Real-time Automated Event Analysis and Supervisory Framework for Power Systems using Synchrophasor Measurements

This article presents a real-time event diagnosis approach for improved situational awareness in power systems using wide area synchrophasor measurements. A new index for early detection of events and a scheme for their geographical localization is proposed. A supervised learning-based event classification module is proposed for real-time identification of the events. The discontinuities generated in voltage and frequency measurements due to events are adequately localized in the wavelet domain. Therefore, the discrete wavelet transform is used for detection and localization of events. Time-frequency features are extracted from voltage and frequency measurements, and a multi-class support vector machine classifier is trained. A linear relationship between wavelet coefficients and the magnitude of disturbance triggered by the event is obtained. The proposed work functions as a supervisory layer above the traditional protection equipment and can validate its operation. The effectiveness of the proposed approach is demonstrated on the standard IEEE New England 39 bus test system.