Supervisory Protection and Automated Event Diagnosis Using PMU Data

This paper presents a new framework for supervisory protection and situational awareness to enhance grid operations and protection using modern wide-area monitoring systems. In contrast to earlier approaches dealing with the combined processing of data from multiple phasor measurement units (PMUs), the proposed approach analyzes only the PMU data with the strongest or the most prominent disturbance signature. The specific contributions of this paper are: (a) new criteria for identification of PMU with the strongest signature, (b) simplified approach for quick detection of faults, (c) early classification of eight other disturbances suitable for near real-time response, (d) time-frequency transform-based feature extraction techniques for speedy and reliable classifiers, and (e) a promising approach to locate disturbances within narrow geographical constraints. The contributions are verified with exhaustive simulation data from the Western Electricity Coordination Council system model and limited real PMU data.