发电机组非计划停机事件的贝叶斯网络分析

doi:10.3969/j.issn.2097-0706.2022.03.012

综合智慧能源 ›› 2022, Vol. 44 ›› Issue (3): 77-82.doi: 10.3969/j.issn.2097-0706.2022.03.012

• 智能电力 • 上一篇

发电机组非计划停机事件的贝叶斯网络分析

马栋梁¹(), 陈辉²(), 朱延海³(), 蒋园¹()

1.河北金融学院信息工程与计算机学院,河北保定 071051
2.淮北申能发电有限公司,安徽淮北 235066
3.国能陈家港发电有限公司生产技术部,江苏盐城,224631

收稿日期:2021-08-03 修回日期:2021-10-15 出版日期:2022-03-25 发布日期:2022-03-28
作者简介:马栋梁（1982）,男,高级工程师,博士,从事能源大数据分析与区域能源经济分析方面的研究, madongliang168@163.com。
陈辉（1982）,男,工程师,从事火电厂热控相关技术及管理工作, jxxychh@163.com。
朱延海（1973）,男,高级工程师,从事发电厂热工控制和可靠性研究, jshyzyh@sina.com。
蒋园（1986）,女,助教,从事大数据分析、机器学习与数据挖掘方面的研究, 3300306132@qq.com。
基金资助:
河北省教育厅高等学校科学技术研究项目(Z2020112);河北金融学院金融创新与风险管理研究中心开放基金(JDKF2021014)

Bayesian network analysis of unplanned shutdown of generating units

MA Dongliang¹(), CHEN Hui²(), ZHU Yanhai³(), JIANG Yuan¹()

1. School of Information Engineering and Computer Science,Hebei Finance University,Baoding 071051,China
2. Huaibei Shenergy Power Generation Company Limited,Huaibei 235066,China
3. China Energy Chenjiagang Power Generation Company Limited,Production Technology Department,Yancheng 224631,China

Received:2021-08-03 Revised:2021-10-15 Online:2022-03-25 Published:2022-03-28

摘要/Abstract

摘要：

为保障发电机组安全稳定运行,需要对机组产生故障的原因进行深入分析。利用发电厂非计划停机事件的分析报告,对非计划停机事件的影响因素进行归纳总结,最终形成预测电厂非计划停机的贝叶斯网络图。通过发电厂非计划停机的贝叶斯网络分析,对各种影响因素对于非计划停机的影响程度进行因果推断分析。结果表明,当对设备老化评估不足时,由于设备发生故障而导致停机事件发生的概率会大幅提升。当设备长期处于恶劣环境中时,老化故障的概率会迅速增大。通过贝叶斯网络分析,明确各种影响因素对非计划停机事件的影响概率情况,为发电企业的能源大数据分析提供参考,提高机组设备安全运行的可靠性。

关键词: 非计划停机, 发电机组, 贝叶斯网络, 因果推断, 能源大数据

Abstract:

To ensure the safe and stable operation of generator sets,it is necessary to analyze the causes of their failures intensively.Based on the analysis report on unplanned shutdowns of a power plant,the influencing factors of the unplanned outage events are summarized,and the Bayesian network diagram for predicting the unplanned outages is made.According to Bayesian network analysis on the unplanned shutdowns of the power plant,the causal inference analysis on the impacts of various factors on unplanned shutdowns is carried out.The results show that when the aging status evaluation of equipment is insufficient,the probability of outage events caused by equipment failure will be significantly increased.Placing equipment in an unfavorable environment for a long time will boost the probability of aging faults.Through Bayesian network analysis,the impact probabilities of various influencing factors on unplanned outage events are clarified,which provides reference for energy big data analysis of power generation enterprises and will improve the operation reliability and safety of units.

Key words: unplanned shutdown, generator set, Bayesian network, causal inference, energy big data

中图分类号:

TK01

马栋梁, 陈辉, 朱延海, 蒋园. 发电机组非计划停机事件的贝叶斯网络分析[J]. 综合智慧能源, 2022, 44(3): 77-82.

MA Dongliang, CHEN Hui, ZHU Yanhai, JIANG Yuan. Bayesian network analysis of unplanned shutdown of generating units[J]. Integrated Intelligent Energy, 2022, 44(3): 77-82.

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因素	变量	发生次数	取值	百分比/%
外部因素	外部环境恶劣	115	1	22.12
	基建安装工艺差	139	1	26.73
	结构设计不合理	95	1	18.27
	煤质变化	24	1	4.62
人为因素	管理不到位	386	1	74.23
	检修不当	407	1	78.27
	运行操作不当	185	1	35.58
	老化评估不足	76	1	14.62
	风险评估不足	330	1	63.46
设备因素	产品质量差	300	1	57.69
	冗余设备失效	48	1	9.23
	设备故障	410	1	78.85
	参数异常	183	1	35.19
逻辑因素	保护逻辑不完善	96	1	18.46
	自动调节不完善	39	1	7.50
	报警不完善	77	1	14.81
	保护信号误发	81	1	15.58

发电机组非计划停机事件的贝叶斯网络分析

Bayesian network analysis of unplanned shutdown of generating units

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