Feasibility Study and Performance Issues of using ECM Drives in Solar PV Pumping Applications Optimized by PSO and GA

  • Vinayaksingh S Rajaput et al.
Keywords: Artificial Neural Network (ANN), BLDC motor, Boost Converter, Maximum Power Point Tracking (MPPT), Solar Photovoltaic (SPV) array, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)

Abstract

This paper undertakes a feasibility analysis and evaluates the performance issues associated with the use of Electronically Commutated Motor (ECM) drives in solar photovoltaic (PV) pumping applications, optimized using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The integration of ECM drives with solar PV systems is assessed for its potential to enhance efficiency and adaptability under varying solar irradiance and load conditions. The optimization techniques, PSO and GA, are employed to optimize key parameters such as maximum power point tracking, energy losses, and system reliability. The results highlight the trade-offs between different performances metrics, including efficiency, stability, and response time, providing insights into the suitability of ECM drives optimized by PSO and GA for solar PV pumping applications. This research aims to contribute to the development of more efficient and sustainable water pumping solutions, leveraging the advantages of advanced drive technologies and optimization algorithms.

Author Biography

Vinayaksingh S Rajaput et al.

Vinayaksingh S Rajaput1, Dr. Basavaraj S Shalavadi 2
1Research Scholar, Department of Electrical & Electronics Engineering Shri Dharmasthala Manjunatheshwara College of Engineering & Technology, Dharwad Karnataka, India,
2Professor, Department of Electrical and Electronics Engineering S.D.M. College of Engineering & Technology. Dharwad, Karnataka, India, affiliated to Visvesvaraya Technological University Belagavi Karnataka
Communication mail: rvinayaksingh@gmail.com, shalavadibs@gmail.com

Published
2024-02-04
Issue
Volume 2024
Section
Regular Issue

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