Published In
IEEE Access
Document Type
Article
Publication Date
8-18-2024
Subjects
Electric power systems, Smart power grids, Technological innovation
Abstract
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.
Rights
Copyright (c) 2024 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1109/ACCESS.2024.3445312
Persistent Identifier
https://archives.pdx.edu/ds/psu/42442
Publisher
IEEE
Citation Details
Farooq, U., Adham, M., Alsaid, M., & Bass, R. B. (2024). A Configurable Real-time Event Detection Framework for Power Systems using Swarm Intelligence Optimization. IEEE Access, 1–1. https://doi.org/10.1109/access.2024.3445312