变压器油中故障特征气体体积分数的预测

变压器是电力系统的重要设备,变压器油中溶解的故障特征气体体积分数是其进行绝缘故障诊断的重要依据。变压器油中气体体积分数的预测是周期性测试的重要补充。应用灰色多变量模型,对变压器油中溶解的5种主要特征气体,即氢气、甲烷、乙烷、乙烯和乙炔建立了MGM(1,5)模型,充分考虑了各种气体之间的相互影响,克服了常用预测方法在预测时只考虑某个特征参数或单独考虑几个特征参数发展变化的不足。通过预测实例分析,将MGM(1,5)模型的计算结果与相应GM(1,1)模型的计算结果比较,验证了该模型的准确性和有效性。

Concentration prediction of fault characteristic gases in power transformer oil

Power transformer is an essential component in the power systems. The concentration of fault characteristic gases dissolved in transformer oil is essential to the insulation fault diagnosis. The concentration prediction of the gases is an important supplement for periodical testing. A MGM(1, 5) model using multivariable grey theory for the five characteristic gases dissolved in transformer oil, i.e. hydrogen, methane, ethane, ethylene, acetylene, was constructed. In the built model the interaction among these gases was comprehensively considered and the disadvantage that only one index extracted from the signal or each index was dealt with separately was made up. An actual prediction case was analyzed and the results were compared with those obtained by GM(1,1) model. The comparison result indicates the validity and efficiency of the proposed model.