Comparative Study Of P&O and PSO Particle Swarm Optimization MPPT Controllers Under Partial Shading

9270a21a-ee5f-40a4-8b00-490c81854cf320221006083104801wseas:wseasmdt@crossref.orgMDT DepositInternational Journal of Electrical Engineering and Computer Science2769-250710.37394/232027https://wseas.com/journals/eeacs/index.php62920226292022410.37394/232027.2022.4https://wseas.com/journals/eeacs/2022.phpComparative Study Of P&O and PSO Particle Swarm Optimization MPPT Controllers Under Partial ShadingMahboubaBrahmiResearch Laboratory LISIER, National High School of Engineers of Tunis ENSIT, University of Tunis, 5 Av. Taha Hussein, Tunis, 1008, TUNISIAChiheb BenRegayaResearch Laboratory LISIER, National High School of Engineers of Tunis ENSIT, University of Tunis, 5 Av. Taha Hussein, Tunis, 1008, TUNISIAHichemHamdiResearch Laboratory LISIER, National High School of Engineers of Tunis ENSIT, University of Tunis, 5 Av. Taha Hussein, Tunis, 1008, TUNISIAAbderrahmenZaafouriResearch Laboratory LISIER, National High School of Engineers of Tunis ENSIT, University of Tunis, 5 Av. Taha Hussein, Tunis, 1008, TUNISIAMaximum power point tracking (MPPT) is a vital and essential requirement for photovoltaic (PV) systems under normal irradiance and partial shading conditions (PSCs). Although, perturb and observe (P&O) algorithm does not map the global maximum power point (GMPP), whereas the algorithm for particle swarm optimization (PSO) tracks it efficiently. This paper explores the rapid determination of GMPP under PSCs using a proposed particle swarm optimization MPPT technique which operates in conjunction with a boost converter. To achieve this analysis, MATLAB/SIMIULINK is used. The results of the simulation illustrate the high tracking performance of the proposed technique under various irradiance patterns.1062022106202245507https://wseas.com/journals/eeacs/2022/a14eeacs-007(2022).pdf10.37394/232027.2022.4.7https://wseas.com/journals/eeacs/2022/a14eeacs-007(2022).pdf10.1109/codit55151.2022.9804021M. Brahmi, B. R. Chiheb, H. Hamdi and Z. Abderrahmen (2022), “Comparative Study of P&O and PSO Particle Swarm Optimization MPPT Controllers for Photovoltaic Systems”, 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT). 10.1109/scc47175.2019.9116178H. Hamdi, B. R. Chiheb and Z. Abderrahmen (2019), “Performance improvement of a photovoltaic system with a radial basis function network based on particle swarms optimization”, 2019 International Conference on Signal, Control and Communication (SCC). 10.1186/s41601-019-0145-1Hamdi, H., Regaya, C. B., & Abderrahmen, Z. (December 2019). A sliding-neural network control of induction-motor-pump supplied by photovoltaic generator. 10.1109/scc47175.2019.9116178Hichem, H., Chiheb, B. R., & Abderrahmen, Z. (2019). Performance improvement of a photovoltaic system with a radial basis function network based on particle swarms optimization. 10.1016/j.solener.2019.02.064Hichem, H., Chiheb, B. R., & Abderrahmen, Z. (May 2019). Real-time study of a photovoltaic system with boost converter using the PSO-RBF neural network algorithms in a MyRio controller (Vol. 183). Jones, A. R. (01 January 2022). Design and Implementation of Photovoltaic Powered SEPIC DCDC Converter Using Particle Swarm Optimization (PSO) Technique. Roy, S. S. (07 December 2021). A PSO Based MPPT Controller for Solar PV System at Variable Atmospheric Conditions (Vol. volume 1373). TaraEmail, B. B., & Suresh, H. L. (21 August 2021). Comparative Analysis of MPPT Techniques in GridConnected and Stand-alone PV System.