基于双空间特征提取的变压器故障诊断模型

为了提高基于油中溶解气体分析(dissolved gas analysis, DGA)的变压器故障诊断正确率，弥补单子空间特征提取的局限性，提出了基于双子空间特征提取的变压器故障分层诊断模型.首先，将DGA测试样本在一个子空间内进行特征提取后，为避免核函数及其参数的选择难题，以及利用多核支持向量机(multiple-kernel support vector machine, MKSVM)鲁棒性强和精度高的特点，采用MKSVM作为分类器对测试样本进行预测.依据预测结果将测试样本分为难分类和易分类样本，对易分类样本直接进行分类识别；对难分类样本则将该样本再次投影到另一子空间进行特征提取后，同样采用MKSVM作为分类器对难分类样本进行预测，综合两次预测结果进行分类识别，实现两分类MKSVM的双子空间特征提取算法.最后，根据故障特征，建立基于双子空间特征提取算法的变压器故障分层诊断模型.诊断实例表明，该模型具有较高的诊断正确率和推广能力.

Transformer Fault Diagnosis Model Based on Dual-space Feature Extraction Algorithm

In order to enhance transformer fault classification accuracy based on dissolved gas analysis (DGA) and overcome the limitations of single subspace, a new transformer fault multilayer diagnosis model based on dual-space feature extraction algorithm was proposed. Firstly, a DGA test sample was projected to a subspace to realize feature extraction in order to reduce the dependence of the modeling accuracy on kernel function and parameters and take advantage of stronger robustness and higher precision. Multiple-kernel support vector machine (MKSVM) was used as the classifier to predict the class label. The test sample was classified into difficult class one or easy class one according to the predicted result, and the class label of the easy one was identified in the subspace directly. As to the difficult one, the test sample was re-projected to another subspace where multiple-kernel support vector machine was used to predict. The class label of the difficult was identified to integrate two predicted results. Therefore, MKSVM of two class problem based on dual-space feature extraction algorithm was achieved. Finally, a multilayer diagnosis model was established according to the fault characteristic of transformer. The diagnosis experiment has shown that the model has a higher diagnosis rate, which proves its effectiveness and usefulness.