A Review of AI-Based Load Estimation and Predictive Outage Analysis Integrated with SCADA in Smart Power Systems

Abstract

The fast evolution of traditional power systems to intelligent smart grids has heightened the need to come up with advanced data-driven solutions to ensure reliability, efficiency, and stability. The combination of Artificial Intelligence (AI) and Machine Learning (ML) methods have made it possible to achieve substantial gains in load forecasting, fault detection, predictive maintenance in smart grid settings. The paper (a review) addresses the topic of AI-based load estimation and predictive outage analysis with consideration to Supervisory Control and Data Acquisition (SCADA) systems to optimize smart power plants. Different models such as deep learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and ensemble learning models are examined on the basis of their effectiveness in processing complex and dynamic grid data. The literature indicates that AI-based models could be highly accurate in predicting the load and in detecting faults, where they can be used to provide real-time monitoring and decision-making possibilities. Also, the combination of IoT and edge computing makes better systems responsive to changes in energy management systems and minimizes latency in energy management systems. Although these developments were made, issues like data complexity, system integration, and cybersecurity risks in SCADA systems have been a major concern. This paper has presented a review of the prevailing methodologies, identification of research gaps, and discussion of future research directions to develop resilient and intelligent smart power systems.