中文摘要：

多分类概率输出方法可用于变压器故障诊断,其分类效果较好并能提供概率信息。针对现有基于支持向量机（SVM）的诊断方法在特征不明显条件下有误分类的情况,提出了一种基于多分类概率输出的变压器故障诊断方法。此方法引入Sigmoid函数将SVM决策函数输出映射为二分类概率输出,然后综合多个二分类概率输出结果,求解一个凸二次规划问题实现多分类概率输出。此方法可以得到发生不同类型故障的可能性,即故障类别概率,进一步分析后给出诊断结论。算例分析表明,此方法在继承了SVM故障诊断方法优点的基础上,提供了概率信息,对现有SVM方法误诊断样本也能给出可能存在的故障,弥补了现有SVM方法在变压器故障特征不明显条件下的不足。

英文摘要：

The multi-classsified probability output method is capable of yielding satisfactory classification results and class probabilities for general transformer fault diagnosis. Based on this method, a new transformer fault diagnosing method is proposed which gives fault probabilities more comprehensive than those of the conventional methods by producing binary outputs. An additional sigmoid function is trained to transfer the traditional support vector machine （SVM） binary outputs 0 and 1 to probabilities of the incipient faults. With the results of binary classifiers, multi classified probabilities are determined by convex quadratic programming. By providing comprehensive fault probability information, not only can the proposed fault diagnosing method achieve high classification accuracy but also it will give rational results possibly misdiagnosed by other methods. For samples misclassified by traditional methods such as SVM and three-ratio method, the proposed method is able to correctly classify the fault type with the greatest probability, and remind the decision-makers of the real fault type hidden in two or three likely types.