Optimal power tracker for stand-alone photovoltaic system using Artificial Neural Network (ANN) and Particle Swarm Optimisation (PSO)

In recent years, many intelligent techniques and approaches have been introduced into photovoltaic (PV) system for the utilisation of free harvest renewable energy. Generally, the output power generation of the PV system rely on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilise the collected solar energy optimally. Artificial Neural Network (ANN) is initially used to forecast the solar insolation level and followed by the Particle Swarm Optimisation (PSO) to optimise the power generation of the PV system based on the solar insolation level, cell temperature, efficiency of PV panel and output voltage requirements. This paper proposes an integrated offline PSO and ANN algorithms to track the solar power optimally based on various operation conditions due to the uncertain climate change. The proposed approach has the capability to estimate the amount of generated PV power at a specific time. The ANN based solar insolation forecast has shown satisfactory results with minimal error and the generated PV power has been optimised significantly with the aids of the PSO algorithm.