Intelligent Reconfigurable Photovoltaic System

Abstract

The global maximum power point tracking of a PV array under partial shading represents a global optimization problem. Conventional maximum power point tracking algorithms fail to track the global maximum power point, and global optimization algorithms do not provide global maximum power point in real-time mode due to a slow convergence process. This paper presents an intelligent reconfigurable photovoltaic system on the basis of a modified fuzzy neural net that includes a convolutional block, recurrent networks, and fuzzy units. We tune the modified fuzzy neural net based on modified multi-dimension particle swarm optimization. Based on the processing of the sensors’ signals and the photovoltaic array’s image, the tuned modified fuzzy neural net generates an electrical interconnection matrix of a photovoltaic total-cross-tied array, which reaches the global maximum power point under non-homogeneous insolation. Thus, the intelligent reconfigurable photovoltaic system represents an effective machine learning application in a photovoltaic system. We demonstrate the advantages of the created intelligent reconfigurable photovoltaic system by simulations. The simulation results reveal robustness against photovoltaic system uncertainties and better performance and control speed of the proposed intelligent reconfigurable photovoltaic system under non-homogeneous insolation as compared to a GA-based reconfiguration total-cross-tied photovoltaic system.

A Review on Machine Learning Applications for Solar Plants

“…Researchers proposed a lot of rPV’s structures, including Honey Comb, Series Parallel, Total Cross Tied (TCT), etc. [ 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 ]. According to the articles [ 140 , 141 , 142 ], the last one generates more power in case of PS as compared to other structures.…”

“…In Table 12 , we summarize the ML models for rPV from studies [ 130 , 131 , 132 , 133 , 134 , 135 , 136 ].…”

“…The goal of study [ 136 ] is a GMPPT of an rPV array based on the MFNN in a case of PS. We created an optimal MFNN based on the dataset that contains the 20 kW PV array’s signals under PS including PV array images that were congregated at the town Abakan from 31 January 2018 through 31 December 2018.…”