信息融合技术在变压器油气识别故障诊断中的研究

Research of the Information Fusion Technology in the Transformer Fault Diagnosis with Dissolved Gas-in-Oil Analysis

变压器故障分为放电性故障和过热性故障两大类别,它们均会在变压器油中有所反映。本文通过对变压器油中主要气体的分析,判断变压器的故障类型。具体方法是:利用改进算法的BP网络和信息融合技术,以变压器油中五种主要特征气体作为神经网络的输入,以六种变压器状态作为相应的输出,通过加入动量因子,可以提高学习率系数,充分发挥改进算法的BP网络具有自适应学习能力的优势。仿真测试结果表明,本方法能够在较大范围内准确有效地进行变压器的故障诊断。

Transformer faults can generally be divided to two types： the discharge fault and the thermal fault. Both of these two faults could cause specific effect on the transformer oil. The fault types can be determined by analyzing the gas content in the transformer oil in this paper. A new method is presented for transformer fault diagnosis, which includes an improved BP algorithm and information fusion. Five characteristic gases dissolved in the transformer oil are applied as the inputs of the neural network, and six states of the transformer as the outputs. With the momentum factor added, the learning rate coefficient of the neutral network is enhanced. The improved BP algorithm features the advantage of adaptive learning. The results of simulation indicate that the new method of transformer fault diagnosis is reliable and effective.