Transformer fault classification for diagnosis based on DGA and deep belief network

被引:11
作者
Zou, Dexu [1 ]
Li, Zixiong [2 ]
Quan, Hao [2 ]
Peng, Qingjun [1 ]
Wang, Shan [1 ]
Hong, Zhihu [1 ]
Dai, Weiju [1 ]
Zhou, Tao [2 ]
Yin, Jianhua [3 ]
机构
[1] China Southern Power Grid Yunnan Power Grid Co Lt, Elect Power Res Inst, Kunming 650217, Peoples R China
[2] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China
[3] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen, Peoples R China
关键词
Power transformer; Fault diagnosis; Dissolved gas analysis; Deep belief network; GAS; OPTIMIZATION; ALGORITHM; MODEL;
D O I
10.1016/j.egyr.2023.09.183
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Power transformer plays a very important role in power system, its long-term operation will cause various kinds of faults. Accurate identification and timely elimination of transformer faults are the basis of safe operation of power grid. As one of the most commonly used fault diagnosis methods, dissolved gas analysis (DGA) technology is used to identify fault types through dissolved gas in transformer oil, and its reliability has been proved. In order to analyze these gases and diagnose transformer fault types with the results, many methods have been developed, such as Key Gas Method, Method of Duval, IEC 60599 Method, Method of Dornenburg and Method of Rogers, etc. In some cases, the accuracy of these traditional methods is reduced and cannot be applied for diagnosis, since they have fixed input features and is not flexible for input combination. In order to achieve the propose of solving this defect, this paper introduces a deep belief network-based DGA method to diagnose the faults and states of power transformers with customized input features. For this work, six fault classifications were considered based on the nine characteristics extracted from the gases precipitated from the insulating oil of power transformers. The deep belief network was tested using oil samples collected from power transformers. Experiments have shown that the performance of the network has obtained relatively good accuracy results.
引用
收藏
页码:250 / 256
页数:7
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