Dual tracking of MPP for PV under diffuse irradiation and urban microclimatic conditions using sub-interval prediction via Bayesian-optimized regression

Abstract

Optimal power extraction from a photovoltaic (PV) plant in urban and rural areas varies due to microclimatic conditions and diffuse irradiations. Traditional methods such as Perturb and Observe (P&O) and Incremental Conductance (INC) are used in urban and rural PV power plants. Diffuse irradiation and microclimatic conditions are different in urban and rural areas. Moreover, one of two sides of the PV characteristics is used for tracking the maximum power point (MPP), i.e., either constant voltage or constant current region. In this paper, a Bayesian-optimized multiple regression-based dual tracking (BM-DT) is proposed for a sub-intervals prediction technique (SIPT) and the tracking of MPP is done using both sides of the PV characteristics. Moreover, the voltage step used for tracking the MPP is not a fixed quantity and is predicted using BM. The proposed BM-DT technique predicts sub-intervals from a specified initial voltage interval. Moreover, the maximum power point is tracked through interval and SIPT, based on microclimatic conditions. As tracking is done along both sides of the photovoltaics (PV), the performance with outstanding power extraction efficiencies at low, medium, and high power levels is 99%, 99.2% and 99.4%, respectively.