An improved search ability of particle swarm optimization algorithm for tracking maximum power point under shading conditions
DOI:
https://doi.org/10.20998/2074-272X.2022.2.04Keywords:
conventional particle swarm optimization, maximum power point, opposition based particle swarm optimization algorithmAbstract
Introduction. Extracting maximum possible power from solar energy is a hot topic of the day as other sources have become costly and lead to pollution. Problem. Dependency on sunlight for power generation makes it unfeasible to extract maximum power. Environmental conditions like shading, partial shading and weak shading are the major aspect due to which the output of photovoltaic systems is greatly affected. Partial shading is the most known issue. Goal. There have been many proposed techniques and algorithms to extract maximum output from solar resources by use of photovoltaic arrays but every technique has had some shortcomings that couldn’t serve the complete purpose. Methodology. Nature inspired algorithms have proven to be good to search global maximum in a partially shaded multipeak curve which includes particle swarm optimization, artificial bee colony algorithm, and flower pollination algorithm. Methods. Particle swarm optimization algorithm is best among these in finding global peaks with less oscillation around maximum power point, less complexity, and easy to implement nature. Particle swarm optimization algorithm has the disadvantage of having a long computational time and converging speed, particularly under strong shading conditions. Originality. In this paper, an improved opposition based particle swarm optimization algorithm is proposed to track the global maximum power point of a solar photovoltaic module. Simulation studies have been carried out in MATLAB/Simulink R2018a. Practical value. Simulation studies have proved that opposition based particle swarm optimization algorithm is more efficient, less complex, more robust, and more flexible and has better convergence speed than particle swarm optimization algorithm, perturb and observe algorithm, hill climbing algorithm, and incremental conductance algorithm.
References
Ullah M.F., Hanif A. Power quality improvement in distribution system using distribution static compensator with super twisting sliding mode control. International Transactions on Electrical Energy Systems, 2021, vol. 31, no. 9, art. no. e12997 doi: https://doi.org/10.1002/2050-7038.12997.
Akbar F., Mehmood T., Sadiq K., Ullah M.F. Optimization of accurate estimation of single diode solar photovoltaic parameters and extraction of maximum power point under different conditions. Electrical Engineering & Electromechanics, 2021, no. 6, pp. 46-53. doi: https://doi.org/10.20998/2074-272X.2021.6.07.
Hosenuzzaman M., Rahim N.A., Selvaraj J., Hasanuzzaman M., Malek A.B.M.A., Nahar A. Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation. Renewable and Sustainable Energy Reviews, 2015, vol. 41, pp. 284-297. doi: https://doi.org/10.1016/j.rser.2014.08.046.
European Photovoltaic Industry Association. Global Market Outlook for Photovoltaics 2014–2018. Brussels, May 2014. 60 p. Available at: https://helapco.gr/wp-content/uploads/EPIA_Global_Market_Outlook_for_Photovoltaics_2014-2018_Medium_Res.pdf (Accessed 20 August 2021).
Rabaia M.K.H., Abdelkareem M.A., Sayed E.T., Elsaid K., Chae K.-J., Wilberforce T., Olabi A.G. Environmental impacts of solar energy systems: A review. Science of the Total Environment, 2021, vol. 754, p. 141989. doi: https://doi.org/10.1016/j.scitotenv.2020.141989.
Ahmed W., Sheikh J.A., Nouman M., Ullah M.F., Mahmud M.A.P. Techno-economic analysis for the role of single end energy user in mitigating GHG emission. Energy, Sustainability and Society, 2021, vol. 11, no. 1, p. 32. doi: https://doi.org/10.1186/s13705-021-00307-3.
Hayder W., Abid A., Hamed M. Ben, Sbita L. MPPT based on P&O method under partially shading. 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD), 2020, pp. 538-542. doi: https://doi.org/10.1109/SSD49366.2020.9364107.
Ali M.N., Mahmoud K., Lehtonen M., Darwish M.M.F. An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems. IEEE Access, 2021, vol. 9, pp. 26420-26430. doi: https://doi.org/10.1109/ACCESS.2021.3058052.
