Optimizing Power System Reliability for Transmission Line Fault Detection Using ANN

This paper introduces an innovative approach to augment power system reliability by implementing efficient transmission line fault detection, integrating IoT technology and advanced algorithms such as Artificial Neural Networks (ANNs). With the escalating demand for uninterrupted electricity supply, effective fault detection mechanisms are imperative to minimize downtime and prevent potential damage. Our proposed system employs under voltage and over voltage analysis, leveraging a micro controller (NODE MCU) along with essential components like Lamp, Switch, and voltage and current sensors, operating with an input voltage of 230 volts. By incorporating IoT capabilities, our system enables remote monitoring and real-time data analysis, facilitating swift response to fault occurrences. The integration of ANNs enhances fault detection accuracy and speed, ensuring timely identification and localization of faults. Extensive simulations and experimental validations demonstrate the efficacy of our approach in optimizing power system reliability. This research contributes to the advancement of fault detection techniques, offering a robust solution for ensuring uninterrupted power supply in modern electrical grids.