Predictive Maintenance of Smart Grid Components Based on Real-Time Data Analysis

This paper examines the application of predictive maintenance strategies for Smart Grid components using realtime data analysis. Through comparative and inductive analysis of existing literature and industry reports, we explore how advanced analytics and machine learning can address limitations of traditional maintenance approaches in ensuring grid reliability and efficiency. Key findings include the potential for IoT sensors and edge computing to enable continuous monitoring of critical parameters, integration of deep learning algorithms for time series analysis, and development of dynamic maintenance scheduling based on risk assessment. We propose strategies for optimizing maintenance operations through predictive analytics, including the prioritization of repair works based on failure risk prediction. While predictive maintenance shows promise for reducing operational costs and improving reliability, challenges in data infrastructure investment and standardization remain. This research highlights predictive maintenance as a critical tool for enhancing Smart Grid performance and resilience through real-time data-driven decision making.