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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During the entire period of transformer's service, the internal parts get ageing gradually. But the aging degree of each part cannot be observed directly. To correctly master the condition of the transformer contributes to the prediction of the risk and reliability of the transformer, and also, this is the foundation of making effective repairing strategies or replacement, ensuring the safe and reliable operation of transformers. However, the exact assessment result has not been acquired according to traditional assessments that based on only one of the state parameter. A new assessment model based on the multi-feature factors is proposed to overcome the disadvantage of traditional condition assessments. Firstly, in order to find out the rule that the characteristic of transformer changes, the factors that influence the state of the transformer health are researched with the method of the correlation analysis of Mathematical Statistics. These factors include DGA, breakdown voltage, dielectric loss, micro-water content, acid value, furfural content and so on. Secondly, determine the state information of the power transformer; establish the index system for health evaluation. Combining with qualitative analysis, the analytic hierarchy process method is adopted to determine the weight of each index, and the health status evaluation model of power transformer is established. As for a transformer that is in operation, as long as the corresponding experimental data is got, the health condition of the transformer can be obtained with the help of the health status evaluation model. Take transformers of a substation for instance. Using the proposed model, the corresponding experimental data is analyzed. The investigations results show that the new health status evaluation mode is effective.the aging process of the transformer, and the load of the transformer and the environment conditions are closely related. Factors that account for the transformer insulation aging include over-current, long time of high-load operation, overheating, moisture, oxidation, etc. Heat and moisture are the most important factors for liquid insulation aging, whereas oxidation can seriously accelerate the degradation of solid insulation. If the stage of insulation life can be de obtained effectively by means of the necessary monitoring and testing in the process of transformer operation, the service life of transformer can be maximized under the premise of reliability. This can not only guarantee the safe and stable operation of power system, but also reduce the operation costs. Evaluate the status of the transformer insulation correctly, predict the risk and reliability of its operation and then make effective economic management of maintenance and replacement strategy. This is the imperative trend of power industry development.: Associate Professor of Electrical Engineering, Wuhan University, Chioa. Research field is electrical equipment, agiog mechanism, fault diagnosis, condition assessment and maiotenance strategy. Mailbox: songbin72@163.com

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

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