Health status diagnosis of distribution transformers based on big data mining

In the fault detection technology of distribution transformer, the traditional artificial intelligence algorithm has been unable to achieve its efficient analysis and processing, but also unable to achieve the elimination of misdiagnosis caused by improper interval segmentation in the process of fault diagnosis of distribution transformer. In a word, the problem that always exists in transformer fault diagnosis technology is the problem of discretization of fault data. In order to solve this problem, this paper creatively proposes a health condition diagnosis method of distribution transformer based on large data. This method innovatively proposes that the dissolved gas analysis value of distribution transformer is the conditional attribute, and the fault type is the decision attribute, and the fault decision table is established. The continuous attribute data in the decision table are discretized by using the optimization behavior of large data sets. Subsequently, the discretized decision table is simplified by using the big data theory, and the decision table of fault diagnosis rules is established, which greatly simplifies the difficulty of attribute simplification of decision table and makes diagnosis more convenient. Finally, an example shows that the proposed method can effectively discretize and reduce samples. Compared with traditional methods, it improves the accuracy of fault diagnosis. This paper proposes a health condition diagnosis method of distribution transformer based on large data. This method innovatively proposes that the dissolved gas analysis value of distribution transformer is the conditional attribute, and the fault type is the decision attribute, and the fault decision table is established. An example shows that the proposed method can effectively discretize and reduce samples. Compared with traditional methods, it improves the accuracy of fault diagnosis.