Conference, Pages: 1 – 6 (2008).
20
[20] Maity D., Banerjee S. and Chanda C.K., “Multi-objective economic emission load dispatch using modified biogeography based optimization algorithm” 2016 IEEE 7th Power India International Conference (PIICON), Pages: 1 – 6 (2016).
21
[21] Nagur P. N., Raj S. and Jadhav H T, “Modified Artificial Bee Colony algorithm for non-convex economic dispatch problems” 2012 International Conference on Green Technologies (ICGT), Pages: 258 – 262 (2012).
22
[22] Shaw B., Mukherjee V. and Ghoshal S. P., “Seeker optimisation algorithm: application to the solution of economic load dispatch problems” IET Generation, Transmission & Distribution, Volume: 5, Issue: 1 Pages: 81 – 91 (2011).
23
[23] Rahmat N. Musirin A., I., Abidin A. F., Ahmed M. R., “Economic load dispatch with valve-point loading effect by using Differential Evolution Immunized Ant Colony optimization technique” 2014 Australasian Universities Power Engineering Conference (AUPEC), Pages: 1 – 6 (2014).
24
[24] Khamsawang S., Pothiya S. and Boonseng C., “Distributed tabu search algorithm for solving the economic dispatch problem” 2004 IEEE Region 10 Conference TENCON 2004, Vol. 3, Pages: 484 – 487 (2004).
25
[25] Maity D., Banerjee S. and Chanda C. K., “Multi-objective economic emission load dispatch using modified biogeography based optimization algorithm” 2016 IEEE 7th Power India International Conference (PIICON), Pages: 1 – 6 (2016).
26
[26] Roy P. K. and Mandal D., “Quasi-oppositional biogeography-based optimization for multi-objective optimal power flow” Electric Power Component System, 40:236–256 (2012).
27
[27] Bhattacharya A. and Chattopadhyay P. K., “Oppositional Biogeography-Based Optimization for multi-objective Economic Emission Load Dispatch” 2010 Annual IEEE India Conference (INDICON), Pages: 1 – 6 (2010).
28
[28] Arul R., Velusami S. and Ravi G., “Solving combined economic emission dispatch problems using self-adaptive differential harmony search algorithm” 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], Pages: 757 – 762 (2014).
29
[29] Chatterjee A, Ghoshal S.P. and Mukherjee V., “Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm” Int J Electr Power Energy Syst., 39(1):9–20 (2012).
30
[30] Ghosh B., Dey B. and Bhattacharya A., “Solving economic load dispatch problem using hybrid Krill Herd algorithm” 2015 International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)(2015).
31
[31] Das S., Bhattacharya A. and Chakraborty A. K. , “Solution of short-term hydrothermal scheduling using sine cosine algorithm” Soft Computing (2017).
32
[32] Qin Q., Cheng S., Chua X., Lei X. and Shi Y., “Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization” Applied Soft Computing Volume 59, Pages 229-242 (2017).
33
[33] Roy S., “The maximum likelihood optima for an economic load dispatch in presence of demand and generation variability” Energy Volume 147, Pages 915-923 (2018).
34
[34] Roy S., Bhattacharjee K. and Bhattacharya A., "A Modern Approach to Solve of Economic Load Dispatch using Group Leader Optimization Technique” International Journal of Energy Optimization and Engineering, IGI-Global. Vol-6, Issue-1,Article-4, pp. 66-85 (2016).
35
[35] Bhattacharjee K., Bhattacharya A. and Halder S., “Teaching Learning Based Optimization for Different Economic Dispatch Problems” International Journal of Science and Technology, Scientia Iranica Volume 21, Issue 3, pp-870-884 (2014).
36
[36] Júnior A. B., Jorge De, M. V. A. Nunes, M. H. R. Nascimento, J. L. M. Rodríguez, J. C. Leite, “Solution to economic emission load dispatch by simulated annealing: case study”. Electrical Engineering, 1-13 J. C. (2017).
37
[37] Kaveh A., Vaez S. R. H. and Hosseini P., “Enhanced vibrating particles system algorithm for damage identification of truss structures”, Scientia Iranica, (2017).
38
[38] Singh D. and Dhillon J.S., “Ameliorated grey wolf optimization for economic load dispatch problem” Energy Volume 169, Pages 398-419 (2019).
39
[39] Kumara M. and Dhillon J. S., “Hybrid artificial algae algorithm for economic load dispatch” Applied Soft Computing Volume 71, Pages 89-109 (2018).
40
[40] Aghay Kaboli S. Hr. and Abdullah K. A., “Solving non-convex economic load dispatch problem via artificial cooperative search algorithm” Expert Systems with Applications Volume 128, Pages 14-27 (2019).
41
[41] Gholamghasemi M., Akbari E. and Asadpoor M. B., “A new solution to the non-convex economic load dispatch problems using phasor particle swarm optimization” Applied Soft Computing Volume 79, Pages 111-124 (2019).
42
[42] Bulbul Sk Md Ali , Pradhan M. , Roy P. K. , Pal T., “Opposition-based krill herd algorithm applied to economic load dispatch problem” Ain Shams Engineering Journal, Volume 9, Issue 3, Pages 423-440 (2018).
43
[43] Zhang Q. , Zou D. and Duan N., “An adaptive differential evolutionary algorithm incorporating multiple mutation strategies for the economic load dispatch problem” Applied Soft Computing, Volume 78, Pages 641-669 (2019).
44
[44] Mirjalili S., Gandomi A. H., Mirjalili S. Z.,et al, “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems” Advances in Engineering Software 114 (2017) 163–191.
45
[45] X. He, Y. Rao and J. Huang, “Novel algorithm for economic load dispatch of power systems” Neurocomputing 171(2016)1454–1461
46
[46] Lu H., Sriyanyong P., Song Y. H., Dhilon T., “Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function” Electrical Power and Energy Systems 32 (2010) 921–935
47
[47] Ali Bulbul S M, Pradhan M., Roy P. K., Pal T., “Opposition-based krill herd algorithm applied to economic load dispatch problem”, Ain Shams Engineering Journal (2016).
48
[48] Coelho L. D. S. and Mariani V. C. “Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve- point effect” IEEE Trans Power System; 21(2):989–96 (2006).
49
[49] Bhattacharjee K., Bhattacharya A. and Halder S., “Oppositional Real Coded Chemical Reaction Optimization for different economic dispatch problems” Electrical Power and Energy Systems 55, 378–391 (2014).
50
[50] Reddy S. and Vaisakh K., “Shuffled differential evolution for large scale economic dispatch” Electric Power Syst Res.; 96:237–245 (2013).
51
[51] Ghorbani N. and Babaei E., “Exchange market algorithm for economic load dispatch”, Electrical Power and Energy Systems 75, 19–27 (2016).
52
[52] Sinha N, Chakrabarti R. and Chattopadhyay P. K., “Evolutionary programming techniques for economic load dispatch” IEEE Trans Evol. Comput. 7(1):83–94 (2003).
53
[53] Bhattacharjee K., Bhattacharya A. and Chattopadhyay P. K., “Discussion on “A GA-API Solution for the Economic Dispatch of Generation in Power System Operation” IEEE transaction on power systems, vol. 28, no. 1, (2013).
