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Online Evaluation Method for Low Frequency Oscillation Stability in a Power System Based on Improved XGboost

Author

Listed:
  • Wenping Hu

    (State Grid Hebei Electric Power Co., Ltd., Technology Research Institute, Shijiazhuang 050021, China)

  • Jifeng Liang

    (State Grid Hebei Electric Power Co., Ltd., Technology Research Institute, Shijiazhuang 050021, China)

  • Yitao Jin

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Fuzhang Wu

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Xiaowei Wang

    (State Grid Hebei Electric Power Co., Ltd., Technology Research Institute, Shijiazhuang 050021, China)

  • Ersong Chen

    (State Grid Hebei Electric Power Co., Ltd., Technology Research Institute, Shijiazhuang 050021, China)

Abstract

Low frequency oscillation in an interconnected power system is becoming an increasingly serious problem. It is of great practical significance to make online evaluation of actual power grid’s stability. To evaluate the stability of the power system quickly and accurately, a low frequency oscillation stability evaluation method based on an improved XGboost algorithm and power system random response data is proposed in this paper. Firstly, the original input feature set describing the dynamic characteristics of the power system is established by analyzing the substance of low frequency oscillation. Taking the random response data of power system including the disturbance end time feature and the dynamic feature of power system as the input sample set, the wavelet threshold is applied to improve its effectiveness. Secondly, using the eigenvalue analysis method, different damping ratios are selected as threshold values to judge the stability of the system low-frequency oscillation. Then, the supervised training with improved XGboost algorithm is performed on the characteristics of stability. On this basis, the training model is obtained and applied to online low frequency oscillation stability evaluation of a power system. Finally, the simulation results of the eight-machine 36-node test system and Hebei southern power grid show that the proposed low frequency oscillation online evaluation method has the features of high evaluation accuracy, fast evaluation speed, low error rate of unstable sample evaluation, and strong anti-noise ability.

Suggested Citation

  • Wenping Hu & Jifeng Liang & Yitao Jin & Fuzhang Wu & Xiaowei Wang & Ersong Chen, 2018. "Online Evaluation Method for Low Frequency Oscillation Stability in a Power System Based on Improved XGboost," Energies, MDPI, vol. 11(11), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3238-:d:184563
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    Citations

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    Cited by:

    1. Sun-Bin Kim & Vattanak Sok & Sang-Hee Kang & Nam-Ho Lee & Soon-Ryul Nam, 2019. "A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems," Energies, MDPI, vol. 12(9), pages 1-19, April.
    2. Vattanak Sok & Sun-Woo Lee & Sang-Hee Kang & Soon-Ryul Nam, 2022. "Deep Neural Network-Based Removal of a Decaying DC Offset in Less Than One Cycle for Digital Relaying," Energies, MDPI, vol. 15(7), pages 1-14, April.

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