A particle swarm optimisation-trained feedforward neural network for predicting the maximum power point of a photovoltaic array. (June 2020)

• Record Type: Journal Article
• Authors: Al-Majidi, Sadeq D.; Abbod, Maysam F.; Al-Raweshidy, Hamed S.
• Is Part Of: Engineering applications of artificial intelligence. Volume 92(2020)
• Publication Date: 2020-06
• Subjects: Artificial neural network (ANN) -- Fuzzy logic control (FLC) -- Maximum power point tracking (MPPT) -- Photovoltaic (PV) -- Perturb and observe (P&O) -- Efficiency of MPPT (η MPPT); Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103688
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper, a feedforward Artificial Neural Network (ANN) technique using experimental data is designed for predicting the maximum power point of a photovoltaic array. An ANN model training strategy is challenging due to the variations in the training and the operation conditions of a photovoltaic system. In order to improve ANN model accuracy, the Particle Swarm Optimisation (PSO) algorithm is utilised to find the best topology and to calculate the optimum initial weights of the ANN model. Hence, the dilemma between computational time and the best-fitting regression of the ANN model is addressed, as well as the mean squared error being minimised. To evaluate the proposed method, a MATLAB/Simulink model for an installed photovoltaic system is developed. Experimental data of a sunny and cloudy day are utilised to determine the average efficiency of this proposed method under varying atmospheric conditions. The results show that the optimised feedforward ANN technique based on the PSO algorithm using real data predicts the maximum power point accurately, achieving hourly average efficiencies of more than 99.67% and 99.30% on the sunny and cloudy day, respectively. Highlights: Real training data are collected during one year. Two hybrid algorithms based on the ANN and PSO are developed. P&O, FLC, ANN and proposed method are compared. Experimental measurement tests are used to determine the efficiency.