54
[54] Roy S. and Bhattacharjee K., “The use of krill herd based optimization to solve complex economic load dispatch problems” Emerging Devices and Smart Systems (ICEDSS), 2017
55
[55] Rani S., Roy S., Bhattacharjee K., Bhattacharya A., “Teaching Learning Based Optimization to Solve Economic and Emission Scheduling Problems”, 2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) (2016).
56
[56] Bhattacharjee K., Bhattacharya A. and Halder S., “Chemical Reaction Optimization Applied In Economic Dispatch Problems” 1st international conference on automation, control, energy and systems (ACES 14), Academic of technology, Hooghly, India, 1-2, Pages: 1 – 6 (2014).
57
[57] Bhattacharjee K., “Economic Dispatch Problems using Backtracking Search Optimization” International Journal of Energy Optimization and Engineering, IGI-Global. Vol-7, Issue-2,Article-2, pp. 39-60 (2018).
58
[58] Park J., Jeong Y., Shin J. et al, “An Improved Particle Swarm Optimization for Non-convex Economic Dispatch Problems” IEEE Transactions on power systems, vol. 25, no. 1 (2010).
59
[59] Huang, Wei T., et al., "Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch" Energies 11.2 (2018): 417.
60
[60] Moradi-Dalvand M., Mohammadi-Ivatloo B. and Najafi A.,et al, “Continuous quick group search optimizer for solving non-convex economic dispatch problems” Electric Power Systems Research, Volume 93, Pages 93-105 (2012).
61
[61] Mohammadi-Ivatloo B., Rabiee A., Ehsan S. M., “Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems” Electrical Power and Energy Systems, Volume 42, Pages 508–516 (2012).
62
ORIGINAL_ARTICLE
On the well-posedness, equivalency, and low-complexity translation techniques of discrete-time hybrid automaton and piecewise affine systems
The main contribution of this paper is to present the systematic and low-complexity translation techniques betweena class of hybrid systems referred to as automaton-based DHA and piecewise affine (PWA) systems. As an startingpoint the general modeling framework of the automaton-based DHA is represented which models the controlled anduncontrolled switching phenomena between linear continuous dynamics including discrete and continuous states,inputs and outputs. The basic theoretical definitions on the state trajectories of the proposed DHA with forwardand backward evolutions which yield forward and backward piecewise affine (FPWA and BPWA) systems are given.Next, the well-posedness and equivalency properties are proposed and the sufficient conditions under which the wellposedness property is achieved with the automaton-based DHA and PWA systems are given. It is shown that thegraphical structure of the proposed automaton-based DHA makes it possible to obtain analytically the equivalent PWAsystem with a polynomial complexity in contrast to the existing numerical translation techniques via decomposedstructure of the DHA with an exponential complexity. Examples are presented to confirm the effectiveness of theproposed translation techniques.
https://scientiairanica.sharif.edu/article_21594_d28d93cd31b3751fedadc534633fc777.pdf
2022-04-01
693
726
10.24200/sci.2019.53308.3177
Automaton-based discrete-time hybrid automaton
Piecewise affine (PWA) systems
Well-posedness
complexity
Equivalency and translation techniques
M.
Hejri
hejri@sut.ac.ir
1
Department of Electrical Engineering, Sahand University of Technology, Sahand New Town, Tabriz, P.O. Box 51335-1996, Iran
LEAD_AUTHOR
H.
Mokhtari
mokhtari@sharif.edu
2
Department of Electrical Engineering, Sharif University of Technology, Tehran, P.O. Box 11365-9363, Iran
AUTHOR
References:
1
[1] Cassandras, C.G. and Lafortune, S. ”Introduction to discrete event systems”, 2nd edition, Springer Berlin Heidelberg (2008).
2
[2] Pcolka M., Zacekova, E., Celikovsky, S. and Sebek, M. ”Toward a smart car: hybrid nonlinear predictive controller with adaptive horizon”, IEEE Trans. Control Syst. Technol., 26(6), pp. 1970-1981 (2017).
3
[3] Tantawy A., Koutsoukos, X. and Biswas, G. ”Aircraft power generators: hybrid modeling and simulation for fault detection”, IEEE Trans. Aerosp. Electron. Syst., 48(1), pp. 552–571 (2012).
4
[4] Soler, M., Kamgarpour, M., Lloret, J. and Lygeros, J. ”A hybrid optimal control approach to fuel-efficient aircraft conflict avoidance”, IEEE Trans. Intell. Transp. Syst., 17(7), pp. 1826–1838 (2016).
5
[5] Manon, P., Valentin-Roubinet, C. and Gilles, G. ”Optimal control of hybrid dynamical systems: application in process engineering”, Control Eng. Pract., 10(2), pp. 133-149 (2002).
6
[6] Lee, J., Bohacek, S., Hespanha, J.P. and Obraczka, K. ”Modeling communication networks with hybrid systems”, IEEE/ACM Trans. Netw., 15(3), pp. 630-643 (2007).
7
[7] Ding, J., and Gillula, J.H., Huang, H., Vitus, M.P., Zhang, W. and Tomlin, C.J. ”Hybrid systems in robotics”, IEEE Robot. Autom. Mag., 18(3), pp. 33-43 (2011).
8
[8] Bortolussi, L. and Policriti, A. ”Hybrid systems and biology” In Formal Methods for Computational Systems Biology: 8th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, Advanced Lectures, Springer Berlin Heidel- berg, pp. 424-448 (2008).
9
[9] Theunisse, T.A.F., Chai, J., Sanfelice, R.G., and Heemels, W.P.M.H. ”Robust global stabilization of the DC-DC boost converter via hybrid control”, IEEE Trans. Circuits Syst. I, 62(4), pp. 1052-1061 (2015).
10
[10] Moarref M. and Rodrigues, L. ”Piecewise affine networked control systems” IEEE Trans. Control Netw. Syst., 3(2), pp. 173-181 (2016).
11
[11] Fourlas, G.K., Kyriakopoulos, K.J., and Vournas, C.D. ”Hybrid systems modeling for power systems”, IEEE Circuits Syst. Mag., 4(3), pp. 16-23 (2004).
12
[12] Kowalewski, S. ”Introduction to the analysis and verification of hybrid systems”, In Modelling, Analysis, and Design of Hybrid Systems, S. Engell, G. Frehse and E. Schnieder, Eds., pp. 153–171, Springer Berlin Heidelberg (2002).
13
[13] Belta, C., Yordanov, B. and Gol, E.A. ”Formal methods for discrete-time dynamical systems”, J. Kacprzyk, Ed., Springer International Publishing (2017).
14
[14] Borrelli, F., Baoti´c, M. and Bemporad, A. and Morari, M. ”Dynamic programming for constrained optimal control of discrete-time linear hybrid systems”, Automatica, 41(10), pp. 1709–1721 (2005).
15
[15] Karer, G. and ˇ Skrjanc, I. ”Introduction to predictive control of complex systems”, In Predictive approaches to control of complex systems, Springer Berlin Heidelberg, pp. 147–156 (2013).
