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Multimodal Operation Data Mining for Grid Operation Violation Risk Prediction

Author

Listed:
  • Lingwen Meng

    (Electric Power Scientific Research Institute of Guizhou Power Grid Guizhou, Guiyang 550000, China)

  • Jingliang Zhong

    (Electric Power Scientific Research Institute of Guizhou Power Grid Guizhou, Guiyang 550000, China)

  • Shasha Luo

    (Electric Power Scientific Research Institute of Guizhou Power Grid Guizhou, Guiyang 550000, China)

  • Xinshan Zhu

    (School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Yulin Wang

    (School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Shumei Zhang

    (School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

With the continuous expansion of the power grid, the issue of operational safety has attracted increasing attention. In power grid operation control, unauthorized operations are one of the primary causes of personal accidents. Therefore, preventing and monitoring unauthorized actions by power grid operators is of critical importance. First, multimodal violation data are integrated through information systems, such as the power grid management platform, to construct a historical case database. Next, word vectors for three types of operation-related factors are generated using natural language processing techniques, and key vectors are selected based on generalized correlation coefficients using mutual information, enabling effective dimensionality reduction. Independent component analysis is then employed for feature extraction and further dimensionality reduction, allowing for the effective characterization of operational scenarios. For each historical case, a risk score is derived from a violation risk prediction model constructed using the Random Forests (RF) algorithm. When a high-risk score is identified, the K-Nearest Neighbor (KNN) algorithm is applied to locate similar scenarios in the historical case database where violations may have occurred. Real-time violation risk assessment is performed for each operation, providing early warnings to operators, thereby reducing the likelihood of violations, and enhancing the safety of power grid operations.

Suggested Citation

  • Lingwen Meng & Jingliang Zhong & Shasha Luo & Xinshan Zhu & Yulin Wang & Shumei Zhang, 2024. "Multimodal Operation Data Mining for Grid Operation Violation Risk Prediction," Energies, MDPI, vol. 17(21), pages 1-16, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5424-:d:1510390
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    References listed on IDEAS

    as
    1. Zhuola Zhang & Shiyuan Lin & Yingjin Ye & Zhao Xu & Yihang Zhao & Huiru Zhao & Jingqi Sun, 2022. "A Hybrid MCDM Model for Evaluating the Market-Oriented Business Regulatory Risk of Power Grid Enterprises Based on the Bayesian Best-Worst Method and MARCOS Approach," Energies, MDPI, vol. 15(9), pages 1-17, April.
    2. Ulaa AlHaddad & Abdullah Basuhail & Maher Khemakhem & Fathy Elbouraey Eassa & Kamal Jambi, 2023. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
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