Construction and analysis of behavior safety early-warning index system for wind power operation and maintenance personnel

doi:10.16265/j.cnki.issn1003-3033.2022.S1.2797

Abstract

Abstract:

In order to improve the safety behavior level of wind power operation and maintenance personnel, on the premise of independence, completeness, ladder and feasibility, a behavioral safety early warning index system was established from five aspects of human factors, mechanical equipment, operating environment, supervision and management, and information communication. A questionnaire survey was used to obtain behavioral safety early warning data. Based on BP neural network optimized by FOA, a behavior safety warning model with the structure of ″15-10-1″ was established, which was used to train the test data. The results show that the constructed behavioral safety early-warning index system is scientific and reasonable, and the FOA-BP neural network model has a strong early-warning ability and can predict the behavioral safety risks of wind power operation and maintenance personnel. After testing, the model can achieve a better warning effect.

Key words: wind power operation and maintenance personnel, behavior safety early-warning, index system, fruit fly optimization algorithm(FOA), back propagation (BP)neural network

ZHENG Peng, QU Lili, CHENG Libin, HE Zichun, ZHANG Yinlong, CHANG Dingyi. Construction and analysis of behavior safety early-warning index system for wind power operation and maintenance personnel[J]. China Safety Science Journal, 2022, 32(S1): 1-5.

Figures/Tables 4

References 13

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