16
[16] Johansson, K.H., Egerstedt, M., Lygeros, J. and Sastry, S. ”On the regularization of zeno hybrid automata”, Syst. Control Lett., 38, pp. 141–150 (1999).
17
[17] Floudas, C.A. and Lin, X. ”Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review”, Comput. Chem. Eng., 28(11), pp. 2109 - 2129 (2004).
18
[18] Imura, J.-ichi ”Optimal control of sampled-data piecewise affine systems”, Automatica, 40(4), pp. 661-669 (2004).
19
[19] Stauner, T. ”Discrete-Time Refinement of Hybrid Automata”, Hybrid Systems: Computation and Control, C.J. Tomlin and M.R. Greenstreet, Eds., pp. 407–420, Springer Berlin Heidelberg (2002).
20
[20] Zaytoon, J. ”Hybrid Dynamic Systems: overview and discussion on verification methods”, Informatics in Control, Automation and Robotics II, J. Filipe, J.-L. Ferrier, J. A. Cetto and M. Carvalho, Eds., pp. 17–26, Springer Netherlands (2007).
21
[21] Heemels,W.P.H.M., De Schutter, B. and Bemporad, A. ”Equivalence of hybrid dynamical models”, Automatica, 37(7), pp. 1085-1091 (2001).
22
[22] Bemporad, A. and Morari, M. ”Control of systems integrating logic, dynamic and constraints”, Automatica, 35(3), pp. 407-427 (1999).
23
[23] Hejri, M., Giua, A., and Mokhtari, H. ”On the complexity and dynamical properties of mixed logical dynamical systems via an automaton- based realization of discrete-time hybrid automaton”, Int. J. of Robust Nonlin., 28(16), pp. 4713–4746 (2018).
24
[24] Heemels, W.P.M.H., Schumacher, J.M. and Weiland, S. ”Linear complementarity systems”, SIAM J. Appl. Math., 60(4), pp. 1234-1269 (2000).
25
[25] De Schutter, B. and DeMoor, B. ”The extended linear complementarity problem and the modeling and analysis of hybrid systems”, Lecture Notes in Computer Science, P. Antsaklis, W. Kohn, M. Lemmon, A. Nerode and S. Sastry, Eds., pp. 70–85 (1999).
26
[26] De Schutter, B. and van den Boom, T. ”Model predictive control for max-plus-linear systems”, American Control Conference, pp. 4046-4050 (2000).
27
[27] Sontag, E.D. ”Nonlinear regulation: the piecewise linear approach”, IEEE Trans. Autom. Control, 26(2), pp. 346-358 (1981).
28
[28] Ferrari-Trecate, G., Cuzzola, F.A., Mignone, D. and Morari M. ”Analysis of discrete-time piecewise affine and hybrid systems”, Automatica, 38(12), pp. 2139–2146 (2002).
29
[29] Johansson, M. and Rantzer, A. ”Computation of piecewise quadratic Lyapunov functions for hybrid systems”, IEEE Trans. Autom. Control, 43(4), pp. 555-559 (1998).
30
[30] Cuzzola, F.A. and Morari, M. ”A generalized approach for analysis and control of discrete-time piecewise affine and hybrid systems”, M.D. Di Benedetto and A. Sangiovanni-Vincentelli, Eds., Hybrid Systems: Computation and Control: 4th International Workshop, HSCC 2001, Rome, Italy, Springer Berlin Heidelberg, pp. 189–203 (2001).
31
[31] Hajiahmadi, M., De Schutter, B. and Hellendoorn, H., ”Design of Stabilizing Switching Laws for Mixed Switched Affine Systems”, IEEE Trans. Autom. Control, 61(6), pp. 1676-1681 (2016).
32
[32] van der Schaft, A.J. and Schumacher, J.M. ”Complementarity modeling of hybrid systems”, IEEE Trans. Autom. Control, 43(4), pp. 483-490 (1998).
33
[33] Lygeros, J., Johansson, K.H., Simic, S.N., Zhang, J. and Sastry, S.S. ”Dynamical properties of hybrid automata”, IEEE Trans. Autom. Control, 48(1), pp. 2-17 (2003).
34
[34] Camacho, E.F., Ramirez, D.R., Limon D., Monuz de la Pena, D. and Alamo T. ”Model predictive control techniques for hybrid systems”, Annu. Rev. Control, 34, pp. 21–31 (2010).
35
[35] Torrisi, F.D. and Bemporad, A. ”HYSDEL-a tool for generating computational hybrid models for analysis and synthesis problems”, IEEE Trans. Control Syst. Technol., 12(2), pp. 235-249 (2004).
36
[36] Borrelli, F., Bemprad, A. and Morari, M. ”Predictive control for linear and hybrid systems”, Cambridge University Press (2017).
37
[37] Bemporad, A., Ferrari-Trecate, G. and Morari, M. ”Observability and controllability of piecewise affine and hybrid systems”, IEEE Trans. Autom. Control, 45(10), pp. 1864-1876 (2000).
38
[38] Geyer, T., Torrisi, F.D. and Morari, M. ”Efficient Mode Enumeration of Compositional Hybrid Systems”, Hybrid Systems: Computation and Control, O. Maler and A. Pnueli, Eds., Springer Berlin Heidelberg, pp. 216–232 (2003).
39
[39] Potocnik, B., Music, G. and Zupancic B., ”A new technique for translating discrete hybrid automata into piecewise affine systems”, Math. Comp. Model. Dyn., 10(1), pp. 41–57 (2004).
40
[40] Bemporad, A. ”Efficient conversion of mixed logical dynamical systems into an equivalent piecewise affine form”, IEEE Trans. Autom. Control, 49(5), pp. 832–838 (2004).
41
[41] Geyer, T., Torrisi, F.D. and Morari, M. ”Efficient mode enumeration of compositional hybrid systems”, Int. J. Control, 83(2), pp. 313–329 (2010).
42
[42] Groot, N., De Schutter, B. and Hellendoorn, H. ”Integrated model predictive traffic and emission control using a piecewise-affine approach”, IEEE Trans. Intell. Transp. Syst., 14(2), pp. 587–598 (2013).
43
[43] Ferrari-Trecate, G., Cuzzola, F.A. and Morari M. ”Lagrange stability and performance analysis of discrete-time piecewise affine systems with logic states”, Int. J. Control, 76(16), pp. 1585–1598 (2003).
44
[44] Ferrari-Trecate, G., Cuzzola, F.A. and Morari, M. ”An LMI approach for H-infinity analysis and control of discrete-time piecewise affine systems”, Int. J. Control, 75(16-17), pp. 1293–1301 (2002).
45
[45] Mignone, D., Ferrari-Trecate, G. and Morari, M. ”Stability and stabilization of piecewise affine and hybrid systems: an LMI approach”, Proceedings of the 39th IEEE Conference on Decision and Control, 1, pp. 504-509 (2000).
46
[46] Johansson, M. ”Piecewise linear control systems: a computational approach”, Springer (2003).
47
[47] Xu, J. and Xie, L. ”Control and estimation of piecewise affine systems”, Woodhead publishing (2013).
48
[48] Christophersen, F. ”Optimal control of constrained piecewise affine systems”, M. Thoma and M. Morari, Eds., Springer Berlin Heidelberg (2007).
