Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

• Published in: Mathematical problems in engineering Vol. 2019; no. 2019; pp. 1 - 10
• Main Authors: Yan, Chun, Liu, Wei, Li, Meixuan
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2019; Hindawi; John Wiley & Sons, Inc
• Subjects: Accuracy; Acetylene; Algorithms; Artificial intelligence; Classification; Dissolved gases; Electricity distribution; Ethane; Fault detection; Fault diagnosis; Fuzzy logic; Gases; Genetic algorithms; Hypothesis testing; Machine learning; Macroeconomics; Methane; Methods; Model accuracy; Multimedia; Neural networks; Power; Principal components analysis; Science; Transformers
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2019/1019845

Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.