Design and implementation of an ANN-based MPPT controller for solar-powered BLDC motor systems: a case study in Yalo Lala village, Ethiopia

Abstract This study focuses on the design and implementation of an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller for a solar-powered water pumping system tailored for the Yalo Lala village in Ethiopia. The research addresses the critical challenge of ensuring a reliable water supply in remote rural areas where conventional power sources are unavailable or insufficient. The proposed system integrates a Brushless DC (BLDC) motor powered by a photovoltaic (PV) array, optimized through the ANN-based MPPT controller. The performance of this controller is evaluated against traditional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (INC), demonstrating superior efficiency, faster response times, and better adaptability to dynamic environmental conditions. Key components of the system, including the PV array, DC-DC boost converter, and BLDC motor, are designed in detail, and simulations validate the system’s performance under varying irradiance levels. Results confirm the system’s capability to meet a daily water demand of 520 cubic meters for a population of 5200, requiring a minimum power of 17 kW. The study concludes that the ANN-based MPPT controller significantly enhances the efficiency, reliability, and sustainability of solar-powered water pumping systems, making it a viable solution for off-grid rural areas like Yalo Lala.