基于贝叶斯网络的多状态变压器可靠性跟踪分析

余文辉; 王沾; 曾祥君; 刘楚

引用本文:	余文辉,王沾,曾祥君,等.基于贝叶斯网络的多状态变压器可靠性跟踪分析[J].电力系统保护与控制,2015,43(6):78-85.
	YU Wenhui,WANG Zhan,ZENG Xiangjun,et al.Reliability tracing analysis for multi-state power transformers using Bayesian network[J].Power System Protection and Control,2015,43(6):78-85

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基于贝叶斯网络的多状态变压器可靠性跟踪分析
余文辉¹, 王沾^2,3, 曾祥君², 刘楚⁴
1.中国南方电网公司，广东广州 510623;2.智能电网运行与控制湖南省重点实验室(长沙理工大学)，湖南长沙 410004;3.广西电网公司崇左供电局，广西崇左 532200;4.北京华电云通电力技术有限公司，北京 100071

摘要:

大型电力变压器是输变电系统的关键设备，其可靠性直接关系到电网的安全稳定运行。为了提高变压器可靠性跟踪分析的效率，提出一种基于贝叶斯网络的多状态变压器可靠性跟踪分析方法。给出了适用于多状态变压器的不可靠性指标跟踪算法。根据相关参考文献、专家经验及数据搜集，首次从部件的角度构建了电力变压器贝叶斯网络模型。结合可靠性指标统计，对变压器可靠性进行了跟踪分析，确定了影响变压器可靠性的关键部件，辨识了变压器薄弱环节。通过运用贝叶斯网络对变压器可靠性进行跟踪分析，为变压器的状态维修及全寿命周期管理提供了理论和数据支持。

关键词: 变压器可靠性分析贝叶斯网络状态维修全寿命管理

DOI：10.7667/j.issn.1674-3415.2015.06.013

分类号:

基金项目:国家自然科学基金项目(61233008，51277014, 51207014);湖南省科技重大专项(2012FJ1003);湖南省高校产业化培育项目(12CY007)

Reliability tracing analysis for multi-state power transformers using Bayesian network

YU Wenhui¹, WANG Zhan^2,3, ZENG Xiangjun², LIU Chu⁴

1.China Southern Power Grid Co., Ltd., Guangzhou 510623, China;2.Hunan Province Key Laboratory of Smart Grids Operation and Control (Changsha University of Science and Technology), Changsha 410004, China;3.Guangxi Power Grid Corporation Chongzuo Power Supply Bureau, Chongzuo 532200, China;4.Beijing Huadian Yuntong Power Technical Co., Ltd., Beijing 100071, China

Abstract:

Large power transformer is the key equipment of power transmission systems, and its reliability is directly related to the safety and stability of power grids operation. In order to improve the efficiency of the transformer reliability analysis, a reliability tracing analysis method for multi-state power transformers using Bayesian Network is proposed. The unreliability tracking algorithm for multi-state transformer is proposed. According to the relevant references, expert experience and data collection, the components based power transformer Bayesian network model is built. Combined with the reliability index statistics, the key components impacting reliability and the weak parts of transformer are recognized by the proposed technique. Through the use of Bayesian network to track the transformer reliability analysis, the data to support the condition based maintenance and all-life management for the transformer is provided.

Key words: transformer reliability analysis Bayesian networks condition based maintenance all-life management

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