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A Novel QGA-UKF Algorithm for Dynamic State Estimation of Power System

  • Lihua Zhou
  • Minrui FeiEmail author
  • Dajun Du
  • Wenting Li
  • Huosheng Hu
  • Aleksandar Rakić
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)

Abstract

To ensure the safe operation of a power system, it is necessary to conduct its state estimation continuously. In this paper, a novel quantum genetic algorithm (QGA) is combined with unscented Kalman filter (UKF) for dynamic state estimation of power systems. Firstly, an innovation matrix is used to improve the estimation accuracy by constructing an adaptive correction factor for correcting the prediction covariance matrix in real time. The prediction error of constant Holt’s two-parameter model is then analysed for adaptive optimization, and QGA is employed to adjust the parameters dynamically. Finally, simulation tests are carried out on IEEE 30 bus system and the results indicate that the proposed approach, namely QGA-UKF, has good estimation accuracy and stability that are higher than GA-UKF and UKF.

Keywords

Power system Dynamic state estimation Unscented Kalman filter Quantum genetic algorithm 

Notes

Acknowledgments

This work was supported by the Natural Science Foundation of China under Grant 61633016 and Project 111 under Grant D18003.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lihua Zhou
    • 1
  • Minrui Fei
    • 1
    Email author
  • Dajun Du
    • 1
  • Wenting Li
    • 1
  • Huosheng Hu
    • 2
  • Aleksandar Rakić
    • 3
  1. 1.Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronical Engineering and AutomationShanghai UniversityShanghaiChina
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK
  3. 3.School of Electrical EngineeringUniversity of BelgradeBelgradeSerbia

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