Intelligent Fault Diagnosis System for Power Grids using AI and IoT Integration

Abstract

The incorporation of smart technologies, renewable energy sources, and distributed systems is making abstract-Modern power grids more complex than ever before and fault detection and management are becoming harder than ever. To help solve these problems, this paper has suggested an Intelligent Fault Diagnosis System (IFDS) that will integrate real-time monitoring, based on Internet of Things (IoT) systems, with advanced Artificial Intelligence (AI) methods. The system is based on a hybrid deep learning architecture, which combines the Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to correctly learn both spatial and temporal dynamics in power system data. The parameters (voltage, current, temperature, etc.) measured by IoT sensors are constantly updated, which allows real-time analysis and a more rapid decision-making process. The suggested solution does not only identify and categorize the faults properly, but also integrates proactive maintenance to project possible breakdowns before they happen. It was experimentally proven that the system is able to achieve high accuracy of 97.4% in fault classification and has a shorter response time of 58 ms that is better than both traditional and standalone machine learning methodologies. The predictive model also has 96.3 percent accuracy, which is a good guarantee of early fault prediction. These findings underscore how the proposed AI-IoT integrated framework is effective in improving the reliability of the power grid, downtime reduction, and proactive and smarter power grid maintenance technologies.