Identification of Acceleration Harmonics for a Hydraulic Shaking Table by Using Hopfield Neural Network

Document Type : Article

Authors

College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang,China

Abstract

 The paper aims to develop a harmonic identification scheme for a hydraulic shaking table’s sinusoidal acceleration response. Nonlinearities are inherent in a hydraulic shaking table. Some of them are dead zone of servo valve, backlash and friction between joints, and friction in actuator. Nonlinearities cause harmonic distortion of the system shaking response when it correspondsto a sinusoidal excitation. This lowers the system control performance. An efficient, time-domain acceleration harmonic identification is developed by using Hopfield neural network. Due to the introduction of energy function used to optimize the computation for the identification harmonic method, the fully connected, single layer feedback neural network does not require training in advance and is able to identifyharmonic amplitudes and phase angles. Each harmonic,as well asthe fundamental response,can be directly obtained.Simulations and experiments show very promising results that the proposed scheme is really applicable to identify harmonicswith high precision and good convergence. Comparisons between the presented method and other method are carried out to further demonstrate its efficiency.

Keywords

Main Subjects


References
 1. Yao, J.J., Hu, S.H., Fu, W. and Han, J.W. Impact of
excitation signal upon the acceleration harmonic distortionof an electro-hydraulic shaking table", Journalof Vibration and Control, 17(7), pp. 1106-1111 (2011).
2. Chung, Y.L.,  Nagae, T., Hitaka, T. and Nakashima,
M. Seismic resistance capacity of high-rise buildings subjected to long-period ground motions: E-defense shaking table test", Journal of Structural Engineering,
136(6), pp. 637-644 (2010).
3. Merritt, H.E., Hydraulic Control Systems, New York:
Wiley (1967).4. Lin, H.C. Intelligent neural network based dynamic
power system  harmonic analysis", International Conference
on Power System Technology, Singapore, pp.
244-248 (2004).
5. Wang, X.H., He, Y.G. and Long, Y. Neural network
based high accuracy frequency harmonic analysis in
power system", 4th International Symposium on Neural
Networks, Nanjing, China, pp. 1006-1014 (2007).
6. Hamad, M.S., Gadoue, S.M. and Williams, B.W.
Harmonic compensation of a six-pulse current source
controlled converter using neural network-based shunt
active power lter", IET Power Electronics, 5(6), pp.
747-754 (2012).
7. Abdeslam, D.O., Wira, P., Fliellert, D. and Merckle, J.
Power harmonic identi cation and compensation with
an arti cial neural network method", IEEE International
Symposium on Industrial Electronics, Montreal,
QC, Canada, pp. 1732-1737 (2006).
8. Almaita, E. and Asumadu, J.A. On-line harmonic
estimation in power system based on sequential training
radial basis function neural network", Proceedings
of the IEEE International Conference on Industrial
Technology, Auburn, AL, United states, pp. 139-144
(2011).
9. Arabaci, H. An arti cial neural network approach for
sensorless speed estimation via rotor slot harmonics",
Turkish Journal of Electrical Engineering and Computer
Sciences, 22(4), pp. 1076-1084 (2014).
10. Nascimento, C.F., Oliveira Jr., A.A., Goedtel, A. and
Dietrich, A.B. Harmonic distortion monitoring for
nonlinear loads using neural-network-method", Applied
Soft Computing Journal, 13(1), pp. 475-482
(2013).
11. Ulinuha, A., Masoum, M.A.S. and Islam, S. Hybrid
genetic-fuzzy algorithm for volt/var/total harmonic
distortion control of distribution systems with high
penetration of non-linear loads", IET Generation,
Transmission and Distribution, 5(4), pp. 425-439
(2011).
12. Zamanan, N., Sykulski, J. and Al-Othman, A.K. A
digital technique for online identi cation and tracking
of power system harmonics based on real coded genetic
algorithm", 6th IASTED International Conference on
European Power and Energy Systems, Rhodes, Greece,
pp. 144-148 (2006).
