A Review of Data-Driven Modal Analysis in Power Systems with Power Electronics Integration

This review examines data-driven modal analysis methods for power systems with high penetration of power electronics, addressing the limitations of traditional model-based approaches in capturing modern grid dynamics. We systematically analyze four methodological categories: signal processing techniques, Dynamic Mode Decomposition (DMD), Koopman operator theory, and machine learning approaches, evaluating their capabilities in handling nonlinearity, data requirements, and practical trade-offs. Through detailed examination of applications in oscillation detection, source location, and stability assessment, we identify how hybrid methods combining physics-based insights with data-driven techniques show particular promise. The review addresses critical implementation challenges, including PMU data quality requirements, computational complexity considerations, and system integration issues. Future research directions are also discussed at the end of the paper.