基于优化模糊Petri网的矿用变压器故障诊断

发布时间：2018-03-06 10:17

本文选题：矿用变压器　切入点：油浸式变压器　出处：《工矿自动化》2017年05期 　论文类型：期刊论文

【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。
[Abstract]:A fault diagnosis model of mine transformer based on optimized fuzzy Petri net is proposed for coal dust without explosion hazard. According to the relationship between fault symptom and fault, a fault diagnosis model of mine transformer based on optimized fuzzy Petri net is presented. The fault diagnosis model is established by using fuzzy generation rules, and the initial parameters of the model are optimized by using the self-learning and adaptive ability of the Elman network algorithm. The simulation results show that the setting of the initial parameters of the fuzzy Petri net is more reasonable. The accuracy of fault diagnosis of the optimized model and the unoptimized model are 87.88% and 75.76 respectively, which verify the validity of the optimized model.
【作者单位】：山东科技大学电气与自动化工程学院;日照市机电工程学校机电系;
【基金】：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)
【分类号】：TD611.3

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