Machine Learning Based Grid and Solar PV-Connected Water Pumping Using BLDC Motor

Photovoltaic (PV) The integration of grid and solar photovoltaic (PV) systems for water pumping applications using Brushless DC (BLDC) motors presents a sustainable solution to meet energy demands in rural areas. In this study, we propose a novel approach to enhance the efficiency and reliability of water pumping systems by predicting solar voltage and boosted voltage using the Naive Bayes algorithm. By leveraging both grid and solar power sources, our system ensures continuous operation of BLDC motor-driven water pumps, thereby mitigating dependence on conventional energy sources and reducing environmental impact. Our research focuses on optimizing energy utilization and system performance through accurate voltage prediction. The Naive Bayes algorithm, renowned for its simplicity and effectiveness in probabilistic classification, is employed to forecast solar voltage and boosted voltage based on historical data and relevant parameters. This predictive capability enables proactive management of energy resources, ensuring seamless transition between grid and solar power inputs to the BLDC motor, thereby maximizing operational efficiency and minimizing downtime. Experimental validation of our proposed methodology demonstrates its efficacy in real-world scenarios. By analyzing voltage trends and system dynamics, we optimize energy allocation strategies to achieve optimal performance under varying operating conditions. Comparative analysis with traditional water pumping systems highlights the superiority of our approach in terms of efficiency, reliability, and environmental sustainability.