WAMS-based two-level robust detection methodology of power system events

Abstract

With the advance of Wide-Area Measurement Systems (WAMS), power system operators have direct access to a large amount of data with valuable information about the power system dynamic performance. As a result, there is a clear need for new data-driven methodologies capable of extracting relevant information from this collected data. One of the key challenges is correctly detecting power system disturbances to avoid false alarms during real-time operation as well as off-line disturbance analysis. This paper proposes a two-level robust event detection methodology aiming to reduce false disturbance detection (false positives/alarms) and validate true events. The methodology is divided into two-levels: (i) signal processing analysis (ii) deep neural network (DNN) classification. In the first level, we apply a widely used spectral analysis based on the Discrete Wavelet Transform (DWT) to event detection. In the second level, the events detected by the DWT are processed by a DNN to check if they are real events or false alarms. Finally, the proposed methodology is evaluated using real synchrophasor event records from the Brazilian Interconnected Power System (BIPS).