Feasibility and Performance Issues of employing ECM Drives for Solar PV based Pumping Applications

DOI: https://doie.org/10.0113/Jbse.2025487051

Vinayaksingh S Rajaput , Dr. Basavaraj S Shalavadi

Keywords:

ANN, BLDC motor, Boost Converter, MPTT (Maximum power point tracking), Solar Photovoltaic (SPV) array.

Abstract:

This paper work presents an innovative approach utilizing a non-electrical input-based artificial neural network (ANN) for maximum power point tracking (MPPT) in solar-powered water pumping systems driven by brushless DC (BLDC) motors. The proposed method aims to design a step-size-independent MPPT algorithm employing a neural network to enhance water pumping efficiency. A DC-DC boost converter is integrated to harness optimal power from the solar photovoltaic (SPV) array while facilitating smooth startup of the BLDC motor. The system incorporates pulse-width modulation (PWM) control via a voltage source inverter (VSI) regulated by a DC-link voltage controller to manage the BLDC motor's speed. Electronic commutation is achieved through Hall effect sensor signals, which generate the necessary PWM signals. The performance of the BLDC motor-based pumping system is thoroughly analyzed under varying solar irradiance conditions using MATLAB/Simulink. The study evaluates system efficiency and operational effectiveness, emphasizing its adaptability to dynamic environmental inputs. 

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