A Configurable Real-Time Event Detection Framework for Power Systems Using Swarm Intelligence Optimization

Power system balancing authorities are routinely affected by sudden frequency fluctuations. These frequency events can take the form of negligible frequency deviations or more severe emergencies that can precipitate cascading outages, depending on the severity of the disturbance and efficacy of remedial action schema. It is imperative to arrest such disturbances quickly by activating primary frequency control measures. This manuscript proposes a configurable event detection framework using optimization methods to tune a detection algorithm to detect events as specified by experts from a Balancing Authority. The utility of the detection framework is demonstrated using a regression-based frequency event detection algorithm with tunable parameters. Two swarm intelligence-based optimization algorithms, Grey Wolf Optimization and Particle Swarm Optimization, are applied to tune the parameters of the detection algorithm according to the definition of frequency events specified by experts. The performances of the GWO and PSO algorithms are analyzed, and the efficacy of the proposed system is demonstrated using an algorithm evaluation environment and a suite of evaluation metrics. The proposed event detection framework is capable of detecting events in real-time with high accuracy and speed using real-world, real-time phasor measurement unit data.