【作者】 尹金良；

【导师】 朱永利；

【作者基本信息】 华北电力大学 ， 电力系统及其自动化， 2013， 博士

【摘要】 基于DGA数据的电力变压器故障诊断方法能及时发现变压器潜在故障,可在变压器运行过程中进行故障分析,促进变压器从定期维修到状态维修的转变,提高变压器的运行维护水平,其研究具有重要的现实意义。本文在分析现有电力变压器故障诊断方法的特点及存在的问题的基础上,首次尝试将可有效解决小样本、高维、非线性分类问题的相关向量机应用于油浸式电力变压器故障诊断,探索基于DGA数据的电力变压器故障诊断的新方法。提出了基于相关向量机的油浸式电力变压器故障诊断方法。采用二叉树方法建立了故障诊断模型,分析了特征变量及核函数的选取对诊断性能的影响,给出了诊断方法的具体实现过程。该诊断方法可以以概率的形式输出诊断结果；具有较快的诊断速度,非常适用于在线诊断；可有效解决小故障样本数据情况下的故障诊断问题。实例分析验证了该方法的诊断性能。提出了基于M-RVM的变压器故障诊断方法。该诊断方法可以直接实现变压器多种状态的识别,输出变压器隶属于各种状态的概率,兼有RVM诊断方法的优点,同时克服了RVM方法因需将诊断转化为多个二分类,而造成的分类重叠和不可分类、需构建较多分类器以及误差累计等问题。实例验证了该诊断方法的有效性。研究了组合核学习及组合核核参数优化方法。在此基础上,提出了基于组合核相关向量机的电力变压器故障诊断方法,以实现多检／监测数据或单一检/监测数据提取的多特征信息的融合诊断,提高故障诊断正确率。基于DGA数据的故障诊断实例验证了该融合诊断方法的有效性。提出了基于贝叶斯风险理论的代价敏感相关向量机,并尝试将其应用于油浸式电力变压器故障诊断。该诊断方法将计及误诊代价差异的诊断思想引入电力变压器故障诊断,以损失代价最小为目标,以克服仅追求高诊断正确率不能完全反应实际诊断需要的问题。故障诊断实例分析表明,代价敏感相关向量机趋于提高高误诊代价类别的诊断正确率,诊断速度足以满足工程需求。更多还原

【Abstract】 Fault diagnosis of power transformer based on Dissolved Gas Analysis (DGA) is a sensitive potential failure detection technique, which can be carried out while transformer is running. This method is of great important practical significance, which can promote the realization of condition maintenance from original regular maintenance, and improve the operation and maintenance level. Based on the analysis of the characteristics and shortcomings of the existing diagnosis methods, the relevance vector machine (RVM), which can solve the small-sample, high-dimensional, and non-linear classification problems, was firstly applied to the fault diagnosis of oil-immersed power transformer in this paper. A new way for DGA-based fault diagnosis is explored.A RVM-based fault diagnosis model of oil-immersed power transformer was built by binary tree method. The affection of the feature variables and kernel functions on diagnostic performance was investigated, and the implementation procedure of fault diagnosis was provided in detail. The diagnosis model can provide probabilistic outputs, and is especially suitable for online diagnosis due to high diagnosis speed. It solved the diagnosis problem of lacking of sample data, and its diagnosis performances were validated by case studies.A fault diagnosis method for oil-immersed power transformer based on multiclass RVM was proposed. This diagnosis method can directly implement multi-state identification and provide the probability of each state. It takes the advantage of original RVM which decomposes fault diagnosis into multiple binary classifications, and overcomes the disadvantages of classification overlap, classification failure, multiple-classifier need and error accumulation. The accuracy of this diagnosis method was validated by real-world diagnosis cases.Combination kernel learning method, as well as kernel parameters optimization method was studied. On this basis, a fault diagnosis method of oil-immersed power transformer based on multi-kernel learning RVM was proposed. The method can integrate the feature information reflecting the operating state from different perspectives. DGA-based diagnosis cases verified the effectiveness of the integration method.Cost-sensitive RVM (CS-RVM) based on Bayesian risk theory was proposed and applied to fault diagnosis of oil-immersed power transformer. The CS-RVM based diagnosis method introduced the idea of considering misdiagnosis cost to fault diagnosis, aiming to minimize misdiagnosis cost. It could overcome the problem of bringing no meaningful results for only pursuing high classification accuracy. Experimental results showed that CS-RVM diagnosis method tended to increase the diagnostic accuracy of high misdiagnosis cost category, and diagnosis speed was high enough to meet the engineering requirement.更多还原