Comparison of Conventional Maximum Power Point Tracking Algorithm with Artificial Neural Network Based Maximum Power Point Tracking Algorithm with Respect to Change in Load

In recent times the main source of energy is fossil fuels which is deposited beneath the earth surface years ago. Due to increased population, the deposited fossil fuels have been severely exploited emitting huge amount of toxins in the environment that diminish the quality of human life. This gave birth to explore the renewable energy. The most abundant source of renewable energy available to us is the Solar Energy, but due to inability of the PV cells to track maximum power point due to varying weather conditions and changing load it is necessary to intensify the output power of the Solar Photovoltaic. In this paper artificial neural network based maximum power point tracking algorithm is proposed to enhance the power output of the Solar PV with changing load and comparison between Perturb and Algorithm, Incremental Conductance and Artificial Neural Network has been done to determine the performance. The purpose of the study is to prove the effectiveness of artificial neural network with varying load over the time.