Improving Maximum Power Point Tracking Efficiency for Partially Shaded PV Arrays by Nested Zone Prediction via Machine Learning

Under partial shading conditions, photovoltaic (PV) modules in a solar array experience varying irradiance. A Global Maximum (GM) and multiple Local Maximums (LMs) can originate on the Power-Voltage (P-V) curve under such non-uniform irradiance conditions. There are many maximum power point tracking (MPPT) algorithms developed to obtain maximum power. In the discrete environment of sampling, limited samples might be available to predict the true value of peak and as the change of environment is dynamic, limited time is available to make the prediction of the peak. Machine learning is used to overcome the two problems mentioned above as the MPPT algorithms are not robust enough. Various techniques are used to classify PV curve type and predict MPP. In this paper we process an algorithm to categorize the PV curves in best way and machine learning techniques are used to predict the range within which MPP is present.