电力作业人员通常面临多种安全风险,对其生命健康造成严重威胁,但是场景复杂、随机性强等原因致使电力作业存在风险识别极其困难的问题,因此急需采用合适的技术手段对电力作业风险进行有效识别。该文构建了一种动态兼容多种典型空间的电力作业风险的自适应识别(ARI)模型。从宏观上构建了电力行业典型作业的风险识别框架,建立了典型作业多源要素的全域风险因子体系。通过融合渐进式蜜獾算法、极大熵准则、约束调节机制,提出了一种主客观动态融合的ARI模型,以低成本适应不同的典型作业空间。案例结果验证了所提ARI模型具有鲁棒性强、实施成本优、偏颇风险低的特性,可有效量化电力行业典型作业的多源要素风险。
Abstract
[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.
关键词
电力作业 /
多源要素风险 /
自适应识别 /
主客观动态融合 /
渐进式蜜獾算法
Key words
electric power operations /
multisource element risks /
adaptive identification /
dynamic fusion of subjectivity and objectivity /
progressive honey badger algorithm
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基金
山东省重点研发计划项目(2021CXGC011301)
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