基于LSSVM概率输出与证据理论融合的变压器故障诊断

为了利用相对较少的故障数据样本对变压器主要故障类型进行较准确的判断，基于智能互补和数据融合的思想，提出基于最小二乘支持向量机LSSVM( least square support vector machine)概率输出与证据理论融合的故障诊断方法。该诊断方法具有以下特点：可融合蕴含变压器运行状态的多种特征信息，输出变压器各种故障的概率，为变压器检修提供更多的可用信息；充分发挥了LSSVM在小样本情况下具有较强泛化能力的优势。算例结果表明，该诊断方法的故障诊断准确率达到91.1%，优于传统的IEC三比值法(故障诊断准确率75.6%)及LSSVM分类法(故障诊断准确率82.2%)，有效降低了诊断误判的风险。

Transformer fault diagnosis based on probability output of LSSVM and DS evidence theory

For accurate estimation of the main types of transformer faults with relatively fewer fault information samples, this paper presents an approach to transformer fault diagnosis based on the probability output of the least squares support vector machine(LSSVM)and DS evidence theory according to the ideas of intelligence complementarity and information fusion. This diagnosis method has the following features: it integrates multiple feature information of the operating state of the power transformer, outputs the probabilities of various transformer faults, and provides more available information for the maintenance and repair of the power transformer. This gives full play to the strong generalization ability of the LSSVM in the case of small samples. In case studies, the diagnosis accuracy of the proposed method reached 91. 1%, which was higher than that of the three-ratio method(with an accuracy of 75. 6%)and that of the LSSVM method(with an accuracy of 82. 2%). The proposed method effectively reduces the risk of misdiagnosis of transformer faults.