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中国电机工程学报 2008, 28(25) 14-19 DOI: ISSN: 0258-8013 CN: 11-2107/TM

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	支持向量机
	变压器故障
	组合预测
	模型群
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	赵文清
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应用支持向量机的变压器故障组合预测

赵文清朱永利张小奇

华北电力大学计算机科学与技术学院华北电力大学计算机科学与技术学院西北电网有限公司调度通信中心

摘要：对变压器油中溶解气体进行预测有助于及时预测变压器的故障。提出一个基于支持向量机(support vector machine，SVM)的变压器故障组合预测模型及其求解步骤。在预测过程中，首先利用多个单一预测方法如线性模型、指数模型、乘幂模型、非等间隔灰色GM(1,1)模型和非等间隔灰色Verhulst模型构成预测模型群，对原始油中溶解气体数据进行拟合。然后，将预测模型群的拟合结果作为支持向量机回归模型的输入进行2次预测，形成变权重的组合预测。该文对基于SVM的组合预测过程和参数计算进行了详细地探讨。通过2个实例证明了该文提出的组合预测模型能较好地平衡拟合和外推，在某种程度上解决了传统方法拟合优而外推差的问题。此外，通过与多种预测方法进行比较，基于支持向量机的变压器故障组合预测模型的预测精度明显优于单一预测模型和其它的组合预测模型。

关键词：支持向量机变压器故障组合预测模型群

Combinational Forecast for Transformer Faults Based on Support Vector Machine

ZHAO Wen-qing ZHU Yong-li ZHANG Xiao-qi

Abstract: The forecast to dissolved gases in a transformer is useful to the forecast of the transformer's faults. A combinational model and relative solving steps for power transformer fault forecast are proposed on the basis of support vector machine (SVM) theory. During the process of the forecast, firstly several single forecast approaches, such as linear model, exponent model, exponentiation model, non-equal-gap grey predictive model and non-equal-gap grey verhulst predictive model, are used to form a model group, and a set of data in time sequence on each dissolved gas are fitted by the model group. Secondly, the fitted results by different traditional predictive models in time sequence act as the input of the support vector machine regression (SVMR) model, and the changeable weights of the input quantities, which are the important part of the combinational SVMR prediction model, can be obtained by relative SVMR approach based on known input and output samples. In the paper, the procedure of the combinational prediction on transformer faults based on SVMR is discussed in details. Two examples on dissolved gas data of transformers in time sequence have proven that the proposed model can give good results on both the fitting to the known data in time sequence and the extrapolation to the data to be predicted. Therefore, the problem of good fitting and bad extrapolation in traditional predictive approaches is solved to some extent. Moreover, compared with other predictive approaches, both single model and other combinational model, the proposed combinational forecast model has higher forecast accuracy.

Keywords: support vector machine transformer fault combinational forecast model group

收稿日期 2007-05-08 修回日期 1900-01-01 网络版发布日期

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通讯作者: 赵文清

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作者Email: jbzwq@126.com

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