IOT-Enabled Real-Time Monitoring and Predictive Maintenance of PV-FED BLDC Irrigation Water Pumping Systems

Photovoltaic (PV) powered BLDC Motors are finding greater applications in present day water pumping systems, especially in the farming areas. Though the use of Renewable energy is an appreciable feature, this system suffers from unintended outages and reduced operational efficiency. This is due to the lack of smart methods to identify and fix the problems before they arise. In addition, the repairing work done post the occurrence of faults is always expensive and there are chances of prolonged degradation. Because of the absence of a real-time monitoring system, there is no provision to alert the user about serious issues. Thus, the aim of this work is to configure a real time setup which can (1) identify the faults using Machine Learning (ML) tools (2) trigger warning messages via Fuzzy Logic control (3) provide greater working strength and live monitoring using IoT. The IoT based problem checking and fixing is made possible through sensors placed on the different parts of the system and the link with the Cloud Storage. Data validation algorithms and cloud-based tools are employed to ensure the accuracy and reliability of sensor data. Based on the learned fault patterns, the smart model classifies potential faults over time. The timely warning messages generated through the IoT system enable effective monitoring and support informed decision-making. The proposed system is developed and evaluated using MATLAB and ThingSpeak platforms through simulation testing. The results indicate that the MPPT accuracy reaches 96.3% even under varying solar conditions. The output efficiency during the test runs is found to be 89.4%. The drop seen in the Fault Rate is 0.75. The pictures from cloud have helped for better visualization and application. For future expansion, edge thinking setups can be considered. Thus, this proposed system guarantees improved irrigation and power usage.