AI-Based Digital Twin for Predictive Fault Detection and Energy Optimization in Smart Microgrids
AI-Based Digital Twin for Predictive Fault Detection and Energy Optimization in Smart Microgrids
1Dr G.Ravi Kumar,Proessor, Department Of EEE & Bapatla Engineering College
2M.V.S.Sasank Department Of EEE & Bapatla Engineering College
3N.Bala Anjaneyulu Naik, Department Of EEE & Bapatla Engineering College
4S.Manasa Lakshmi, Department Of EEE & Bapatla Engineering College
5S.Anil Kumar, Department Of EEE & Bapatla Engineering College
6T.Kiranmai, , Department Of EEE & Bapatla Engineering College
Abstract - Significant operational challenges, such as power imbalance, voltage instability, and increased system losses, have been brought about in contemporary smart microgrids by the growing use of distributed generation and renewable energy sources. Digital twin (DT) technology has become a viable option for intelligent control of complex energy systems, predictive analysis, and real-time monitoring. For predictive fault detection and energy optimization in smart microgrids, this paper suggests an AI-based Digital Twin framework. To monitor critical system parameters like voltage deviation, power loss, load mismatch, battery state-of-charge (SOC), frequency stability, reactive power imbalance, and harmonic distortion, the suggested system combines real-time sensor data, digital twin simulation models, and data-driven analytics.
To evaluate system improvements using key performance indicators (KPIs) such as energy balance stability, reliability enhancement, and economic cost reduction, a thorough framework for performance evaluation is created. According to simulation results, the Digital Twin framework greatly enhances microgrid performance, reducing power losses by about 40%, load mismatch by 35%, voltage stability by 30%, frequency stability by 25%, and total harmonic distortion by 30%. Additionally, the suggested system uses real-time optimization and predictive fault detection to improve battery SOC stability and overall grid reliability. The findings demonstrate how Digital Twin technology can help next-generation smart microgrids operate intelligently, robustly, and energy-efficiently.
Key Words: Digital Twin, Smart Microgrid, Predictive Fault Detection, Energy Optimization, Machine Learning, Particle Swarm Optimization.