| 引用本文: | 李存斌,董 佳,丁 佳.基于大数据的燃煤发电运行风险实时评估[J].电力系统保护与控制,2022,50(16):47-57.[点击复制] |
| LI Cunbin,DONG Jia,DING Jia.Real-time assessment of operational risk of coal-fired power generation based on big data[J].Power System Protection and Control,2022,50(16):47-57[点击复制] |
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| 摘要: |
| 燃煤发电机组运行过程中面临各种风险，一旦发生故障将造成不小的经济损失和社会影响。为了保障机组的安全生产和稳定运行，建立了燃煤发电运行风险实时评估模型，从而及时制定故障检修计划。基于大数据关联规则分析了燃煤发电运行风险与影响因子的关联关系。在此基础上，基于熵权法对影响因子赋权，并结合灰色关联理论、证据理论和Dempster合成规则实现基本信度分配函数的确定和融合，从而得到燃煤发电机组运行风险值和风险等级。最后，以发电厂A的燃煤发电机组进行算例分析，其风险评估结果与实际运行情况具有相关一致性，证明了模型的现实意义。 |
| 关键词: 大数据 燃煤发电 风险评估 证据理论 灰色关联分析 |
| DOI：DOI: 10.19783/j.cnki.pspc.211415 |
| 投稿时间：2021-10-21修订日期：2021-12-30 |
| 基金项目:国家自然科学基金项目资助(71840004) |
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| Real-time assessment of operational risk of coal-fired power generation based on big data |
| LI Cunbin,DONG Jia,DING Jia |
| (School of Economics and Management, North China Electric Power University, Beijing 102206, China)) |
| Abstract: |
| Coal-fired power generation units are faced with various risks in their operation. A fault can cause considerable economic loss and social impact. In order to ensure the safe production and stable operation of the unit, a real-time assessment model of operational risk of a coal-fired power generation is established, so as to formulate the troubleshooting plan in time. Based on big data association rules, the association relationship between the operation risk of coal-fired power generation and impact factors is analyzed. The impact factors are weighted based on the entropy weight method, and the determination and fusion of the basic reliability distribution function are realized by combining the grey correlation theory, evidence theory and Dempster synthesis rules, so as to obtain the operation risk value and risk level of a coal-fired power generation unit. Finally, taking the coal-fired power generation unit of power plant A as an example, the risk assessment results are consistent with the actual operation. This proves the practical significance of the model.
This work is supported by the National Natural Science Foundation of China (No. 71840004). |
| Key words: big data coal-fired power generation risk assessment evidence theory grey relational analysis |
