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State Estimation for Active Distribution Networks Considering Bad Data in Measurements and Topology Parameters

Author

Listed:
  • Yizhe Chen

    (Zhaoqing Power Supply Bureau, Guangdong Power Grid Co., Ltd., Zhaoqing 526040, China)

  • Yifan Gao

    (Zhaoqing Power Supply Bureau, Guangdong Power Grid Co., Ltd., Zhaoqing 526040, China)

  • Kai Gan

    (Zhaoqing Power Supply Bureau, Guangdong Power Grid Co., Ltd., Zhaoqing 526040, China)

  • Ming Li

    (Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Chengzhi Wei

    (Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Xiaoyi Guo

    (Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Ruifeng Zhao

    (Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Jiangang Lu

    (Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Liang Che

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

Abstract

SE is critical in ADNs integrating renewable DGs. Traditional SE methods face the challenges of increasing SE errors and decreased robustness due to the adverse impact of bad data in measurements and topology parameters. To address these issues, this paper proposes a robust SE method that considers bad data in measurements and topology parameters. First, a bad measurement data processing model is proposed to improve measurement and SE accuracy by generating high-precision pseudo-measurements through adaptive learning from historical data sequences to replace the bad measurement data in measurements. Second, a robust SE model combining network estimation and linear estimation is proposed, which enhances SE accuracy and robustness under bad data generated in measurements and topology parameters in ADNs. In a simulation, the proposed method’s effectiveness is verified on the modified IEEE 33-node system.

Suggested Citation

  • Yizhe Chen & Yifan Gao & Kai Gan & Ming Li & Chengzhi Wei & Xiaoyi Guo & Ruifeng Zhao & Jiangang Lu & Liang Che, 2025. "State Estimation for Active Distribution Networks Considering Bad Data in Measurements and Topology Parameters," Energies, MDPI, vol. 18(9), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2222-:d:1643830
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