Accident Factors Importance Ranking for Intelligent Energy Systems Based on a Novel Data Mining Strategy

Rongbin Li, Jian Zhang and Fangming Deng ()
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Rongbin Li: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Jian Zhang: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Fangming Deng: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China

Energies, 2025, vol. 18, issue 3, 1-18

Abstract: As global energy networks expand and smart grid technology evolves rapidly, the volume of historical power accident data has increased dramatically, containing valuable risk information that is essential for building efficient public safety early warning systems. This paper introduces an innovative text analysis method, the Sparse Coefficient Optimized Weighted FP-Growth Algorithm (SCO-WFP), which is designed to optimize the processing of power accident-related textual data and more effectively uncover hidden patterns behind accidents. The method enhances the evaluation of sparse risk factors by preprocessing, clustering analysis, and calculating piecewise weights of power accident data. The SCO-WFP algorithm is then applied to extract frequent itemsets, revealing deep associations between accident severity and risk factors. Experimental results show that, compared to traditional methods, the SCO-WFP algorithm significantly improves both accuracy and execution speed. The findings demonstrate the method’s effectiveness in mining frequent itemsets from text semantics, facilitating a deeper understanding of the relationship between risk factors and accident severity.

Keywords: smart grid; power security; text analysis; data mining strategy; FP-Growth algorithm (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: 2025
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