基于优化模糊Petri网的矿用变压器故障诊断

基金项目:

中国博士后科学基金资助项目(2015T80729)

青岛市博士后研究人员应用研究项目(2015190)

详细信息

Fault diagnosis of mine-used transformer based on optimized fuzzy Petri net

1.
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2.
Electromechanical Department, Rizhao Electrical and Mechanical Engineering S

摘要: 针对用于矿井中有煤尘而无爆炸危险的地方、以油浸式为主的变压器，提出了一种基于优化模糊Petri网的矿用变压器故障诊断模型。根据故障征兆与故障之间的关系，利用模糊产生规则来建立故障诊断模型；利用Elman网络算法的自学习和自适应能力对模型初始参数进行优化处理，使模糊Petri网初始参数值的设置更加合理。Matlab仿真结果表明，优化模型和未优化模型的故障诊断准确率分别为87.88%和75.76%，验证了优化模型的有效性。
- 矿用变压器 /
- 油浸式变压器 /
- 故障诊断 /
- 模糊Petri网 /
- Elman网络
Abstract: For oil-immer transformer used in places with coal dust and no explosion hazard,an improved fault diagnosis model of mine-used transformer based on fuzzy Petri net was proposed. Fuzzy generation rule was used to establish fault diagnosis model according to relationship between fault symptom and the fault. Self-learning and adaptive ability of Elman network algorithm are used to optimize initial parameters of the model, and the settings of initial parameters of the fuzzy Petri net are more reasonable. Matlab simulation results show that fault diagnosis accuracy of the optimized model and unoptimized model is 87.88% and 75.76% respectively, which verifies effectiveness of the optimized model.
- mine-used transformer /
- oil-immer transformer /
- fault diagnosis /
- fuzzy Petri net /
- Elman network

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