High-Resolution Time-Synchronized Monitoring and Anomaly Detection in Modern Distribution System

Abstract

The increasing penetration of renewable generation introduces new challenges to distribution system monitoring, including faster system dynamics and increased harmonic distortion. This dissertation aims to enhance traditional monitoring frameworks by developing high-resolution, cross-synchronized fundamental and harmonic measurement techniques that assist system operators in more effectively extracting additional dynamic information in modern power distribution systems. The first half of the dissertation focuses on developing advanced time-synchronized measurement techniques for identifying system-wide disturbances and dynamic behaviors. In Chapter 2, a cross-synchronized, frequency-adaptive synchrophasor estimation algorithm is developed to generate time-synchronized fundamental and harmonic phasors with high resolution, enabling real-time spectral analysis. The algorithm improves upon the traditional Discrete Fourier Transform (DFT) by employing adaptive windowing to suppress spectral leakage. In addition, its harmonic estimation accuracy is enhanced through the implementation of M-point average filters that mitigate the leakage from the fundamental component. In Chapter 3, an adaptive linear state estimator for unbalanced distribution systems is developed. A novel optimal PMU placement (OPP) scheme is proposed to guarantee complete observability of the system. The linear state estimator further improves its robustness under contingencies by adaptively reformulating its model to account for topology changes. The second half of the dissertation explores applications of the techniques developed in the first half. In Chapter 4, an SVM-based detector for transformer saturation caused by geomagnetically induced currents (GICs) is developed using synchronized harmonic real power derived from the harmonic synchrophasors estimated by the algorithm in Chapter 2. This approach offers a more cost-efficient alternative to direct GIC measurements. Finally, in Chapter 5, a detection and localization scheme for incipient faults is developed based on singular value decomposition (SVD). Faults are detected by tracking abrupt changes in singular values, and their locations are determined by analyzing correlations among the participations of each monitored bus in fault-related singular value variations.