摘要
为了提高电力变压器故障诊断的正确率,提出了一种基于BP网络算法优化模糊Petri网的电力变压器故障诊断方法。利用具有自学习、自适应能力的BP网络算法,在确定模糊Petri网的权值、阈值、可信度等网络参数初始值的前提下,实现模糊Petri网网络参数的优化。在模糊Petri网网络结构上,运用BP网络算法,对电力变压器DGA样本进行学习训练,使模糊Petri网网络参数逐步向真实值逼近。实例分析结果表明,该方法能够有效地诊断电力变压器中的单一故障和多重故障,提高故障诊断正确率,证明了方法的正确性和有效性。
In order to improve the accuracy of fault diagnosis of power transformers, this paper presents a fault diagnostic method based on the back propagation algorithm evolving fuzzy Petri nets. Based on BP network algorithm, the optimization of FPN parameters can be determined when the initial value of weight of FPN on the arcs, threshold value and credibility is given. Using BP network algorithm with its capacity of self-learning and self-adaption, on FPN structure, the training is carried out on the sample data of DGA. Throughout the process, the value of FPN parameter approaches the real value. The results of numerical examples show that the algorithm proposed has good classifying capability of both single fault and multiple fault samples. At the same time, the correctness and effectivity of the proposed method are verified.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2015年第3期113-117,共5页
Power System Protection and Control
基金
山东省自然科学基金资助项目(ZR2012EEM021)~~
关键词
变压器
故障诊断
油中溶解气体分析
BP网络
模糊PETRI网
power transformer
fault diagnosis
dissolved gas-in-oil analysis（DGA）
back propagation
fuzzy Petri nets
