Artificial Intelligence Based Inverter Fault Detection System

A malfunction in an inverter may cause an instantaneous power loss, disconnecting home and business systems and possibly destroying the electrical devices that are connected to them. Traditional fault detection methods utilize threshold-based methods or manual monitoring, which are not precise, quick, or timely in fault detection. This results delays in fault detection and increased maintenance costs. To properly identify and classify some of the inverter faults, this research suggests an AI-driven inverter fault detection system based on real-time processing. Through the use of hardware devices like the MCP3008 and NodeMCU alongside the K-Nearest Neighbors (KNN) algorithm, the system is capable of analyzing voltage and current parameters to be able to differentiate between normal and different faulty conditions. Using actual real-time information, the said technology significantly enhances defect detection efficiency with minimal involvement of human interface and immediate corrective measures. Results from the present study confirm that such an AI approach provides a proactive and sophisticated solution to deal with inverter failure by reducing downtime, improving accuracy in fault diagnosis, and enhancing system overall reliability.