A Fault Diagnosis System of Power Transformers Using Acoustic Characteristics and Neural Network. Issue 3 (March 2020)

• Record Type: Journal Article
• Authors: Geng, Mingxin; Fan, Chuang; Wang, Kun; Zhang, Xiao; Yang, Zhijun
• Is Part Of: IOP conference series. Volume 782:Issue 3(2020)
• Publication Date: 2020-03
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/782/3/032098
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: As one of the most important facilities in the power system, power transformers undertake the crucial tasks of voltage transformation, power distribution and transmission. In the process of operation, the power transformers may have discharges, overheating, insulation degradation, winding and core loosing, solid pollution of insulation oil and some other faults. In order to address the aforementioned issues, a novel fault diagnosis system for power transformer is proposed. Through using the acoustic characteristics of the power transformer and establishing the model of the neural network, the proposed system is demonstrated with high accuracy in the experiment.