Hayder, W.; Sera, D.; Ogliari, E.; Lashab, A. On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions. Energies 2022, 15, 7668, doi:10.3390/en15207668.
Hayder, W.; Sera, D.; Ogliari, E.; Lashab, A. On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions. Energies 2022, 15, 7668, doi:10.3390/en15207668.
Hayder, W.; Sera, D.; Ogliari, E.; Lashab, A. On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions. Energies 2022, 15, 7668, doi:10.3390/en15207668.
Hayder, W.; Sera, D.; Ogliari, E.; Lashab, A. On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions. Energies 2022, 15, 7668, doi:10.3390/en15207668.
Abstract
This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between Neural Network and Perturb & Observe method (NN_P&O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as: voltage and power are obtained. The MPPT techniques were simulated using Matlab/Simulink environment. A comparison of the performance of IPSO and NN_P&O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy and simplicity.
Keywords
Maximum Power Point Tracking (MPPT); Improved Particle Swarm Optimization (IPSO); photo-voltaic (PV); Neural Network and Perturb & Observe method (NN-P&O)
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.