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A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest

Author

Listed:
  • Songkai Liu

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Ruoyuan Shi

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Yuehua Huang

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Xin Li

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Zhenhua Li

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Lingyun Wang

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Dan Mao

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Lihuang Liu

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Siyang Liao

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

  • Menglin Zhang

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Guanghui Yan

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Lian Liu

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

Abstract

Due to the rapid development of phasor measurement units (PMUs) and the wide area of interconnection of modern power systems, the security of power systems is confronted with severe challenges. A novel framework based on data for static voltage stability margin (VSM) assessment of power systems is presented. The proposed framework can select the key operation variables as input features for the assessment based on partial mutual information (PMI). Before the feature selection procedure is completed by PMI, a feature preprocessing approach is applied to remove redundant and irrelevant features to improve computational efficiency. Using the selected key variables, a voltage stability assessment (VSA) model based on iterated random forest (IRF) can rapidly provide the relative VSM results. The proposed framework is examined on the IEEE 30-bus system and a practical 1648-bus system, and a desirable assessment performance is demonstrated. In addition, the robustness and computational speed of the proposed framework are also verified. Some impact factors for power system operation are studied in a robustness examination, such as topology change, variation of peak/minimum load, and variation of generator/load power distribution.

Suggested Citation

  • Songkai Liu & Ruoyuan Shi & Yuehua Huang & Xin Li & Zhenhua Li & Lingyun Wang & Dan Mao & Lihuang Liu & Siyang Liao & Menglin Zhang & Guanghui Yan & Lian Liu, 2021. "A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest," Energies, MDPI, vol. 14(3), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:715-:d:490080
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    Citations

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    Cited by:

    1. Yuko Hirase & Yuki Ohara & Naoya Matsuura & Takeaki Yamazaki, 2021. "Dynamics Analysis Using Koopman Mode Decomposition of a Microgrid Including Virtual Synchronous Generator-Based Inverters," Energies, MDPI, vol. 14(15), pages 1-20, July.
    2. Oludamilare Bode Adewuyi & Komla A. Folly & David T. O. Oyedokun & Emmanuel Idowu Ogunwole, 2022. "Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    3. Tianhao Song & Xiaoqing Han & Baifu Zhang, 2021. "Multi-Time-Scale Optimal Scheduling in Active Distribution Network with Voltage Stability Constraints," Energies, MDPI, vol. 14(21), pages 1-20, November.
    4. Songkai Liu & Dan Mao & Tianliang Xue & Fei Tang & Xin Li & Lihuang Liu & Ruoyuan Shi & Siyang Liao & Menglin Zhang, 2021. "A Data-Driven Approach for Online Inter-Area Oscillatory Stability Assessment of Power Systems Based on Random Bits Forest Considering Feature Redundancy," Energies, MDPI, vol. 14(6), pages 1-20, March.
    5. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.

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