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

  • Wei Sun
  • Chanjuan ZhaoEmail author
  • Jianping Wang
  • Chenghui Zhu
  • Daoming Mu
  • Liangfeng Chen
  • Jie Li
  • Qiyue Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9528)


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.


Harmonic state estimation Distributed related kalman filter Correlation coefficient IEEE-14 bus power system Anti-disturbance 



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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wei Sun
    • 1
  • Chanjuan Zhao
    • 1
    Email author
  • Jianping Wang
    • 1
  • Chenghui Zhu
    • 1
  • Daoming Mu
    • 1
  • Liangfeng Chen
    • 2
  • Jie Li
    • 3
  • Qiyue Li
    • 1
  1. 1.School of Electrical Engineering and AutomationHefei University of TechnologyHefeiChina
  2. 2.Hefei Institutes of Physical Science, Chinese Academy of SciencesHefeiChina
  3. 3.School of Computer and InformationHefei University of TechnologyHefeiChina

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