An ANN-Based Maximum Power Point Tracking Strategy for Single-Stage PV-Powered BLDC Motor Systems

Abstract

This paper proposes an artificial neural network (ANN)-based maximum power point tracking (MPPT) scheme for a single-stage photovoltaic-powered brushless DC motor system. Unlike conventional analytical methods, the proposed approach employs data-driven learning to directly estimate the optimal operating point under varying environmental conditions. Its performance is evaluated through comparison with the traditional Incremental Conductance (INC) algorithm. Simulation results demonstrate that the ANN-based controller achieves faster convergence, higher energy extraction efficiency, and reduced steady-state oscillations. In addition, the proposed method shows improved robustness under different irradiance levels and maintains stable motor operation. The system adopts a simplified single-stage topology in which the photovoltaic array is directly connected to a voltage source inverter driving the BLDC motor, reducing power loss and control complexity. A comprehensive MATLAB/Simulink platform is developed to validate the effectiveness of the proposed scheme under diverse operating conditions.