AI in Power Systems: Strategic Insights from Grey Relational Analysis (GRA) Evaluation of Performance Metrics

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ABSTRACT

The integration of Artificial Intelligence (AI) in the power sector has shown significant potential for enhancing operational efficiency, cost savings, reliability, and user satisfaction. This study evaluates the performance of five AI-driven applications—Predictive Maintenance, Smart Grid Optimization, Demand Response System, Renewable Integration, and Fault Detection—using Grey Relational Analysis (GRA) to prioritize their impact on the power industry. Key performance indicators such as cost reduction, efficiency improvement, reliability, and user satisfaction were normalized, and a Grey Relational Grade (GRG) was computed to assess each application’s alignment with optimal performance. The results indicate that AI-powered Fault Detection achieved the highest GRG, positioning it as the most effective application, particularly in enhancing reliability and user satisfaction, critical for grid stability and reduced downtime. AI Predictive Maintenance closely follows, demonstrating strong contributions to cost reduction and preventive maintenance strategies. Renewable Integration ranks third, emphasizing its role in optimizing renewable energy integration for a more sustainable grid. Smart Grid Optimization and Demand Response System, while beneficial in enhancing grid efficiency and balancing demand, rank lower due to relative limitations in cost and reliability metrics. This analysis provides a structured framework for stakeholders in the power sector to prioritize AI investments based on performance metrics. By focusing on top-ranked applications like Fault Detection and Predictive Maintenance, energy providers can achieve enhanced reliability and operational efficiency, paving the way for a more resilient and sustainable energy infrastructure. The findings underscore the transformative potential of AI in addressing the dynamic challenges within the power industry and guiding strategic resource allocation for maximum impact.

Significance of the Study:

The study highlights the transformative potential of AI in the power sector by improving reliability, operational efficiency, and cost management. Fault Detection and Predictive Maintenance, identified as the top-ranking applications, address critical challenges like system downtime and equipment failures, ensuring grid stability. Renewable Integration underscores AI’s role in fostering sustainable energy transitions. By leveraging GRA, this research provides actionable insights for prioritizing AI investments, enabling stakeholders to develop resilient, efficient, and sustainable energy infrastructure.

Summary of the Study:

This study examines the integration of Artificial Intelligence (AI) in the power sector, focusing on five applications: Fault Detection, Predictive Maintenance, Renewable Integration, Smart Grid Optimization, and Demand Response Systems. Using Grey Relational Analysis (GRA), performance metrics such as cost reduction, reliability, efficiency, and user satisfaction were analyzed. Fault Detection emerged as the most impactful application, excelling in reliability and user satisfaction, followed by Predictive Maintenance and Renewable Integration. These findings offer a framework for strategic AI implementation in power systems.