A new computing perturb-and-observe-type algorithm for MPPT in solar photovoltaic systems and evaluation of its performance against other variants by experimental validation

Document Type : Research Article

Authors

1 Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

2 Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan.

3 b. Department of Basic Sciences and Related Studies, Mehran University of Engineering and Technology, Jamshoro, Pakistan.; Supply Chain and Operations Management Research Group, Mehran University of Engineering and Technology, Jamshoro, Pakistan.

Abstract

Solar energy is becoming a mainstream energy source with considerable attention from analysts these days. The photovoltaic (PV) system’s output power fluctuates with temperature and sunlight affecting its efficiency. To extract accessible power by PV system, maximum power point tracking (MPPT) method is used. A famous strategy, regularly utilized for simplicity and low cost, is the Perturb and Observe (PO) algorithm. However, there are a few downsides of PO algorithm, which result in power loss and low efficiency. We evaluate the performance of the conventional PO against some of its enhancements, specially a recent PO-variant, for MPPT. Experiments are conducted at different irradiances and temperature levels in two ways: with load and with battery, by conventional PO and its variants. Outlining strategy to reach optima and stability of the methods are discussed. The PO variants are rated from view-points of stability, accuracy, post-MPP oscillations and tracking speed. The recommendations can prove to be fruitful for the practitioners working with MPPT in PV solar systems using PO algorithms. The validation of simulation results has been made using the real time experimental results. The new PO-variant appears to be a reliable computing algorithm for MPPT in solar PV systems.

Keywords

References

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Scientia Iranica
Volume 26, Special Issue on machine learning, data analytics, and advanced optimization techniques...
Transactions on Computer Science & Engineering and Electrical Engineering (D)
November and December 2019
Pages 3656-3671

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  • Receive Date: 05 August 2019
  • Accept Date: 08 September 2019

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