13. Xue, Z.H., Du, P.J. and Su, H.J. Harmonic analysis
for hyperspectral image classi cation integrated with
PSO optimized SVM", IEEE Journal of Selected Topics
in Applied Earth Observations and Remote Sensing,
7(6), pp. 2131-2146 (2014).
14. Yin, Y.N., Lin, W.X. and Li, W.L. Estimation
amplitude and phase of harmonic based on improved
PSO", 8th IEEE International Conference on Control
and Automation, Xiamen, China, pp. 826-831 (2010).
15. Yang, X.F. and Wang, J.L. An analysis method for
power system transient signal based on improved prony
algorithm and PSO algorithm", International Conference
on Information Technology and Applications,
Chengdu, China, pp. 209-213 (2013).
310 J. Yao et al./Scientia Iranica, Transactions B: Mechanical Engineering 25 (2018) 299{310
16. Vasumathi, B. and Moorthi, S. Implementation of
hybrid ANNPSO algorithm on FPGA for harmonic
estimation", Engineering Applications of Arti cial Intelligence,
25(3), pp. 476-483 (2012).
17. Ray, P.K. and Subudhi, B. Ensemble-Kalman- lterbased
power system harmonic estimation", IEEE
Transactions on Instrumentation and Measurement,
61(12), pp. 3216-3224 (2012).
18. Singh, S.K., Sinha, N., Goswami, A.K. and Sinha, N.
Several variants of Kalman Filter algorithm for power
system harmonic estimation", International Journal of
Electrical Power and Energy Systems, 78(1), pp. 793-
800 (2016).
19. Jatoth, R.K. and Reddy, G.A. A hybrid GA-adaptive
particle swarm optimization based tuning of unscented
Kalman lter for harmonic estimation", Lecture Notes
in Computer Science, Chennai, India, pp. 380-388
(2010).
20. Ren, J. and Kezunovic, M. A wavelet method for
power system frequency and harmonic estimation",
North American Power Symposium, Arlington, TX,
United states, pp. 1052-1056 (2010).
21. Jain, S.K., Saxena, D. and Singh, S.N. Adaptive
wavelet neural network based harmonic estimation of
single-phase systems", Proceedings of the 2011 International
Conference and Utility Exhibition on Power
and Energy Systems: Issues and Prospects for Asia,
Pattaya City, Thailand, pp. 1113-1117 (2012).
22. Yang, J.N. and Cheng, L. A wavelet model for online
tracking of power system: Time-varying harmonics
using Kaman ltering", International Conference on
Informationization, Automation and Electri cation in
Agriculture, Zhengjiang, China, pp. 351-358 (2008).
23. Yao, J.J., Jiang, G.L., Di, D.T. and Liu, S. Acceleration
harmonic identi cation for an electro-hydraulic
servo shaking table based on the normalized leastmean-
square adaptive algorithm", Journal of Vibration
and Control, 19(1), pp. 47-55 (2013).
24. Yao, J.J., Di, D.T., Jiang G.L. and Gao, S. Acceleration
amplitude-phase regulation for electro-hydraulic
servo shaking table based on LMS adaptive ltering
algorithm", Journal of Vibration and Control, 85(10),
pp. 1581-1592 (2012).
25. Yao, J.J., Zhang, L., Chen, S., Wan, Z.S., Wang, T.
and Niu, Q.T. Translational shaking tests achieved on
a 6-DOF hydraulic parallel manipulator", Industrial
Robot: An International Journal, 43(1), pp. 103-111
(2016).
26. Hillar, C., Mehta, R. and Koepsel, K. A hop eld
recurrent neural network trained on natural images
performs state-of-the-art image compression", IEEE
International Conference on Image Processing, Paris,
France, pp. 4092-4096 (2014).
27. Martin, T.H., Neural Network Design, Beijing: China
Machine Press (2007).
28. Singh, S.K., Nath, A., Chakraborty, R., Kalita, J.,
Sinha, N. and Goswami, A.K. Fast transverse-RLS
algorithm based power system harmonic estimation",
2014 International Conference on Information Communication
and Embedded Systems, Chennai, India,
pp. 1253-1257 (2015).
29. Garanayak, P., Panda, G. and Ray, P.K. Harmonic
estimation using RLS algorithm and elimination with
improved current control technique based SAPF in a
distribution network", International Journal of Electrical
Power and Energy Systems, 73(12), pp. 209-217
(2015).
30. Ray, P.K. and Subudhi, B. BFO optimized RLS
algorithm for power system harmonics estimation",
Applied Soft Computing Journal, 12(8), pp. 1965-1977
(2012).