【作者】 赵文清；

【导师】 朱永利；

【作者基本信息】 华北电力大学（河北） ， 电力系统及其自动化， 2009， 博士

【摘要】 变压器是电力系统中最重要的电气设备之一,其运行状态直接影响系统的安全运行水平,变压器一旦发生事故,造成的直接和间接的经济损失是巨大的。变压器故障诊断和故障预测是保证其正常运行并进行状态维修的基础。本文就变压器数据仓库的构建、变压器综合故障诊断、变压器故障预测方法进行了深入的研究。为了进行变压器状态和故障自动预测,以及便于维护人员对于变压器状态进行人工预测,提出了构建电力变压器数据仓库的数据处理思想,在对有关变压器信息的多种数据源进行分析和重新组织的基础上,利用联机分析处理和数据挖掘技术,可对变压器故障诊断和早期故障预测起到积极作用。针对单一诊断模型受变压器试验数据量和色谱数据随机因素影响较大的缺点,提出了以多个贝叶斯网络分类器作为故障诊断模型群和利用支持向量机进行集成的变压器故障组合诊断方法,该方法可解决支持向量机进行多分类问题时训练效率低、求解精度差的问题。针对已有变压器故障诊断方法在信息缺失多、有偏差的情况下误判率高的问题,提出了利用选择性贝叶斯分类器SRBC直接从不完整的变压器试验数据估算变量的概率分,进而估算其互信息的方法,该方法可解决已有变压器故障诊断方法在信息缺失多、有偏差的情况下误判率高的问题。针对单一预测模型的适应性较差的缺点,提出了一个基于支持向量机的变压器故障组合预测模型及其求解步骤。在预测过程中,首先利用多个单一预测方法构成预测模型群,对原始油中溶解气体数据进行拟合。然后,把预测模型群的拟合结果作为支持向量机回归模型的输入进行二次预测,形成变权重的组合预测。采用.NET平台和C/S结构,利用VC#语言和SQL Server数据库开发了变压器故障诊断系统。该系统主要包括变压器油色谱预测、变压器故障诊断以及数据仓库OLAP分析等几个模块。该系统已在河北衡水电力公司投入运行。更多还原

【Abstract】 Power transformer is one of the expenxive device in power system, power transformer fault diagnose is vital to make the whole power system run normally, the failures of transformers can result in serious issues, such as service disruptions and severe economic losses.Transformer fault diagnose and fault prediction are the basis of the transformer condition based maintenance. In this paper we have done in-depth research in transformer data warehouse construction, transformer fault diagnosis and fault forecast.A data precessing method is provided through constructing the power transformer fault information data warehouse, based on analyzing and reorganizing the various existing data sources, the transformer failure diagnosis and early failure predication can be benefit from OLAP (On-Line Analysis Processing) and data mining technology.Aiming at the low diagnositic performance of single models caused by transformer testing data, a combination diagnosis model based on the Bayes classifiers and SVM is proposed in this paper. This model can solve the low efficientcy and accuracy when using support vector machines (SVM) to solve multi-classification question.Due to the randomness and uncertainty of power transformer fault diagnosis data, a novel method using selective Bayes classifier SRBC is proposed to estimate probability distribution directly from incomplete transformer fault data, and then estimates mutually information. This proposed. SRBC approach can solve the randomness and uncertainty problem of power transformer fault diagnosis data. The experimental results show that this method can obviously enhance the accuracy rate.Aiming at the low forecasting accuracy of traditional predictive approaches, a combinational model is proposed on the basis of SVM theory in this paper. During the process of the forecast, firstly several single forecast approaches are used to form a model group, and a set of data in time sequence on each dissolved gas are fitted by the model group. And then the fitted results by different traditional predictive models in time sequence act as the input of the support vector machine regression (SVMR) model, and the changeable weights combinational SVMR prediction model is obtained.Adopting .NET platform and the C/S structure, the transformer fault diagnosis system has been developed using the VC# language and SQL Server. This system mainly includes the transformer oil chromatograph forecast, the transformer fault diagnosis as well as the data warehouse OLAP analysis and so on several modules. This system has been put the operation in the Hebei Hengshui electric power company.更多还原