虚拟现实变电站主变压器低压侧故障诊断模型

虚拟现实变电站主变压器低压侧故障诊断模型
李军锋;王钦若;熊山;温满华;黄成云
【期刊名称】《科学技术与工程》
【年(卷),期】2018(018)014
【摘要】传统主变压器低压侧故障诊断模型需要大量故障特征数据;且故障特征数据不相互独立,很难找到最合适的特征实现故障诊断.为此,提出一种新的虚拟现实变电站主变压器低压侧故障诊断模型.介绍了虚拟现实变电站主变压器低压侧典型故障,主要包括漏电故障和短路故障,对这两种故障进行分析,发现出现故障时电压电阻变化特征,为建立故障诊断模型提供依据.依据贝叶斯定理,利用某对象的先验概率,通过贝叶斯公式求出其后验概率,构建单层贝叶斯分类器.在此基础上,利用径向基神经网络将故障类变量看作属性变量的父节点,构建故障特征相互独立的虚拟现实变电站主变压器低压侧故障诊断模型.实验结果表明,构建模型能够准确诊断低压侧故障,具有很高的应用性.%The fault diagnosis model for low voltage side of traditional main transformer requires a large number of fault feature data , and the fault feature data are not independent .Thus, it is difficult to find the most suitable feature to realize fault diagnosis .To this end , a new fault diagnosis model for the low voltage side of the main trans-former in virtual reality substation is proposed .The typical low voltage side faults of main transformer in virtual real-ity substation are introduced , including leakage fault and short circuit fault .The two kinds of faults are analyzed , and the characteristics of voltage and resistance change when fault occurs are found , which provides basis for estab-lishing fault diagnosis
model .According to the Bias theorem , the posteriori probability is found and a single Bias classifier is constructed by using the prior probabilities of a certain object and the Bias formula .On this basis , the radial basis function neural network is used to consider the fault class variable as the parent node of the attribute variable , and the fault diagnosis model for the low voltage side of the main transformer in the virtual reality substa -tion with independent fault characteristics is constructed .The experimental results show that the model can diagnose the low voltage side fault accurately , and it has high applicability .
【总页数】6页(P147-152)
【作者】李军锋;王钦若;熊山;温满华;黄成云
【作者单位】广东电网有限责任公司教育培训评价中心,广州510006;广东工业大学自动化学院,广州510006;广东工业大学自动化学院,广州510006;广东电网有限责任公司教育培训评价中心,广州510006;广东电网有限责任公司教育培训评价中心,广州510006;广东电网有限责任公司教育培训评价中心,广州510006
【正文语种】中文
【中图分类】TM411
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