WAMS Operations in Power Grids: A Track Fusion-Based Mixture Density Estimation-Driven Grid Resilient Approach Toward Cyberattacks

Abstract

Synchrophasor-based wide-area monitoring system (WAMS) applications are vital for acquiring the real-time grid information under ambient and nonlinear conditions. The high dependence on sensor data and signal-processing software for daily grid operation is becoming a concern in an era prone to cyberattacks. To resolve this issue, a mixture density-based maximum likelihood (MDML) estimation was proposed to detect attack vectors. The algorithm was deployed at each monitoring node using a track-level fusion (TLF)-based architecture. A parallelized message passing interface (MPI)-based computing was processed to reduce its computational burden. This work adopted a mature application known as oscillation detection as an example of a monitoring candidate to demonstrate the proposed method. Two test cases were generated to examine the resilience and scalability of the proposed scheme. The tests were conducted in severe data-injection attacks and multiple system disturbances. Results show that the proposed TLF-based MDML estimation method can accurately extract the oscillatory parameters from the contaminated measurements.