Power System Monitoring and Controlling System

Abstract

Artificial Intelligence and Machine Learning (AI/ML) methodologies are increasingly being leveraged to improve power systems' monitoring, operation, and control. This review paper comprehensively analyses cutting-edge AI/ML techniques employed across various power system applications. The synergy between AI/ML, Internet-of-Things (IoT) devices, and advanced metering infrastructure enables real-time monitoring, predictive maintenance, and autonomous control capabilities. Key challenges about data quality, computational complexity, and uncertainty quantification are examined. The review also identifies future research directions, highlighting the potential of deep learning, federated learning, and hybrid AI architectures to revolutionize intelligent power systems further. This paper consolidates recent advancements and provides insights into leveraging AI/ML technologies for enhanced reliability, efficiency, and resilience of modern power networks. The increasing complexity and demand for reliable, efficient, and secure power systems necessitate the development of advanced monitoring and control strategies. This research proposes a novel monitoring and controlling power system that integrates state-of-the-art sensor networks with intelligent algorithms to enable real-time data acquisition, analysis, and decision-making. The proposed system employs a distributed network of smart sensors, including Phasor Measurement Units (PMUs), Intelligent Electronic Devices (IEDs), and Advanced Metering Infrastructure (AMI), to collect high-resolution, time-synchronized data from various points across the power grid.