Chaos Synchronization-Based Detector for Power-Quality Disturbances Classification in a Power System

This paper proposes a chaos synchronization (CS)-based detector for power-quality disturbances classification in a power system. The Lorenz chaos system realized a CS-based detector to track the dynamic errors from the fundamental signal and the distorted signal, including power harmonics and voltage fluctuation phenomena. A CS-based detector uses dynamic error equations to extract the features and construct various butterfly patterns. The probabilistic neural network is an adaptive classifier that performs pattern recognition. The particle swarm optimization algorithm is used to estimate the optimal parameter and can heighten the accuracy. For a sample power system, the test results showed accurate discrimination, rapid learning, good robustness, and faster processing time for detecting disturbances.