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...

The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.

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The design of external insulation for transmission lines is usually based on real-type experiments. With the increasing voltage level of the transmission lines, in order to better design the experiments and reduce the experiment workload in the future, this paper studies the switching impulse discharge characteristics of air gaps of an ultrahigh-voltage (UHV) transmission line. A long air gap switching impulse breakdown voltage prediction method based on the leader discharge mechanism is proposed in this paper, and a continuous leader inception model is fitted for the air gaps in UHV transmission lines by calculating the factor R of the conductor-plane gaps. The method and the model can effectively predict the air gap breakdown voltage of transmission lines. The influence of tower width and conductor structure on air gap breakdown characteristics of UHV transmission lines is studied by the method and the model proposed this paper. The results show that, as the width of the tower increases, the breakdown voltage decreases, and as the gap length increases, the influence of the tower width on the breakdown voltage decreases. The conductor structure has no obvious influence on the discharge characteristics of transmission line air gaps.

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