49
[49] Sontag, E.D. ”Interconnected automata and linear systems: a theoretical framework in discrete-time”, Proceedings of the DIMACS/SYCON workshop on Hybrid systems III: verification and control, Springer-Verlag New York, pp. 436–448 (1996).
50
[50] Cairano, S. and Bemporad, A. ”Equivalent piecewise affine models of linear hybrid automata”, IEEE Trans. Autom. Control, 55(2), pp. 498–502 (2010).
51
[51] Henzinger, T.A. ”The theory of hybrid automata”, Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science (LICS ’96), New Brunswick, pp. 278–292 (1996).
52
[52] Alur, R., Courcoubetis, C., Halbwachs, N., Henzinger, T.A., Ho, P.-H.,Nicollin, X., Olivero, A., Sifakis, J. and Yovine, S. ”The algorithmic analysis of hybrid systems”, Theor. Comput. Sci., 138(1), pp. 3–34 (1995).
53
[53] Alur, R., Courcoubetis, C., Henzinger, T.A. and Ho, P.-H. ”Hybrid automata: An algorithmic approach to the specification and verification of hybrid systems”, Hybrid Systems, pp. 209–229, Springer Berlin Heidelberg (1993).
54
[54] Nicollin, X., Olivero, A., Sifakis, J., and Yovine, S. ”An approach to the description and analysis of hybrid systems”, In Hybrid Systems, R.L. Grossman, A. Nerode, A. P. Ravn and H. Rischel, Eds., pp. 149–178, Springer Berlin Heidelberg (1993).
55
[55] Lygeros, J., Godbole, D.N. and Sastry, S.S. ”Verified hybrid controllers for automated vehicles”, IEEE Trans. Autom. Control, 43(4), pp. 522-539 (1998).
56
[56] Stursberg, O., Panek, S., Till, J. and Engell, S., ”Generation of optimal control policies for systems with switched hybrid dynamics”, Mod- elling, Analysis, and Design of Hybrid Systems, S. Engell, G. Frehse and E. Schnieder, Eds., pp. 337–352, Springer Berlin Heidelberg (2002).
57
[57] Stursberg, O. and Engell, S. ”Optimal control of switched continuous systems using mixed-integer programmings”, IFAC Proceedings Vol- umes, 35(1), pp. 433-438 (2002).
58
[58] Stursberg, O. and Panek, S. ”Control of switched hybrid systems based on disjunctive formulations”, Hybrid Systems: Computation and Control, C.J. Tomlin and M. R. Greenstreet, Eds., Springer Berlin Heidelberg, pp. 421–435 (2002).
59
[59] Pang, Y. and Spathopoulos, M.P. ”Time-optimal control for discrete-time hybrid automata”, Int. J. Control, 78(11), pp. 847-863 (2005).
60
[60] Zoncu M., Balluchi, A., Sangiovanni-Vicentelli, A.L., and Bicchi, A. ”On the stabilization of linear discrete-time hybrid automata”, 42nd IEEE International Conference on Decision and Control, pp. 1147-1152 (2003).
61
[61] Seatzu, C., Gromov, D., Raisch, J., Corona, D. and Giua, A. ”Optimal control of discrete-time hybrid automata under safety and liveness constraints”, Nonlinear Anal.-Theor., 65(6), pp. 1188-1210 (2006).
62
[62] Hejri, M. ”Hybrid modeling and control of power electrinic converters”, Ph.D Thesis, Sharif University of Technology Cotutorship with University of Cagliari, Iran and Italy (2010).
63
[63] Hejri, M. and Giua, A. ”Hybrid modeling and control of switching DC-DC converters via MLD systems”, IEEE 7th International Conference on Automation Science and Engineering, Trieste, Italy (2011).
64
[64] Hejri, M. and Mokhtari, H. ”Hybrid modeling and control of a DC-DC boost converter via Extended Mixed Logical Dynamical systems (EMLDs)”, Power Electronics, Drive Systems and Technologies Conference (PEDSTC), Tehran, Iran, pp. 373–378 (2014).
65
[65] Bemporad, A. ”An efficient technique for translating mixed logical dynamical systems into piecewise affine systems”, 41th IEEE Conf. on Decision and Control, pp. 1970-1975 (2002).
66
[66] Xia, X. ”Well posedness of piecewise-linear systems with multiple modes and multiple criteria”, IEEE Trans. Autom. Control, 47(10), pp. 1716–1720 (2002).
67
[67] Sahan, G. and Eldem, V. ”Well posedness conditions for Bimodal Piecewise Affine Systems”, Syst. Control Lett., 83, pp. 9–18 (2015).
68
[68] Ferrari-Trecate, G., Cuzzola, F.A. and Morari, M. ”Analysis of Discrete-Time PWA Systems with Logic States”, Hybrid Systems: Computa- tion and Control, C.J. Tomlin, and M.R. Greenstreet, Eds., pp. 194–208, Springer Berlin Heidelberg (2002).
69
[69] Bemporad, A. and Fukuda, K. and Torrisi, F.D. ”Convexity recognition of the union of polyhedra”, Computational Geometry, 18(3), pp. 141-154 (2001).
70
[70] Branicky, M.S., Borkar, V.S. and Mitter, S.K., ”A unified framework for hybrid control: model and optimal control theory”, IEEE Trans. Autom. Control, 43(1), pp. 31–45 (1998).
71
[71] Imura, J.-ichi and van der Schaft, A. ”Characterization of well-posedness of piecewise-linear systems”, IEEE Trans. Autom. Control, 45(9), 34 pp. 1600–1619 (2000).
72
[72] Lazar M., Heemels, W.P.M.H., Weiland, S. and Bemporad, A. ”Stabilizing model predictive control of hybrid systems”, IEEE Trans. Autom. Control, 51(11), pp. 1813–1818 (2006).
73
[73] Lin, H. and Antsaklis, P.J. ”Stability and stabilizability of switched linear systems: a survay of recent results”, IEEE Trans. Autom. Control, 54(2), pp. 308–322 (2009).
74
[74] Sindareh, Esfahani P. and Kurt, Pieper J. ”H∞ model predictive control for constrained discrete-time piecewise affine systems”, Int. J. of Robust Nonlin., 28(6), pp. 1973–1995 (2017).
75
ORIGINAL_ARTICLE
Electricity market assessment in wind energy integrated power systems with the potential of flexibility: A boundary condition approach
This paper focuses on a dynamic equilibrium considering the flexible ramp market and demand response resources. With ever-swelling installation of variable renewable energies, demand response programs can play an important role in mitigating the system ramping deficiency. Hence in this paper, the ramping capability of demand response resources in procuring system ramp requirement is considered. The strategic behavior of different players is modeled through a multi-leader-common-follower game, in which suppliers and demand response aggregators are laid as the leaders and market operator is considered as the single follower of the game. In addition, a dynamic forward rolling process to find equilibria at the real-time market is proposed. The effect of considering demand response resources and flexible ramp penalty price on the strategic behavior of players in equilibrium is evaluated. Finally, the effectiveness of the proposed approach is verified on a three-firm system. While revealing demand response resources roles in mitigating ramping deficiency, the results show that how penalty price on flexible ramp violation can lead uplift payments to be formed.
https://scientiairanica.sharif.edu/article_21697_34bd88df691e11c388c8d45c0bb98142.pdf
2022-04-01
727
738
10.24200/sci.2019.54732.3887
Power System Flexibility
Equilibrium
Wind energy
Demand Response Program
H.
