Leveraging Artificial Intelligence and Machine Learning Techniques Improve Performance of Electrical Systems and Smart Grids

Abstract

The integration of AI and ML in electrical power systems and smart grids has the ability to greatly enhance their efficiency, reliability, along with sustainability. With the increasing complexity of modern power grids as well as the growing reliance on RES, AI and ML provide advanced solutions to optimize operations, enhance grid stability, and address challenges such as intermittent energy generation, energy storage, and fault detection. This research examines the application of AI and ML, such as supervised learning, deep learning, reinforcement learning, along with anomaly detection, to key areas of power systems, including LF, fault detection, PM, and grid optimization. The use of AI for predictive maintenance, load prediction, as well as real-time optimization of power flow is particularly beneficial for ensuring the efficient integration of renewable energy sources while maintaining system stability. Moreover, these technologies enable the development of self-healing grids that can detect along with respond to faults autonomously, reducing downtime as well as enhancing the resilience of the grid. This paper presents a comprehensive analysis of recent advancements in AI and ML applications within electrical power systems, highlighting case studies and performance evaluations to demonstrate their impact on operational performance and cost-effectiveness. The findings suggest that the adoption of AI and ML can significantly reduce energy losses, improve fault detection accuracy, and increase the overall efficiency of power distribution. As power grids evolve towards more decentralized and renewable-driven systems, AI and ML will be integral to their futuresuccess. The research concludes by exploring the challenges and opportunities in scaling these technologies to address the growing demands of modern energy systems.