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This paper offers a comprehensive exploration of the application of digital twin technology for power system stability. Leveraging both physics-based and data-driven modeling techniques, we propose a novel digital twin framework that provides high fidelity simulations of power systems. Several case studies, including voltage stability assessment, dynamic stability analysis, predictive maintenance of power transformers, and renewable energy integration optimization, substantiate the efficacy of the framework under different real-world operating conditions. Our research conclusively indicates that the digital twin framework significantly enhances prediction accuracy, accelerates analysis speed, improves system reliability, and aids in efficient risk management in power systems. As a significant outcome, the framework has demonstrated its potential to revolutionize power system management and operations by facilitating predictive maintenance and efficient renewable energy integration. The paper concludes by emphasizing that strategic partnerships between academia and industry, robust cybersecurity measures, and the development of comprehensive standards and guidelines are key to unlocking the full potential of digital twin technology in power systems. |
