# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
## 1 Introduction  
The health of power transformers is very important for the stable operation of power grid. There are a large number of transformers in service, most of which have been put into use for a certain period of time, and there will be internal faults in the long-term aging process. It is of great significance to diagnose all kinds of latent faults in transformers accurately for the stable and normal operation of transformers. Dissolved gas analysis (DGA) is widely used in online diagnosis of oil-immersed transformers because it uses non-electric quantity as a reference and is not affected by electromagnetism [1, 2]. The diagnosis process of DGA is generally divided into the extraction of transformer oil samples, the stripping of dissolved gas in oil, the measurement of gas components and the determination of fault category. The determination of fault category is the core process of DGA diagnosis. The central idea is to determine the fault category by the content of gas component in oil [3]. Fault diagnosis technology can be divided into traditional chart query methods and modern intelligent algorithm identification methods [4, 5]. Traditional chart query methods include the three-ratio method [6, 7], the Duval triangle method [8] and the Pentagon method [9]. These methods are very convenient to use, and do not need programming and complex calculation. When the ratio of gas components is calculated to draw a graph, you can look up the table or the graph to find the corresponding fault category, but there are problems of low accuracy and judgments that are too absolute [10, 11]. An intelligent algorithm is a kind of diagnosis method of patternrecognition by a computer. The fault recognition algorithm mainly includes classification and clustering algorithms [12; 13; 14]. The classification algorithms include support vector machine [15], decision tree and neural network [16], etc. These methods have achieved good results in transformer fault diagnosis. Clustering algorithms like the k-means and [17] fuzzy clustering algorithm [18; 19] can accurately cluster the transformer fault data and identify the type of transformer fault. However, there are some shortcomings, such as the need to determine the number of clusters, difficulty building a reasonable membership function, being easily affected by some deviation points, and having a more complex calculation process [20]. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is a density based clustering algorithm [21; 22], the algorithm does not need to determine the number of clusters in advance, can divide the high-density point area into clusters, and effectively filter out the low-density point area. It can realize clustering of any shape in the noisy dataset, and it is widely used in detection and diagnosis. [PERSON] [23] employed DBSCAN to detect textural damage, [PERSON] [24] accomplished thermal runaway diagnosis of battery system for electric vehicles by DBSCAN, and [PERSON] [25] made the combination of a DBSCAN and symmetrized dot pattern to complete fault diagnosis of rolling bearing. For transformer fault diagnosis, the density difference of each fault category in the Euclidean distance space of DGA data is not obvious, and direct application of DBSCAN to DGA diagnosis is not effective. In the practical application of DBSCAN in DGA data processing, due to the variety of transformer faults and the vagueness of their Euclidean distance distinction, the classification results are sensitive to the clustering data, and the accuracy of fault identification is low, making the clustering effect unsatisfying, and difficult to get the data classification in line with the engineering practice.