Gharibpour
h_gharibpoor@yahoo.com
1
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
F.
Aminifar
faminifar@ut.ac.ir
2
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
References:
1
[1] Navid, N. and Rosenwald, G. “Ramp capability product design for MISO markets”, MISO, Market Development and Analysis (2013).
2
[2] Abdul-Rahman, K. H., Alarian, H., Rothleder, M., et al. “Enhanced system reliability using flexible ramp constraint in CAISO market”, Proc. IEEE Power Energy Soc. Gen. Meeting, San Diego, USA, pp.1-6 (2012).
3
[3] Srinivasan, D., Trung, L.T., and Singh, C. “Bidding and Cooperation Strategies for Electricity Buyers in Power Markets”, IEEE Syst. J., 10(2), pp. 422-433 (2016).
4
[4] Klemperer, P.D. and Meyer, M.A. “Supply Function Equilibria in Oligopoly under Uncertainty”, Econometrica: J. Econometric Soc., pp. 1243-1277 (1989).
5
[5] Anderson, E.J. and Hu, X. “Finding Supply Function Equilibria with Asymmetric Firms”, Operations research, 56(3), pp. 697-711 (2008).
6
[6] Azadi, A. and Akbari Foroud, A. “A New Solution Approach for Supply Function Equilibrium-based Bidding Strategy in Electricity Markets”, Scientia Iranica, vol. 24(6), pp. 3231-3246 (2017).
7
[7] Rahimiyan, M. and Baringo, L. “Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach”, IEEE Trans. Power Syst., 31(4), pp. 2676-2687 (2016).
8
[8] Xu, Z., Hu, Z., Song, Y., and et al. “Risk-Averse Optimal Bidding Strategy for Demand-Side Resource Aggregators in Day-Ahead Electricity Markets under Uncertainty”, IEEE Trans. Smart Grid, 8(1), 96-105 (2017).
9
[9] Ladjici, A.A., Tiguercha, A., and Boudour, M. “Nash Equilibrium in a Two-settlement Electricity Market Using Competitive Co-evolutionary Algorithms”, Int. J. Electr. Power Energy Syst., 57, p. 148-55 (2014).
10
[10] Nezamabadi, N. and Vahidinasab, V. “Microgrids Bidding Strategy in a Transactive Energy Market”, Scientia Iranica, (in press).
11
[11] Bashi, MH., Yousefi GR., Latify, MA., and et al. “Impacts of Intraday Risky Power Trades on the High Wind Penetrated Electricity Markets”, IET Gener. Transm. Distrib, 13(17), pp. 3836-3846 (2019).
12
[12] Ruiz, C., Conejo, A.J., and Smeers, Y. “Equilibria in an Oligopolistic Electricity Pool with Stepwise Offer Curves”, IEEE Trans. Power Syst., 27(2), pp. 752-761 (2012).
13
[13] Cornelius, A. “Assessing the impact of flexible ramp capability products in the Midcontinent ISO”, Master's thesis, Duke University (2014).
14
[14] Bashi, MH., Gharibpour, H., Rahmati, I., and et al. “Including the Constraints That Have Less Than One-Hour Characteristcis in an Hourly Based Generation Scheduling Regime”, IEEE International Conference on Environment and Electrical Engineering, pp. 1-4 (2018).
15
[15] Doostizadeh, M., Aminifar, F., Ghasemi, and et al. “Energy and Reserve Scheduling under Wind Power Uncertainty: An Adjustable Interval Approach”, IEEE Trans. on Smart Grid, 7(6), pp. 2943-2952 (2016).
16
[16] Zou, P., Chen, Q., Wang, J., and et al. “Evaluating the Impacts of Flexible Ramping Products on the Market Equilibrium”, IEEE Power Energy Soc. Gen. Meeting, San Diego, Boston, USA (2016).
17
[17] AEMO. “Integrating Renewable Energy–Wind Integration Studies Report”, Melbourne, Australia, pp. 3-21 (2013).
18
[18] Chen, R., Wang, J., Botterud, A., and et al. “Wind Power Providing Flexible Ramp Product”, IEEE Trans. Power Syst., 32(3), pp. 2049-2061 (2017).
19
[19] Cui, M., Zhang, J., Wu, H., and et al. “Wind-Friendly Flexible Ramping Product Design in Multi-Timescale Power System Operations”, IEEE Trans. Sustain. Energy, 8(3), pp. 1064-1075 (2017).
20
[20] Heydarian-Forushani, E., Golshan, M.E.H., Shafie-khah, M., and et al. “Optimal Operation of Emerging Flexible Resources Considering Sub-Hourly Flexible Ramp Product”, IEEE Trans. Sustain. Energy, 9(2), pp. 916-929 (2018).
21
[21] Fang, X., Krishnan, V., and Hodge, B.-M. “Strategic Offering for Wind Power Producers Considering Energy and Flexible Ramping Products”, Energies, 11(5), p. 1239 (2018).
22
[22] Gharibpour, H. and Aminifar, F. “Multi-stage Equilibrium in Electricity Pool with Flexible Ramp Market”, Int. J. Electr. Power Energy Syst., 109, p. 661-671 (2019).
23
[23] Chen, Q., Zou, P., Wu, C., and et al. “A Nash-Cournot Approach to Assessing Flexible Ramping Products”, Appl. Energy, 206, p. 42-50 (2017).
24
[24] https://www.eia.gov/electricity/data/eia861/dsm/
25
[25] York, D. and Kushler, M. “Exploring the Relationship between Demand Response and Energy Efficiency: A Review of Experience and Discussion of Key Issues”, American Council for an Energy-Efficient Economy (2005)
26
[26] Roozbehani, M., Dahleh, M., and Mitter, S. “Dynamic Pricing and Stabilization of Supply and Demand in Modern Electric Power Grids”, IEEE Intl. Conf. Smart Grid Commun., Gaithersburg, USA (2010).
27
[27] Tulabing, R., Yin, R., DeForest, N., and et al. “Modeling Study on Flexible Load's Demand Response Potentials for Providing Ancillary Services at the Substation Level”, Electric Power Systs. Research, 140, pp. 240-252 (2016).
28
[28] FERC. “Assessment of Demand Response and Advanced Metering”, Federal Energy Regulatory Commission (2006).
29
[29] Aalami, H., Moghaddam, M.P., and Yousefi, G. “Demand Response Modeling Considering Interruptible/Curtailable Loads and Capacity Market Programs”, Appl. Energy, 87(1), pp. 243-250 (2010).
30
[30] Hu, J., Wen, F., Wang, K., and et al. “Simultaneous Provision of Flexible Ramping Product and Demand Relief by Interruptible Loads Considering Economic Incentives”, Energies, 11(1), pp. 46 (2017).
31
[31] Helistö, N., Kiviluoma, J., and Holttinen, H. “Long-Term Impact of Variable Generation and Demand Side Flexibility on Thermal Power Generation”, IET Renew. Power Gener., 12(6), pp. 718-726 (2018).
