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Robust Power System State Estimation Method Based on Generalized M-Estimation of Optimized Parameters Based on Sampling

Author

Listed:
  • Yu Shi

    (Department of Science, Shandong Jiaotong University, Jinan 250353, China)

  • Yueting Hou

    (Department of Electrical Engineering, Shandong University, Jinan 250100, China)

  • Yue Yu

    (Department of Electrical Engineering, Shandong University, Jinan 250100, China)

  • Zhaoyang Jin

    (Department of Electrical Engineering, Shandong University, Jinan 250100, China)

  • Mohamed A. Mohamed

    (Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61519, Egypt)

Abstract

Robustness is an important performance index of power system state estimation, which is defined as the estimator’s capability to resist the interference. However, improving the robustness of state estimation often reduces the estimation accuracy. To solve this problem, this paper proposes a power system state estimation method for generalized M-estimation of optimized parameters based on sampling. Compared with the traditional robust state estimator, the generalized M-estimator based on projection statistics improves the robustness of state estimation, and the proposed optimized parameter determination method improves the overall accuracy of state estimation by appropriately adjusting its robustness. Considering different degrees of non-Gaussian distributed measurement noises and bad data, the estimation accuracy the proposed method is demonstrated to be up to 23% higher than the traditional generalized M-estimator through MATLAB simulations in IEEE 14, 118 bus test systems, and Polish 2736 bus system.

Suggested Citation

  • Yu Shi & Yueting Hou & Yue Yu & Zhaoyang Jin & Mohamed A. Mohamed, 2023. "Robust Power System State Estimation Method Based on Generalized M-Estimation of Optimized Parameters Based on Sampling," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2550-:d:1052683
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    References listed on IDEAS

    as
    1. Ye Guo & Wenchuan Wu & Boming Zhang & Hongbin Sun, 2014. "A Fast Solution for the Lagrange Multiplier-Based Electric Power Network Parameter Error Identification Model," Energies, MDPI, vol. 7(3), pages 1-12, March.
    2. Jian Chen & Tao Jin & Mohamed A. Mohamed & Andres Annuk & Udaya Dampage, 2022. "Investigating the Impact of Wind Power Integration on Damping Characteristics of Low Frequency Oscillations in Power Systems," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    3. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
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    Cited by:

    1. Ting Chen & Lei Gan & Sheeraz Iqbal & Marek Jasiński & Mohammed A. El-Meligy & Mohamed Sharaf & Samia G. Ali, 2023. "A Novel Evolving Framework for Energy Management in Combined Heat and Electricity Systems with Demand Response Programs," Sustainability, MDPI, vol. 15(13), pages 1-23, July.

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