Intelligent Fault Detection and Prediction in Smart Grids Using Supervised Learning Model

The integration of renewable energy sources and distributed power generation in smart grids has significantly increased the complexity of fault detection and prediction. Traditional methods like Supervisory Control and Data Acquisition (SCADA) and Rule-Based Detection (RBD) face challenges in processing the vast amounts of real-time data generated by modern grids, often leading to delayed responses and low fault detection accuracy. Furthermore, conventional techniques such as Fault Tree Analysis (FTA) are rule-dependent and lack adaptability to dynamic grid conditions, resulting in inefficient fault prediction and grid stability issues. To overcome these challenges, this paper introduces an Intelligent Fault Detection and Prediction Model (IFDPM), utilizing supervised learning techniques to enhance the detection and prediction of faults in smart grids. The IFDPM leverages machine learning algorithms to process real-time grid data, improving both the accuracy and speed of fault identification. By continuously adapting to the grid’s operational environment, the proposed model predicts faults before they occur, enabling timely preventive actions. Through extensive simulations and testing, the IFDPM has demonstrated a 0.25% improvement in fault detection accuracy and a 0.20% reduction in response time compared to conventional methods. This approach provides a more reliable and efficient framework for managing fault events in next-generation smart grids.