基于SVM的电力变压器内部故障部位的概率估计

目前变压器智能故障诊断大多是以油中溶解气体为特征对故障性质的诊断,缺乏对内部故障部位的分析及量化的诊断结果。针对上述问题,提出一种基于SVM的电力变压器内部故障部位的概率估计模型。该模型结合SVM与概率建模的优点,充分利用油中溶解气体和电气试验数据的互补信息,运用SVM后验概率理论,对变压器内部可能发生故障的部位进行概率估计,克服了标准SVM硬判决输出的缺陷,以概率的形式给出诊断结论。通过实例分析表明,该模型不仅故障识别率较高,还具有良好的概率分布形态,具有较好的实用性和推广性。

Probability estimation of interior fault position for power transformer based on SVM

At present, most intelligent fault diagnostic methods of power transformer are based on dissolved gas analysis in oil to make diagnosis on fault property, which lacks of a quantitative diagnosis on inner fault position. To solve the problem, a novel probability estimation model of interior fault position for power transformer based on support vector machine (SVM) is proposed. The model takes advantages of SVM and probability modeling to make probability estimation on interior possible fault position of power transformer by fully utilizing dissolved gas analysis and routine electrical testing data. It overcomes drawbacks in hard-decision outputs of the traditional support vector machine and gives a probabilistic conclusion by using posterior probability support vector machine. Finally, fault diagnosis examples are used to illustrate the performance of the proposed model. The diagnostic results show that the proposed model has high recognition rate and better probability distribution, which proves its effectiveness and usefulness.