Machine learning based cascaded ANN MPPT controller for erratic PV shading circumstances

Abstract

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.

A Real-Time Deterministic Peak Hopping Maximum Power Point Tracking Algorithm for Complex Partial Shading Condition

“…The rapid advancements in computing technology have driven the emergence of numerous artificial intelligence (AI) methods used to solve the PSCs such as artificial neural network (ANN) [6][7], fuzzy logic controller (FLC) [8], and adaptive neuro fuzzy inference system (ANFIS) [9]. While these AI algorithms are known for their quick convergence, their implementations can be excessively complex and computationally demanding.…”