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A Dynamic State Estimation of Power System Harmonics Using Distributed Related Kalman Filter

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Algorithms and Architectures for Parallel Processing (ICA3PP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9528))

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Abstract

In order to improve the performance of measuring the harmonic state, a distributed related Kalman filter method for power system dynamic harmonic state estimation is presented. Firstly, the neighbor correlation coefficient is introduced into the distributed Kalman filtering. And then, a method for calculating the neighbor node fusion variables which suitable for power harmonic measurements is given based on the distributed related Kalman filter. Lastly, further distributed fusion processing among the neighbor nodes of estimated values is proposed. The algorithm is simulated on IEEE-14 bus power system. The results show that the proposed algorithm has less communication cost, better anti-disturbance performance, and more accurate estimation in comparison to the conventional Kalman filtering.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (51307041, 51304058 and 51177034). The authors would like to thank the anonymous reviewers for their invaluable comments for improving this work.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Wei Sun, Chanjuan Zhao, Jianping Wang, Chenghui Zhu, Daoming Mu & Qiyue Li

  2. Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230000, China

    Liangfeng Chen

  3. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Jie Li

Authors
  1. Wei Sun
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  2. Chanjuan Zhao
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  3. Jianping Wang
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  4. Chenghui Zhu
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  5. Daoming Mu
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  6. Liangfeng Chen
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  7. Jie Li
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  8. Qiyue Li
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Corresponding author

Correspondence to Chanjuan Zhao .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Guojun Wang

  2. The University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. University of Murcia, Murcia, Murcia, Spain

    Gregorio Martinez

  4. Hunan University , Changsha, China

    Kenli Li

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Sun, W. et al. (2015). A Dynamic State Estimation of Power System Harmonics Using Distributed Related Kalman Filter. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9528. Springer, Cham. https://doi.org/10.1007/978-3-319-27119-4_36

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  • DOI: https://doi.org/10.1007/978-3-319-27119-4_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27118-7

  • Online ISBN: 978-3-319-27119-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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