Transformer fault diagnosis technology based on the fusion of WRSR and improved naive Bayes

被引：0
作者：
Zhu B. [1 ]
Xian R. [2 ]
Fan H. [2 ]
Liu X. [1 ]
Gao H. [2 ]
Chen L. [2 ]
机构：
[1] Zibo Power Supply Company, State Grid Shandong Electric Power Company, Zibo
[2] College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2021年 / 49卷 / 20期
基金：
中国国家自然科学基金;
关键词：
Fault location; Naive Bayes; State assessment; Transformer; WRSR;
D O I：
10.19783/j.cnki.pspc.201564
中图分类号：
学科分类号：
摘要：
Evaluation of the operational status of power transformers and accurate location of faults have always been technical bottlenecks affecting the safety of power grid operation and the efficiency of equipment operation and maintenance. This paper establishes a diagnosis based on the Weighted Rank Sum Ratio (Weighted Rank Sum Ratio, WRSR) combined with improved naive Bayesian networks. The model is used to evaluate the overall operational status of the power transformer, determine the fault location and specific fault types. The paper first collects historical fault data of transformers from multiple substations and uses it as a training set to establish a nonlinear mapping relationship between characteristic parameters and fault locations and fault types in an improved naive Bayesian network. Combined with specific transformer operating status information and detection data of a power grid, it first uses the WRSR model to evaluate the overall operating status of the specific transformer, and then substitutes the transformer fault detection data with poor performance as a test set into the improved naive Bayes network to predict fault location. The final results show that the model proposed can realize a reasonable evaluation of the state of power transformers, and can maintain a high accuracy rate in predicting fault locations and fault types. © 2021 Power System Protection and Control Press.
引用
收藏
页码：120 / 128
页数：8
相关论文
共 25 条
[1]  
HE Xianhua, ZHANG Yuanpeng, CUI Guixing, Et al., Research on transformer fault detection method based on a regression algorithm, Power System Protection and Control, 48, 21, pp. 132-139, (2020)
[2]  
CHEN Lei, HE Huiwen, WANG Lei, Et al., Fault isolation method of a hybrid HVDC system based on the coordination of a fault current limiter and a DC circuit breaker, Power System Protection and Control, 48, 19, pp. 119-127, (2020)
[3]  
LI Xinyue, LI Fengting, YIN Chunya, Et al., Transient overvoltage calculation method of HVDC sending-end system under DC bipolar blocking, Power System Protection and Control, 49, 1, pp. 1-8, (2021)
[4]  
DONG Fangxu, XIAN Richang, XIAN Riming, Et al., Fault diagnosis technology of power transformer based on bacterial foraging algorithm optimization, Electrical Measurement & Instrumentation, 55, 19, pp. 34-40, (2018)
[5]  
SU Lei, CHEN Lu, XU Peng, Et al., Operation condition analysis of transformer based on deep belief network, High Voltage Apparatus, 57, 2, pp. 56-62, (2021)
[6]  
ZHANG Yan, FANG Ruiming, Fault detection and identification of transformer based on dynamical network marker model of dissolved gas in oil, Transactions of China Electrotechnical Society, 35, 9, pp. 2032-2041, (2020)
[7]  
HAN Jiexiang, KONG Xiangping, LI Peng, Et al., A novel low voltage ride through strategy for cascaded power electronic transformer, Protection and Control of Modern Power Systems, 4, 3, pp. 227-238, (2019)
[8]  
LIU Yong, Design of transformer health diagnosis model based on adaptive neural network, Automation & Instrumentation, 1, pp. 104-107, (2021)
[9]  
ZHANG Bingqian, XIAN Richang, ZHU Feng, Et al., Case analysis of 110 kV transformer winding deformation fault, Transformer, 58, 2, pp. 69-73, (2021)
[10]  
DANG Dongsheng, ZHANG Shuyong, GE Pengjiang, Et al., Transformer fault diagnosis method based on support vector machine optimized by improved quantum-behaved particle swarm optimization, Journal of Electric Power Science and Technology, 34, 3, pp. 108-113, (2019)
123