An intelligent MPPT controller for a PV source using cascaded artificial neural network controlled DC link

Abstract

The concept of PV panel characteristics under partially shaded conditions, individual PV panel Maximum Power Point Tracking (MPPT) for better utilization of power output of each panel and artificial neural networks (ANN) controller for tracking actual MPP under partially shaded conditions are presented in this paper. By this setup we extract maximum obtainable solar power from a PV module and use the energy for a DC application. Proposed work presents a two-stage maximum power point tracking (MPPT) controller for a photovoltaic (PV) source using Artificial Neural Network (ANN), under varying weather conditions of solar intensity, solar irradiation and module temperature. At the first-stage, the Artificial Neural Network controller is trained with the data of voltage, irradiance, intensity and temperature of a solar panel which gives the range of duty ratio as an input to the MPPT controller. In the second stage, a simple MPPT controller searches for MPP in the range given by the ANN controller thus quickens the response of the system and by changing the duty cycle of a DC-DC boost converter accordingly tracks the MPP. The MPP of each individual pv panel are tracked and cascaded at the end through a dc link. In this method, experimental data collection of a solar panel is used for development of the ANN architecture which is developed in Weka-3.9 and MATLAB. The whole system is simulated in MATLAB Simulink.