DIGITAL TWIN-BASED CONDITION MONITORING AND PREDICTIVE MAINTENANCE OF POWER SYSTEM ASSETS

Abstract

The increasing complexity of modern power systems has created a strong need for reliable condition monitoring and timely maintenance of critical power system assets such as transformers, generators, circuit breakers, and transmission equipment. Traditional maintenance approaches mainly depend on fixed schedules or manual inspections, which may result in unnecessary maintenance, unexpected failures, and increased operational costs. This paper proposes a Digital Twin-Based Condition Monitoring and Predictive Maintenance framework for power system assets. The proposed system creates a virtual representation of physical assets by continuously collecting real-time operational data such as temperature, voltage, current, vibration, load, and other relevant health parameters. These data are processed and analyzed using machine learning techniques to identify abnormal operating conditions, estimate asset health, and predict potential failures before they occur. The Digital Twin continuously synchronizes with the physical asset, enabling real-time visualization of its operating condition and historical performance. Predictive models analyze degradation patterns and generate early warnings when abnormal behavior or failure risks are detected. The framework supports condition-based maintenance by helping operators determine the appropriate time for inspection, servicing, or component replacement. By combining real-time monitoring, digital modeling, data analytics, and predictive maintenance, the proposed approach can reduce unplanned outages, improve asset reliability, increase equipment lifetime, and optimize maintenance costs. The system provides a scalable and intelligent solution for developing more reliable, efficient, and resilient smart power grids.