【摘要】 摘要:提出了一种基于最小二乘加权融合集成神经网络的变压器故障识别新方法。首先对色谱分析法检测到的特征气体含量进行数值预处理,提取出故障识别所需的6个特征量,再应用5个不同结构的BP子网络分别进行识别,接着运用最小二乘加权融合算法对各个子网络的识别结果进行信息融合,最后根据融合结果来识别故障。与单个神经网络识别方法相比,该最小二乘加权融合集成神经网络可在故障特征比较类似的情况下,正确识别故障类型,且该方法的识别结果具有更大的安全间隔空间、可靠性更高。测试结果也表明了这些特征。更多还原

【Abstract】 A new method to diagnose power transformer faults based on the combination of neural network with least square weighted fusion algorithm is presented. Firstly, the numerical preprocessing to the contents of five characteristic gases obtained by chromatography is performed and the six characteristic quantities which are necessary to fault diagnosis are abstracted, and five back-propagation(BP) artificial neural networks(ANN) with different structures are applied to identify respectively, then the information fusion of the identifiedresults from the subnetworks is carried out by use of leastsquare weighted fusion algorithm. Finally, according to thesituation of the fusion the fault is diagnosed. Compared with the diagnosis method based on single neural network, the presented method can correctly determine the type of the fault under the similar conditions, the obtained diagnosis resultpossesses wider safe interval and is more reliable. The testing results of the presented method prove above mentionedadvantages of this method.更多还原