32
[32] Hu, J., Sarker, M.R., Wang, J., and et al. “Provision of Flexible Ramping Product by Battery Energy Storage in Day-Ahead Energy and Reserve Markets”, IET Gener. Transm. Distrib., 12(10), pp. 2256-2264 (2018).
33
[33] Nikoobakht, A., Aghaei, J., Shafie-khah, M., and et al. “Assessing Increased Flexibility of Energy Storage and Demand Response to Accommodate a High Penetration of Renewable Energy Sources”, IEEE Trans. Sustain. Energy, in press (2018).
34
[34] Mohammadi, J., Rahimi-Kian, A., and Ghazizadeh, M.-S. “Aggregated Wind Power and Flexible Load Offering Strategy”, IET Renew. Power Gener., 5(6), pp. 439-447 (2011).
35
[35] Klobasa, M. “Analysis of Demand Response and Wind Integration in Germany's Electricity Market”, IET Renew. Power Gener., 4(1), pp. 55-63 (2010).
36
[36] https://www.caiso.com/Documents/ Section34_Real-imeMarket_asof_Feb15_2018.pdf
37
ORIGINAL_ARTICLE
Tunable active grounded lossless and lossy inductance simulators with single grounded capacitor using VDBAs
In this paper, three active-C synthetic grounded inductance simulator circuits are presented, which realize tunable lossless and lossy series and parallel RL-type inductances. Each of which employs two voltage differencing buffered amplifiers (VDBAs) as active components, and a single grounded capacitor as a passive component. In all the proposed circuits, the simulated equivalent resistance and inductance values can be adjusted electronically through the transconductance gains of the VDBAs. They also do not require any critical component matching conditions and cancellation constraints. Detail non-ideal analysis including transfer errors of the VDBA has been analyzed. For circuit performance verification and comparison, some application examples are given together with computer simulation results by PSPICE program.
https://scientiairanica.sharif.edu/article_21694_ac32781a4956d0d8a53118205b6440d2.pdf
2022-04-01
739
748
10.24200/sci.2019.51551.2248
Voltage Differencing Buffered Amplifier (VDBA)
lossless inductor
lossy inductor
grounded inductance simulator
electronically tunable circuit
W.
Tangsrirat
worapong.ta@kmitl.ac.th
1
School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL), Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
LEAD_AUTHOR
W.
Surakampontorn
wanlop.ltpw@gmail.com
2
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang (KMITL), Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand - Academy of Science, Royal Society of Thailand, Dusit Palace Ground, Dusit, Bangkok 10300, Thailand
AUTHOR
References:
1
[1] Moezzi, M. and Bakhtiar, M. S. “A tunable high-Q active inductor with a feed forward noise reduction path”, Scientia Iranica, Trans. D: Comp. Science & Eng. Electr. Eng., 21(3), pp. 945-952 (2014).
2
[2] Yuce, E., Minaei S. and Cicekoglu, O. “A novel grounded inductor realization using a minimum number of active and passive components”, ETRI Journal, 27(4), pp.427-432 (2005).
3
[3] Yuce, E. “Inductor implementation using a canonical number of active and passive elements”, Int. J. Electron., 94(4), pp.317-326 (2007).
4
[4] Yuce, E. and Minaei, S. “On the realization of simulated inductors with reduced parasitic impedance effects”, Circuits Syst. Signal Process., 28(3), pp. 451-465 (2009).
5
[5] Kacar, F. “New lossless inductance simulators realization using a minimum active and passive components”, Microelectron. J., 41(2-3), pp.109-113 (2010).
6
[6] Maundy, B. and Gift, S. J. G. “Active grounded inductor circuit”, Int. J. Electron., 98(5), pp. 555-567 (2011).
7
[7] Myderrizi, I., Minaei, S. and Yuce, E. “DXCCII-based grounded inductance simulators and filter applications”, Microelectron. J., 42(9), pp.1074-1081 (2011).
8
[8] Yesil, A., Kacar F. and Gurkan, K. “Lossless grounded inductance simulators employing single VDBA and its experimental band-pass filter application”, Int. J. Electron. Commun. (AEU), 68(2), pp.143-150 (2014).
9
[9] Metin, B. “Canonical inductor simulators with grounded capacitors using DCCII”, Int. J. Electron., 99(7), pp.1027-1035 (2012).
10
[10] Cam, U., Kacar, F., Cicekoglu, O., Kuntman, H. and Kuntman, A. “Novel two OTRA-based grounded immittance simulator topologies”, Analog Integr. Circ. Sig. Process., 39(2), pp. 169-175 (2004).
11
[11] Yuce, E. “Novel lossless and lossy grounded inductor simulators consisting of a canonical number of components”, Analog Integr. Circ. Sig. Process., 59(1), pp.77-82 (2009).
12
[12] Metin, B. “Supplementary inductance simulator topologies employing single DXCCII”, Radioengineering, 20(3), pp.614-618 (2011).
13
[13] Alpaslan, H. and Yuce, E. “Inverting CFOA based lossless and lossy grounded inductor simulators”, Circuits Syst. Signal Process., 34(10), pp.3081-3100 (2015).
14
[14] Kuntman, H., Gulsoy, M. and Cicekoglu, O. “Actively simulated grounded lossy inductors using third generation current conveyors”, Microelectron. J., 31(4), pp.245-250 (2000).
15
[15] Pathak, J. K., Singh A. K. and Senani, R. “New canonical lossy inductor using a single CDBA and its application”, Int. J. Electron., 103(1), pp.1-13 (2016). [16] Senani, R., Singh, A. K., Gupta, A. and Bhaskar, D. R. “Simple simulated inductor, low-pass/band-pass filter and sinusoidal oscillator using OTRA”, Circuits and Systems, 7(3), pp. 83-99 (2016).
16
[17] Jaikla, W. and Lahiri, A. “Current feedback op-amp based linear voltage-controlled oscillator using analog multipliers and minimum passive components”, Scientia Iranica, Trans. D: Comp. Science & Eng. Electr. Eng., 23(3), pp. 1294-1300 (2016).
17
[18] Bhushan, M. and Newcomb, R. W. “Grounding of capacitors in integrated circuits”, Electron. Lett., 3, pp.148-149 (1967).
18
[19] Sun, Y. “Design of high frequency integrated analogue filters”, IET Circuits, Devices and Systems Series 14, (2002).
19
[20] Kacar, F., Yesil, A. and Noori, A. “New CMOS realization of voltage differencing buffered amplifier and its biquad filter applications”, Radioengineering, 21(1), pp.333-339 (2012).
20
[21] Sotner, R., Jerabek, J. and Herencsar, N. “Voltage differencing buffered/inverted amplifiers and their applications for signal generation”, Radioengineering, 22(2), pp.490-504 (2013).
21
[22] Tangsrirat, W., Surakampontron, W. and Fujii, N. “Realization of leapfrog filters using current differential buffered amplifiers”, IEICE Trans. Fundamental., E86-A(2), pp.318-326 (2003).
