基于云物元分析原理的电力变压器故障诊断方法研究

Study on Fault Diagnosis for Power Transformer Based on Cloud Matter Element Analysis Principle and DGA

变压器油中溶解气体分析(DGA)是电力变压器故障诊断的重要方法。针对物元理论变压器故障诊断方法中,在建立故障模式物元模型时没有考虑边界值的不确定性的不足,首次在变压器故障诊断研究方面引入云模型,结合云模型的不确定推理特性以及物元理论能同时进行定性定量分析问题的优点,提出了一种基于云物元分析原理和DGA相结合的电力变压器故障诊断新方法。通过建立变压器故障诊断的云物元模型和计算特征云物元与标准云物元之间的关联函数,实现对变压器故障模式的有效识别。实例分析验证了方法的正确性和有效性。

Dissolved gas-in-oil analysis （DGA） is an important method to find the hidden or incipient insulation faults of oil-immersed power transformer. Matter element theory was employed to research the fault diagnosis of transformer with qualitative and quantity advantages. However, the method did not consider the uncertain essence of the fault diagnosis of transformer. And in the fact, there were two uncertain characteristic in it, random and fuzzy. Hence a new fault diagnosis method is presented in this paper. The method has two advantages of considering two uncertain characteristic and realizing fault diagnosis qualitatively and quantitatively based on cloud model and matter element theory. By building the cloud matter element models of transformer fault diagnosis and calculating the correlation function of feature matter element models and standard ones, fault modes of transformer are identified effectively. Then, the results of examples research indicate the method is effective.