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

被引：61

作者：

Huang, Cong-Hui ^{[1
]}

Lin, Chia-Hung ^{[2
]}

Kuo, Chao-Lin ^{[3
]}

机构：

[1] Far E Univ, Dept Automat & Control Engn, Hsin Shih Township 744, Tainan County, Taiwan

[2] Kao Yuan Univ, Dept Elect Engn, Kaohsiung 821, Taiwan

[3] Natl Kaohsiung Marine Univ, Inst Maritime Informat & Technol, Kaohsiung 81143, Taiwan

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2011年 / 26卷 / 02期

关键词：

Butterfly patterns; chaos synchronization (CS); Lorenz chaos system; particle swarm optimization (PSO); probabilistic neural network (PNN); SLIDING MODE CONTROL; WAVELET TRANSFORM; SUBJECT;

D O I：

10.1109/TPWRD.2010.2090176

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：944 / 953

页数：10

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