Multimodal Operation Data Mining for Grid Operation Violation Risk Prediction

Lingwen Meng, Jingliang Zhong, Shasha Luo, Xinshan Zhu, Yulin Wang and Shumei Zhang ()
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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

Energies, 2024, vol. 17, issue 21, 1-16

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.

Keywords: violation dictionary construction; operation risk prediction; random forests; independent component analysis; mutual information (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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