Title :
Fault diagnosis method for power transformer based on ant colony -SVM classifier
Author :
Wu, Niu ; Liangfa, Xu ; Sanguo, Hu
Author_Institution :
Dept. of Found., First Aeronaut. Inst. of Air Force, Xinyang, China
Abstract :
Failure of power transformer is very complex, so that it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on ant colony-support vector machine (AC-SVM) approach is presented for fault diagnosis of power transformer. The AC-SVM selects kernel function parameter and soft margin constant C penalty parameter of support vector machine (SVM) classifier. The performance of the AC-SVM system proposed in this study is evaluated by cases in China. The test results show that this AC-SVM model is effective to detect failure of power transformer.
Keywords :
cooperative systems; fault diagnosis; fault location; optimisation; pattern classification; power engineering computing; power transformers; support vector machines; ant colony SVM classifier; ant colony-support vector machine classifier; failure detection; fault diagnosis method; intelligent diagnostic method; power transformer; Artificial neural networks; Fault diagnosis; Kernel; Machine intelligence; Mathematical model; Power system modeling; Power transformers; Risk management; Support vector machine classification; Support vector machines; ant colony-support vector machine; fault diagnosis; kernel function parameter; transformer;
Conference_Titel :
Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-5585-0
Electronic_ISBN :
978-1-4244-5586-7
DOI :
10.1109/ICCAE.2010.5451326
