基于气体增长速率和Petri网的变压器内部故障状态推理

Reasoning on the internal fault state of transformer based on fuzzy Petri nets and gas growth rate

针对变压器故障诊断中的故障漏判、误判和某些故障无法判断的问题,将Petri网和油中气体增长速率相结合,提出一种基于模糊Petri网来构建变压器故障诊断模型,它通过分析油中溶解的气体浓度来判断变压器的故障类型。该方法首先利用通过气相色谱法得到各气体的浓度值,使用建立起来的Petri网模型,推理出每种故障类型的可能性大小。用这种方法能避免三比值编码的缺失等问题,也就能有效地处理变压器故障诊断中漏判、误判和无法判断的问题,并能定量地给出每种故障的可能性大小。实例进一步证明,研究模型提高了诊断的正确率。

In view of problems of the fault leakage judgment, misjudgment and some faults which can not be judged in transformer fault diagnosis, this paper presents a transformer fault diagnosis model based on fuzzy Petri nets. The transformer fault diagnosis model is used to judge the type of the transformer faults through the analysis of the oil dissolved gas concentration. The method firstly uses gas chromatography to get the concentration value of each gas, and then calculates the rate of the gas growth to judge whether the faults happen. If the fault happens, the probability of each type of failure is calculated based on the model of the Petri nets. The method can avoid the lack of coding of three ratios, so that it can solve effectively the problems of the transformer fault diagnosis, misdiagnosis and missed judgment, as well as can provide the probability of each failure in a quantitative way, thus it improves the diagnostic accuracy.