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首页> 外文期刊>Electric power systems research >Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm
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Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm

机译：基于遗传算法的支持向量机预测电力变压器油中溶解气体含量。

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Forecasting of dissolved gases content in power transformer oil is very significant to detect incipient failures of transformer early and ensure hassle free operation of entire power system. Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, SVM has rarely been applied to forecast dissolved gases content in power transformer oil. In this study, support vector machine with genetic algorithm (SVMG) is proposed to forecast dissolved gases content in power transformer oil, among which genetic algorithm (GA) is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed SVMG model. The experimental results indicate that the proposed SVMG model can achieve greater forecasting accuracy than grey model (GM) under the circumstances of small sample. Consequently, the SVMG model is a proper alternative for forecasting dissolved gases content in power transformer oil.
机译：预测电力变压器油中的溶解气体含量对于及早发现变压器的早期故障并确保整个电力系统的无忧运行非常重要。由于其非线性和少量训练数据，预测电力变压器油中的溶解气体含量是一个复杂的问题。支持向量机（SVM）已成功用于解决非线性和小样本的回归问题。但是，SVM很少用于预测电力变压器油中的溶解气体含量。本文提出了一种基于遗传算法的支持向量机（SVMG）来预测电力变压器油中的溶解气体含量，其中遗传算法（GA）被用于确定支持向量机的自由参数。来自中国多家电力公司的实验数据用于说明所提出的SVMG模型的性能。实验结果表明，在小样本情况下，提出的SVMG模型比灰色模型（GM）具有更高的预测精度。因此，SVMG模型是预测电力变压器油中溶解气体含量的合适替代方法。

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