Alajmi B.N., Ahmed K.H., Finney S.J., Williams B.W. Fuzzy-Logic-Control Approach of a Modified Hill-Climbing Method for Maximum Power Point in Microgrid Standalone Photovoltaic System. IEEE Transactions on Power Electronics, 2011, vol. 26, no. 4, pp. 1022-1030. doi: https://doi.org/10.1109/TPEL.2010.2090903.
Masoum M.A.S., Dehbonei H., Fuchs E.F. Theoretical and experimental analyses of photovoltaic systems with voltage and current-based maximum power-point tracking. IEEE Transactions on Energy Conversion, 2002, vol. 17, no. 4, pp. 514-522. doi: https://doi.org/10.1109/TEC.2002.805205.
Esram T., Kimball J.W., Krein P.T., Chapman P.L., Midya P. Dynamic maximum power point tracking of photovoltaic arrays using ripple correlation control. IEEE Transactions on Power Electronics, 2006, vol. 21, no. 5, pp. 1282-1291. doi: https://doi.org/10.1109/TPEL.2006.880242.
Kim I.-S. Sliding mode controller for the single-phase grid-connected photovoltaic system. Applied Energy, 2006, vol. 83, no. 10, pp. 1101-1115. doi: https://doi.org/10.1016/j.apenergy.2005.11.004.
Patel H., Agarwal V. MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array Characteristics. IEEE Transactions on Energy Conversion, 2008, vol. 23, no. 1, pp. 302-310. doi: https://doi.org/10.1109/TEC.2007.914308.
Ben Salah C., Ouali M. Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems. Electric Power Systems Research, 2011, vol. 81, no. 1, pp. 43-50. doi: https://doi.org/10.1016/j.epsr.2010.07.005.
Syafaruddin, Karatepe E., Hiyama T. Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions. IET Renewable Power Generation, 2009, vol. 3, no. 2, p. 239. doi: https://doi.org/10.1049/iet-rpg:20080065.
Roy Chowdhury S., Saha H. Maximum power point tracking of partially shaded solar photovoltaic arrays. Solar Energy Materials and Solar Cells, 2010, vol. 94, no. 9, pp. 1441-1447. doi: https://doi.org/10.1016/j.solmat.2010.04.011.
Kennedy J. Particle swarm optimization. Encyclopedia of machine learning, 2010, pp. 760-766.
Tizhoosh H.R. Opposition-Based Learning: A New Scheme for Machine Intelligence. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2005, pp. 695-701, doi: https://doi.org/10.1109/CIMCA.2005.1631345.
Wang H., Wu Z., Rahnamayan S., Liu Y., Ventresca M. Enhancing particle swarm optimization using generalized opposition-based learning. Information Sciences, 2011, vol. 181, no. 20, pp. 4699-4714. doi: https://doi.org/10.1016/j.ins.2011.03.016.
Li J., Chen T., Zhang T., Li Y.X. A Cuckoo Optimization Algorithm Using Elite Opposition-Based Learning and Chaotic Disturbance. Journal of Software Engineering, 2015, vol. 10, no. 1, pp. 16-28. doi: https://doi.org/10.3923/jse.2016.16.28.
Poli R., Kennedy J., Blackwell T. Particle swarm optimization. Swarm Intelligence, 2007, vol. 1, no. 1, pp. 33-57. doi: https://doi.org/10.1007/s11721-007-0002-0.
Obukhov S., Ibrahim A., Aboelsaud R. Maximum Power Point Tracking of Partially Shading PV system Using Particle Swarm Optimization. Proceedings of the 4th International Conference on Frontiers of Educational Technologies - ICFET ’18, 2018, pp. 161-165. doi: https://doi.org/10.1145/3233347.3233375.
Anwar N., Hanif A.H., Khan H.F., Ullah M.F. Transient Stability Analysis of the IEEE-9 Bus System under Multiple Contingencies. Engineering, Technology & Applied Science Research, 2020, vol. 10, no. 4, pp. 5925-5932. doi: https://doi.org/10.48084/etasr.3273.
Mehdi M.F., Ahmad A., Ul Haq S.S., Saqib M., Ullah M.F. Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function. Electrical Engineering & Electromechanics, 2021, no. 2, pp. 64-69. doi: https://doi.org/10.20998/2074-272X.2021.2.09.
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Copyright (c) 2022 H. Saeed, T. Mehmood, F. A. Khan, M. S. Shah, M. F. Ullah, H. Ali
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