Fault Diagnosis of Power Transformers With Membership Degree

Abstract

Power transformers are important equipment for power systems, and a dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. The conventional methods are prone to misinterpreting the gas data near the boundaries and the correct rate is low. Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. Based on clustering techniques, this paper proposes a new method for fault diagnosis of transformers with the DGA. A reference fault set is provided, and the fault diagnosis is implemented by calculating the membership of the DGA data to the reference fault set. Test with credible DGA dataset (201 field cases) shows that the correct rate of the new method is 89%, while the David triangle method is 79% and the IEC ratio method is 59%, which demonstrate the superiority of the proposed method to the conventional ones. The new method is simple and highly accurate, indicating a good application prospect in engineering practice. INDEX TERMS Power transformer, fuzzy clustering, fault diagnosis, membership degree. I. INTRODUCTION The oil-paper insulation system in power transformers operates under the effects of high temperature and strong electromagnetic environment, and the insulation medium can slowly decompose into a number of small molecules. The decomposition gases dissolved in oil are H 2 , CH 4 , C 2 H 6 , C 2 H 4 , C 2 H 2 , CO 2 , CO and N 2. However, when a fault occurs, the insulation breaks down more quickly and the decomposition products will be different according to the type and severity of the fault [1], [2]. Dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. This technique involves several steps, such as taking oil samples from a transformer, removing dissolved gases from oil, determining gas component content, and identifying fault types [3]. Fault identification is a decisive step in the internal fault state determination of the transformer in DGA analysis. Various computational and graphical methods employing gas ratios and proportions of gases dissolved in oil determined by gas chromatography have been worked out for recognizing the characteristic patterns of the dissolved gases that are associated with the main types of faults [4], [5]. These methods available to interpreted DGA data include The associate editor coordinating the review of this manuscript and approving it for publication was Chuan Li. Key Gas Method, Doernenburg Ratio Method, Rogers Ratio Method, IEC Ratio Method and Duval Triangle Method, and they have been developed and validated using large sets of data for equipment in service. In these methods, the multiple numeric thresholds and gas boundaries are commonly set to classify features of the dissolved gas data. However, these thresholds and boundaries do not physically exist, and the gas data near the ratio boundaries are prone to misinte...

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