Title :
Particle Swarm Optimization-Based SVM Application: Power Transformers Incipient Fault Syndrome Diagnosis
Author :
Lee, Tsair-Fwu ; Cho, Ming-Yuan ; Shieh, Chin-Shiuh ; Fang, Fu-Min
Author_Institution :
Nat. Kaohsiung Univ. of Appl. Sci.
Abstract :
Based on statistical learning theory, support vector machine (SVM) has been well recognized as a powerful computational tool for problems with nonlinearity had high dimensionalities. In this paper, we present a successful adoption of the particle swarm optimization (PSO) algorithm to improve the performances of SVM classifier for the purpose of incipient faults syndrome diagnosis of power transformers. A PSO-based encoding technique is applied to improve the accuracy of classification. The proposed scheme removes irreverent input features that may be confusing the classifier and optimizes the kernel parameters simultaneously. Experiments on real operational data demonstrated the effectiveness and high efficiency of the proposed approach which make operation faster and also increase the accuracy of the classification
Keywords :
fault diagnosis; particle swarm optimisation; power engineering computing; power transformer protection; support vector machines; encoding technique; particle swarm optimization; power transformers incipient fault syndrome diagnosis; statistical learning theory; support vector machine; Dissolved gas analysis; Fault diagnosis; IEC standards; Oil insulation; Partial discharges; Particle swarm optimization; Petroleum; Power transformers; Support vector machine classification; Support vector machines;
Conference_Titel :
Hybrid Information Technology, 2006. ICHIT '06. International Conference on
Conference_Location :
Cheju Island
Print_ISBN :
0-7695-2674-8
DOI :
10.1109/ICHIT.2006.253528