DocumentCode :
2736844
Title :
Application of adaptive neuro fuzzy inference system to the partial discharge pattern recognition
Author :
Guo, Canxin ; Zhang, Li ; Qian, Yong ; Huang, Chengjun ; Wang, Hui ; Yao, Linpeng ; Jiang, Xiuchen
Author_Institution :
Dept. of Electr. Eng., Shanghai Jiao Tong Univ., Shanghai, China
Volume :
2
fYear :
2009
fDate :
20-22 Nov. 2009
Firstpage :
729
Lastpage :
732
Abstract :
The application of adaptive neuro fuzzy inference system (ANFIS) to the partial discharge (PD) pattern recognition is presented in this paper. Four types of defect models are made according to the main reason of insulation failures in real power system. Experiments are carried out to acquire the sample data, from which eight statistical features are extracted to construct the ANFIS. Different characteristics of the proposed defect models are compared based on the extracted features. Then the ANFIS is trained by characteristic features. Testing samples are utilized to validate the performance of the recognition system. The result shows that ANFIS reaches a successful recognition rate in the application of PD pattern classification.
Keywords :
adaptive systems; discharges (electric); fuzzy reasoning; pattern recognition; power system faults; statistical analysis; adaptive neuro fuzzy inference system; defect models; insulation failures; partial discharge pattern recognition; power system; statistical features; Circuit testing; Data mining; Feature extraction; Fuzzy logic; Fuzzy systems; Partial discharges; Pattern recognition; Power system modeling; System testing; Voltage; ANFIS; PD; features extraciton; pattern recognition; power apparatus;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
Type :
conf
DOI :
10.1109/ICICISYS.2009.5358281
Filename :
5358281
Link To Document :
بازگشت