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An Optimized Shunt Hybrid Power Quality Conditioner Based on an Adaptive Neural Network for Power Quality Improvement in Power Distribution Network

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

This paper presents an optimized shunt hybrid power quality conditioner (SHPQC) for the compensation of harmonics and reactive power in power distribution network. A novel signal processing technique based on the adaptive neural network algorithm is applied to determine harmonics for generating the reference signals for the current controller of the SHPQC. The conventional hysteresis current controller for the three-phase insulated gate bipolar transistor (IGBT) voltage source inverter (VSI) is replaced by a current regulated space vector pulse width modulated controller. Simulations and experimental results are presented verifying the excellent power quality improvement performance of the proposed topology while keeping the apparent power of the SHPQC minimal.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Dalian University of Technology, 116023, Dalian, China

    Ming Zhang, Hui Sun, Jiyan Zou & Hang Su

Authors
  1. Ming Zhang
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  2. Hui Sun
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  3. Jiyan Zou
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  4. Hang Su
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhang, M., Sun, H., Zou, J., Su, H. (2004). An Optimized Shunt Hybrid Power Quality Conditioner Based on an Adaptive Neural Network for Power Quality Improvement in Power Distribution Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_94

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_94

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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