Research on Transformer Fault Diagnosis Method Based on Deep Learning Algorithm Optimization

Abstract

As power systems enter the intelligent age, transformers, the essential devices in power systems, are also receiving significant attention for fault diagnosis problems. Focusing on transformer fault diagnosis based on oil chromatographic analysis, we need to propose an efficient proposition for fault type identification that satisfies the operating state of the transformer. Based on the dynamic development of the deep learning algorithm, the theoretical framework of the transformer fault diagnosis system is built according to the internal logic of the network model and the protocol specification, which can describe the transformer diagnosis method. This method comprises an error diagnosis and prediction engine in which data collection and preprocessing, as well as optimization of deep learning algorithms, are involved. In addition, the researchers used the experimental results in the transformer fault diagnosis system to explore the possibility of upgrading to the efficient diagnosis of faults from the perspective of analysis and practice. The task of a transformer fault diagnosis system is to provide standard fault type judgment for the power systems and to improve the accuracy of fault diagnosis and fault prediction. Hence, people take measures such as strengthening the control of deep learning algorithms based on model structure and training and parameter optimization and constructing data acquisition and preprocessing to optimize interaction and feedback between quality perception and deep learning algorithms. In addition, the system of evaluation index and method is established to realize the efficient development of transformer fault diagnosis system and the intelligence of power system and maintain the operation state of transformer.