22
[23] Tangsrirat, W. “Actively floating lossy inductance simulators using voltage differencing buffered amplifiers”, IETE Journal of Research, doi : 10.1080/03772063.2018.1433082 (2018).
23
ORIGINAL_ARTICLE
Optimal scheduling of hydrothermal system considering variable nature of water transportation delay
This paper presents solution of short-term hydrothermal scheduling problem using an algorithm called Grasshopper optimization. The objective of hydrothermal scheduling is to reduce the total cost of generation by optimizing the output of power generation of different thermal and hydro plants for a certain time interval. A non-linear relationship between hydropower generation, net head and rate of water discharge is considered here. The complicated head-sensitive water-to-power conversion and piecewise output limit is also considered. To investigate the performance of this new technique, two test systems have been considered. The simulation results are compared with some well-known optimization methods such as Genetic algorithm, Biogeography based optimization, hybrid Differential evolution with Biogeography based optimization and Grey wolf optimizer algorithm. The simulation results show the superiority of this technique as compared to other well-known optimization methods.
https://scientiairanica.sharif.edu/article_22093_680bea3d0face43e32f941e92dfdb9a6.pdf
2022-04-01
749
770
10.24200/sci.2020.52887.2936
Grasshopper optimization
Hydrothermal Scheduling
Water-to-power conversion
Valve point loading
D.
Das
diptanuonline@yahoo.co.in
1
Department of Electrical Engineering, National Institute of Technology, Agartala, Tripura, Pin-799046, India
LEAD_AUTHOR
A.
Bhattacharya
bhatta.aniruddha@gmail.com
2
Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, Pin-713209, India
AUTHOR
R. N.
Ray
rupnarayan_r@yahoo.co.in
3
Department of Electrical Engineering, National Institute of Technology, Agartala, Tripura, Pin-799046, India
AUTHOR
References:
1
[1] Engles, L., Larson, R.E., Peschon, J. et al. “Dynamic programming applied to hydro and thermal generation scheduling”, In: IEEE tutorial course text, 76CH1107-2-PWR, IEEE, NewYork (1976).
2
[2] Wood, J. and Wollenberg, B.F. “Power generation, operation, and control”, John Wiley and Sons, 2nd edition. Wiley New York (1984).
3
[3] Saha, T.N. and Khaparde, S.A.“An application of a direct method to the optimal scheduling of hydrothermal systems”, IEEE Trans PAS, 97 (3), pp. 977–983 (1978).
4
[4] Rashid AHA. and Nor, K.M.“An efficient method for optimal scheduling of fixed head hydro and thermal plants”, IEEE Trans Power Syst,6 (2), pp. 632–636 (1991).
5
[5] Nilsson,O.and Sjelvgren, D. “Mixed-integer programming applied to short-term planning of a hydrothermal system”, IEEE Trans Power Syst 11 (1), pp. 281–286 (1996).
6
[6] Wong, K.P. and Wong, Y.W. “Short-term hydrothermal scheduling Part I: simulated annealing approach”, Proc Inst Electr Eng Gen Transm Distrib.,141 (5), pp. 497–501(1994).
7
[7] Wong, K.P. and Wong, Y.W.“Short-term hydrothermal scheduling Part II: parallel simulated annealing approach”, Proc Inst Electr Eng Gen Transm Distrib, 141 (5), pp. 502–506 (1994).
8
[8] Chen PO-H. and Chang, H.C.“Genetic aided scheduling of hydraulically coupled plants in hydrothermal coordination”, IEEE Trans Power Syst, 11 (2), pp. 975–981 (1996).
9
[9] Orero, S.O. and Irving, M.R.“A genetic algorithm modeling framework and solution technique for short term optimal hydrothermal scheduling”, IEEE Trans PWRS, 13 (2), pp. 501–518 (1998).
10
[10] Gil, E., Bustos, J. and Rudnick, H.“Short-term hydro thermal generation scheduling model using a genetic algorithm”, IEEE Trans PWRS, 18 (4), pp. 1256–1264 (2003).
11
[11] Kumar, S. and Naresh R. “Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem”, Int J Elec Power and Energ Sys, 29 (10), pp. 738-747 (2007).
12
[12] Hota, P.K. Chakrabarti, R. and Chattopadhyay, P.K. “Short-term hydrothermal scheduling through evolutionary programming technique”, Electr Power Syst Res, 52, pp. 189–196 (1999).
13
[13] Sinha, N., Chakrabarti, R. and Chattopadhyay, P.K. “Fast evolutionary programming techniques for short-term hydrothermal scheduling” Electric Power Syst Res, 66, pp. 97–103 (2003).
14
[14] Mandal K.K., Basu M. and Chakraborty N. “Particle swarm optimization technique based short-term hydrothermal scheduling”, Appl Soft comput, 8, pp. 1392-1399 (2008).
15
[15] Wang, Y., Zhou, J., Zhou, C. et al. “ An improved self-adaptive PSO technique for short-term hydrothermal scheduling”, Expert Sys with Appl, 39, pp. 2288-2295 (2012).
16
[16] Cavazzini, G., Pavesi, G. and Ardizzon, G. “A novel two-swarm based PSO search strategy for optimal short-term hydro-thermal generation scheduling”, Energ Conv and Mang, 164, pp. 460-481 (2018).
17
[17] Mandal, K.K. and Chakraborty, N. “Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems”, Electr Power Syst Res, 78 (11) pp. 1972–1979 (2008).
18
[18] Sivansubramani, S. and swarup K.S. “Hybrid DE-SQP algorithm for non-convex short term hydrothermal scheduling problem”, Energ Conv and Mang, 52, pp. 757-761 (2011).
19
[19] Basu M. “Improved differential evolution for short-term hydrothermal scheduling”, Elec Power & Energ, 58, pp. 91-100 (2014).
20
[20] Basu M. “Hopfield neural networks for optimal scheduling of fixed head hydrothermal power systems”, Electr Power Syst Res, 64 (1), pp 11–15 (2003).
21
[21] Roy, P.K. “Teaching learning based optimization for short term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint”, Int J Elec Power and Energ Sys, 53, pp. 10–19 (2013).
22
[22] Roy, P.K., Paul, C. and Sultana, S. “Oppositional teaching learning based optimization approach for combined heat and power dispatch”, Int J Elec Power and Energ Sys, 57, pp. 392–403 (2014).
23
[23] Nguyen, T.T., Vo, D.N. and Ruong, A.V. “Cuckoo search algorithm for short-term hydrothermal scheduling”, Appl Energy, 132, pp. 276–287 (2014).
24
[24] Zhou, J., Liao, X., Ouyang, S. et al. “Multi-objective artificial bee colony algorithm for short-term scheduling of hydrothermal system”, Electr Power Energy Syst, 55, pp 542-553 (2014).
25
[25] Das, S. and Bhattacharya, A. “Symbiotic organisms search algorithm for short-term hydrothermal scheduling”, Ain Shams Engg J, 9 (4), pp. 499-516 (2016).
26
[26] Roy S., Rani S., Bhattacharjee K. et al. “Chemical reaction based optimization implemented to solve short-term hydrothermal generation scheduling problems”, 3rd International Conference on Electrical Energy Systems (ICEES 2016), SSN college of Engineering, Tamilnadu, India, pp. 79-84 (2016)
27
[27] Sutradhar S., Dev Choudhury N.B. and Sinha N. “Grey wolf optimizer for short-term hydrothermal scheduling problems”, Michael Faraday IET International Sumit 2015, Kolkata, India (2015).
