A new method based on fuzzy TOPSIS and BP neural network for power transformer fault diagnosis

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control

Volumn 37, Issue 9, 2009, Pages

  Pan, Chao ^a   Ma, Cheng Lian ^a   Zheng, Ling Feng ^a   Liu, Ning ^a  

^a NORTHEAST ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Dissolved gas analysis; Fuzzy TOPSIS; Multiple attribute decision making; Pattern identification

Indexed keywords

BP NEURAL NETWORKS; COMPLEX PROCESSES; DISSOLVED GAS ANALYSIS; FAULT DIAGNOSIS; FAULT PATTERNS; FUZZY MULTIPLE ATTRIBUTE DECISION MAKING; FUZZY TOPSIS; HAUSDORFF; MONITORING INFORMATION; MULTI-ATTRIBUTE; MULTIPLE ATTRIBUTE DECISION MAKING; OPERATION STATE; PATTERN IDENTIFICATION; SAMPLE DATA; SIMILAR DEGREE; TOPSIS METHOD; TRANSFORMER FAULT DIAGNOSIS; VAGUE SETS;

AVIONICS; DISSOLUTION; ELECTRIC INSTRUMENT TRANSFORMERS; FAILURE ANALYSIS; GAS CHROMATOGRAPHY; NEURAL NETWORKS; POWER TRANSFORMERS; TRANSFORMER SUBSTATIONS;

DECISION MAKING;

EID: 67650248450     PISSN: 16743415     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Marino P, Sigiienza C, Poza F. Supporting information system for power transformer fault forecasting applications. industrial electronics society[A]. In: 2003. IECON'03. The 29th Annual Conference of the IEEE[C]. 2003.1899-1904.
2. Mao P L, Aggarwal P K. A novel approach to the classification of the transient phenomena in power transformers using combined wavelet transform and neural network[J]. IEEE Trans on Power Delivery, 2001, 16(4): 654-660.
3. Adrianna Rosa Garcez Castro, Vladimiro Miranda. Knowledge discovery in neural network with application to transformer failure dianosis[J]. Power Systems, 2005, 20(2): 717-724.
4. Wang Jian-Yuan, Ji Yan-Chao. Application of fuzzy Petri nets knowledge representation in electric power transformer fault diagnosis[J]. Proceedings of the CSEE, 2003, 23(1): 121-125.
5. Dong Li-Xin, Xiao Deng-Ming, Li Zhe, et al. Insulation fault diagnosis for power transformers based on dissolved gas analysis data mining[J]. Automation of Electric Power Systems, 2004, 28(15): 85-89.
6. Zhang Li-Gang. Relation between the composition and contents of dissolved gases in transformer oil and insulation fault diagnosis of oil-filled power equipment[J]. Transformer, 2000, 37(3): 39-42.
7. Shang Yong, Yan Chun-Jiang, Yan Zhang, et al. Synthetic Insulation Fault Diagnostic Model of Oil-immersed Power Transformers Utilizing Information Fusion[J]. Proceedings of the CSEE, 2002, 22(7): 116-118.
8. Hong Dug Hum, Choi Chang-Hwan. Multicriteria fuzzy decision-making problems based on vague sets theory[J]. Fuzzy Set and Systems, 2000, 1(14): 103-113.
9. Su Q, Lai L L, et al. A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer[J]. IEEE Trans on Power Systems, 2000, 15(2): 593-598.
10. Ding Xiao-Qun, Sun Jun, Yuan Ning-Bo. A new method of preprocessing the input data in BP network[J]. Power System Technology, 1998, 22(11): 62-63.
11. Zhang Zhou-Sheng, Xiao Deng-Ming. A small vacuum online extraction technology for the automatic chromatographic analysis of gas dissolved in insulating oil[J]. Automation of Electric Power Systems, 2007, 31(11): 92-96.
12. Zhou Li-Jun, Wu Guang-Ning, Zhang Xing-Hai, et al. Prediction of power transformer faults based on time series of weighted fuzzy degree analysis[J]. Automation of Electric Power Systems, 2005, 29(13): 53-56.