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Energies 2014, 7(10), 6340-6357;

New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network

Department of Electrical Engineering, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
Author to whom correspondence should be addressed.
Received: 25 August 2014 / Revised: 18 September 2014 / Accepted: 23 September 2014 / Published: 8 October 2014
(This article belongs to the Special Issue Microgrids)
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A hybrid method comprising a chaos synchronization (CS)-based detection scheme and an Extension Neural Network (ENN) classification algorithm is proposed for power quality monitoring and analysis. The new method can detect minor changes in signals of the power systems. Likewise, prominent characteristics of system signal disturbance can be extracted by this technique. In the proposed approach, the CS-based detection method is used to extract three fundamental characteristics of the power system signal and an ENN-based clustering scheme is then applied to detect the state of the signal, i.e., normal, voltage sag, voltage swell, interruption or harmonics. The validity of the proposed method is demonstrated by means of simulations given the use of three different chaotic systems, namely Lorenz, New Lorenz and Sprott. The simulation results show that the proposed method achieves a high detection accuracy irrespective of the chaotic system used or the presence of noise. The proposed method not only achieves higher detection accuracy than existing methods, but also has low computational cost, an improved robustness toward noise, and improved scalability. As a result, it provides an ideal solution for the future development of hand-held power quality analyzers and real-time detection devices. View Full-Text
Keywords: power quality; chaos synchronization detection; extension theory power quality; chaos synchronization detection; extension theory

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Wang, M.-H.; Yau, H.-T. New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network. Energies 2014, 7, 6340-6357.

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