Abstract
The large integration of phasor measurement units (PMUs) providing high-fidelity phasor data at an increased rate of delivery has enhanced situational awareness in the power system. The high-resolution phasor data from wide-area measurement systems (WAMS) is utilized for a number of real-time and offline applications such as disturbance monitoring, mode metering, attack detection, state estimation, etc. Real-time dynamic disturbance identification and visualization are of paramount importance for timely fault diagnosis and emergency control actions. This paper explores real-time detection, identification, and localization of dynamic events in a power system using WAMS data. The paper presents heuristics detection criteria using integrated voltages and frequency change markers. Additionally, a tier-based event localization strategy is proposed using unsupervised clustering of event data spread into tiers. The paper also presents fundamental time-domain disposition-based features extraction and machine learning based classification methods. The proposition is tested for simulated test cases for IEEE-39 bus system in DigSILENT/PowerFactory.
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