28
[28] Bhattacharjee K., Bhattacharya A. and nee Dey S.H. “Real coded chemical reaction based optimization for short-term hydrothermal scheduling”, Appl. Soft Comput., 24, pp. 962-976 (2014).
29
[29] Roy P.K., Pradhan M. and Paul T. “Krill herd algorithm applied to short-term hydrothermal scheduling problem”, Ain Shams Engg. Jr.9, pp. 31-43 (2018).
30
[30] Swain R.K., Barisal A. K., Hota P.K. et al. “ Short-term hydrothermal scheduling using clonal selection algorithm”, Int. J. Elec. Power & Energ. Sys., 33 (3) pp. 647-656 (2011).
31
[31] Balachander T., Jeyanthy P.A. and Devaraj D. “ Short term hydrothermal scheduling using flower pollination algorithm”, 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Srivilliputhur, India (2017).
32
[32] Das S., Bhattacharya A. and Das S. “Solution of short-term hydrothermal scheduling using sine cosine algorithm”, Soft Comput., 22 (19), pp. 6409-6427 (2018).
33
[33] Dubey H.M., Pandit M. and Panigrahi B.K. “Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling”, Int. J. Elec. Power & Energ. Sys, 83, pp. 158-174 (2016).
34
[34] Das S., Bhattacharya A., Chakraborty A.K. et al. “Fixed head short-term hydrothermal scheduling using whale optimization algorithm considering the uncertainty of solar power”, 2017 Ninth International Conference on Advanced Computing (ICoAC), Chennai, India (2017).
35
[35] Nguyen, T.T. and Vo, D.N. “Modified cuckoo search algorithm for short-term hydrothermal scheduling”, Electr Power Energy Syst, 65 pp. 271–281 (2015).
36
[36] Das, S., Bhattacharya, A. and Chakraborty A.K. “Solution of short-term hydrothermal scheduling problem using quasi-reflected symbiotic organisms search algorithm considering multi-fuel cost characteristics of thermal generator”, Arab J Sci and Engg, 43, pp. 2931-2960 (2018).
37
[37] Das, S., Bhattacharya, A. and Chakraborty, A.K. “Quasi-reflected ions motion optimization algorithm for short term hydrothermal scheduling”, Neural Comput & Applic, 29, pp. 123-149 (2018).
38
[38] Narang, N. “Short-term hydrothermal generation scheduling using improved predator influenced civilized swarm optimization technique”, Appl Soft Comput, 58, pp. 207-224 (2017).
39
[39] Fang, N., Zhou, J., Zhang, R. et al. “ A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling”, Int J Elec Power and Energ Sys, 62, pp. 617-629 (2014).
40
[40] Bhattacharjee, K., Bhattacharya, A. and nee Dey S.H. “Oppositional real coded chemical reaction based optimization to solve short-term hydrothermal scheduling problems”, Int J Elec Power and Energ Sys, 63, pp. 145-157 (2014).
41
[41] Zhang, J., Lin, S. and Qin, W. “A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling”, Int J Elec Power and Energ Sys, 65, pp. 159-168 (2015).
42
[42] Rasoul, Z.A. and Mohammadi-Ivatloo B. “Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization”, Int J Elec Power and Energ Sys, 67, pp. 350-367 (2015).
43
[43] Roy, P.K. “Hybrid chemical reaction optimization approach for combined economic emission short-term hydrothermal scheduling”, Elec Power Comp and Sys, 42 (15), pp. 1647-1660 (2014).
44
[44] Nadakuditi, G. Sharma, V. and Naresh, R. “Application of non-dominated sorting gravitational search algorithm with disruption operator for stochastic short-term hydrothermal scheduling”, IET Gener. Transm. Distrib, 10 (4), pp. 862-872 (2016).
45
[45] Goutham, N.K., Sharma, V. and Naresh R. “Hybridized Gravitational search algorithm for short-term hydrothermal scheduling”, IETE Jr of Res, 62 (4), pp. 468-478 (2016).
46
[46] Feng, Z.K., Niu, W.J., Zhou, Z. et al. “Scheduling of short-term hydrothermal energy system by parallel multi-objective differential evolution”, Appl Soft Comput, 61, pp. 58-71 (2017).
47
[47] Zhang, J., Tang, Q., Chen, Y. et al. “A hybrid particle swarm optimization with small population size to solve the optimal short-term hydro-thermal unit commitment problem”, Energy, 109, pp. 765-780 (2016).
48
[48] Basu, M. “Quasi-oppositional group search optimization for hydrothermal power system”, Int J of Elec Power and Energ Sys, 81, pp. 324-335 (2016).
49
[49] Feng Z.-K.m, Niu W.-J., Zhou J.-Z. et al. “Parallel multi-objective genetic algorithm for short-term economic environmental hydrothermal scheduling”, Energies, 10 pp. 1-22 (2017).
50
[50] Heris M.N., Babaei A.F., Ivatloo B.M. et al. “Improved harmony search algorithm for the solution of non-linear non-convex short-term hydrothermal scheduling”, Energy, 151, pp. 226-237 (2018).
51
[51] Nguyen T.T., Vo D.N. and Dinh B.H. “An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems”, Energy, 155, pp. 930-956 (2018).
52
[52] Wu Y., Wu Y. and Liu X. “Couple-based particle swarm optimization for short-term hydrothermal scheduling”, Appl. Soft Comput., 74, pp. 440-450 (2019).
53
[53]Y. H.K., Liu L., Su L. et al. “ Short-term hydropower generation scheduling using an improved cloud adaptive quantum-inspired binary social spider optimization algorithm”, Water Resources management, 33 (7), pp. 2357-2379 (2019).
54
[54] Gosh, S., Kaur M., Bhullar S. et al. “Hybrid ABC-BAT for solving short-term hydrothermal scheduling problems”, Energies 12, pp. 1-15 (2019).
55
[55] Saremi, S., Mirjalili, S. and Lewsis, A. “Grasshopper optimization algorithm: Theory and application”, Advan in Engg Soft, 105, pp. 30-47 (2017).
56
[56] Ge, X.L., Zhang, Li-Zi, Shu, J. et al. “Short-term hydropower optimal scheduling considering the optimization of water time delay”, Elect Power Syst Res,110, pp 188–197 (2014).
57
[57] Thakur, S., Chanwit B., and Weerakon O. "Optimal hydrothermal generation scheduling using self-organizing hierarchical PSO", IEEE PES General Meeting, 2010.
58
[58] Roy P.K., Sur A. and Pradhan D.K. "Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization", Engineering Applications of Artificial Intelligence, 26, pp. 2516-2524 (2013).
59
[59] Derac, j., Garcia, S., Molina, D. et al. “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”, Swarm and evolutionary computation. 1, pp 3-18 (2011).
60
[60] Shenkin D. J. “Hand book of parametric and no parametric statistical procedures”, 4th ED., Chapman & Hall/CRC (2006).
61