Adaptive identification model for multisource element risks in electric power operations
[Objective]Electric power operators often encounter various safety risks that severely threaten their life and health.However,accurate identification of such risks is challenging because of the complex nature of work scenarios and the high randomness of risk factors.Currently,the analysis of electric power operation risks mostly involves the qualitative analysis of the entire operation process.Therefore,there is an urgent need to use appropriate technical means to quantitatively analyze the risks in electric power operations,providing safety assurance for electrical power operators.[Methods]This study develops a dynamic adaptive risk identification(ARI)model compatible with various typical spatial scenarios in the electric power industry.First,we build a macrolevel risk identification framework for typical electric power operations.Second,we establish a comprehensive risk factor classification system for multiple risk sources associated with typical operations.Finally,the ARI model is proposed,which incorporates the progressive honey badger algorithm,maximum entropy criterion,and constraint adjustment mechanism.Case studies validate the effectiveness of this model.[Results]The research outcomes based on case studies demonstrate that the ARI model exhibits high robustness,high cost effectiveness,and low bias risk.Compared with traditional risk identification methods,the ARI model effectively mitigates the effect of subjective judgments by integrating objective criteria.Its more balanced weight distribution reduces unwarranted subjective assumptions arising from inaccurate objective risk data.If the workspace undergoes alterations,there is no imperative need to solicit experts for a reassessment because the ARI model dynamically adjusts the distribution of factor weights based on typical spatial features using the progressive honey badger algorithm and constraint adjustment mechanism.This dynamic adaptation facilitates cost-effective and efficient risk identification in typical spaces.[Conclusions]The proposed model effectively quantifies the multisource element risks of typical operation scenarios in the electric power industry.Integrating the model with existing risk information systems further enhances precision and efficiency of existing risk control measures,providing crucial technical support for the safety assurance of electrical power operators.
electric power operationsmultisource element risksadaptive identificationdynamic fusion of subjectivity and objectivityprogressive honey badger algorithm
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