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

Author

Listed:
  • Meng-Hui Wang

    (Department of Electrical Engineering, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan)

  • Her-Terng Yau

    (Department of Electrical Engineering, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan)

Abstract

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.

Suggested Citation

  • Meng-Hui Wang & Her-Terng Yau, 2014. "New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network," Energies, MDPI, vol. 7(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:10:p:6340-6357:d:40971
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    Citations

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    Cited by:

    1. Jiandong Yang & Mingjiang Wang & Chao Wang & Wencheng Guo, 2015. "Linear Modeling and Regulation Quality Analysis for Hydro-Turbine Governing System with an Open Tailrace Channel," Energies, MDPI, vol. 8(10), pages 1-16, October.
    2. Vitor Hugo Ferreira & André da Costa Pinho & Dickson Silva de Souza & Bárbara Siqueira Rodrigues, 2021. "A New Clustering Approach for Automatic Oscillographic Records Segmentation," Energies, MDPI, vol. 14(20), pages 1-18, October.
    3. Jingjing Bai & Wei Gu & Xiaodong Yuan & Qun Li & Feng Xue & Xuchong Wang, 2015. "Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms," Energies, MDPI, vol. 8(9), pages 1-18, August.
    4. Borin, Daniel, 2024. "Caputo fractional standard map: Scaling invariance analyses," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    5. Guoqing Weng & Feiteng Huang & Jun Yan & Xiaodong Yang & Youbing Zhang & Haibo He, 2016. "A Fault-Tolerant Location Approach for Transient Voltage Disturbance Source Based on Information Fusion," Energies, MDPI, vol. 9(12), pages 1-23, December.
    6. Cheng-Biao Fu & An-Hong Tian & Yu-Chung Li & Her-Terng Yau, 2018. "Fractional Order Chaos Synchronization for Real-Time Intelligent Diagnosis of Islanding in Solar Power Grid Systems," Energies, MDPI, vol. 11(5), pages 1-14, May.

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