A Comparative Analysis of ANN Algorithms Performance for Maximum Power Point Tracking in Solar Photovoltaic System

Abstract

Abstract: This paper presents the comparative analysis of Artificial Neural Network (ANN) based algorithms in maximum power point tracking (MPPT) for solar photovoltaic system. The algorithms deployed in this paper are Bayesian Regularization (BR), Levenberg- Marquardt (LM) and Scaled Conjugate Gradient algorithm (SCG).The MPPT model for solar photovoltaic system was designed in MATLAB/Simulink environment and ANN toolbox was used to for analysis. For training 70% data was used and rest 30% was used for validation and testing purpose, which was 15% each. The proposed model was trained seven times for each algorithm and best result was taken. The performance of BR algorithm was better in terms of mean square error which was less than LM algorithm. But with LM algorithm the learning rate, thus time required for training is less so it can be preferred over Time constrained system. SCG algorithm trained the system perfectly with low performance hence it is not suitable for MPPT module. Solar module of 200W with 2 modules in series and 1 module in parallel were taken. The output generated from the trained MPPT solar energy system was 400 W.