Data-Driven Event Diagnosis in Transmission Systems with Incomplete and Conflicting Alarms Given Sensor Malfunctions

Accurate fault event diagnosis with incomplete and conflicting alarms given sensor malfunctions is a challenging problem for power system operators. To solve this problem, this study proposes a data-driven approach based on Mixed Integer Linear Programming (MILP) for fast determination of fault event scenarios with uncertainties. The uncertainties include failures and malfunction of relays and circuit breakers (CBs) as well as incomplete/incorrect sensor alarms at the control center. To improve the accuracy for fault event diagnosis, redundant alarms from multiple sources, i.e., Phasor Measurement Units (PMUs), Supervisory Control and Data Acquisition (SCADA), and Sequence of Events Recorders (SERs) are jointly used in this study. The temporal correlation of sensor alarms is incorporated in the constraints of the MILP model. The resulting data-driven algorithm determines the most credible fault scenario that is well supported by the available sensor alarms at the control center. Simulation results of the IEEE 14-bus system, the synthetic South Carolina 500-bus system, and a real-world complex event scenario demonstrate the effectiveness of the proposed approach for accurate and efficient fault event diagnosis.