| Summary: | 碩士 === 國立高雄應用科技大學 === 電機工程系碩士班 === 94 === This thesis presents an innovative method based on multi-layer Support Vector Machine (SVM) combined with Clonal Selection Algorithm （CSA） for the purpose of fault diagnosis of power transformers. A clonal selection algorithm (CSA) based encoding technique is applied to improve the accuracy of classification, which demonstrated in the literature for the first time. With features and RBF kernel parameters selection to predict incipient fault of power transformer improve the accuracy of classification systems and the generalization performance. The proposed approach is distinguished by removing redundant input features that may be confusing the classifier and optimizing the selection of kernel parameters.
As a result, the proposed approach can assist the maintenance of power transformers and extend their operation life, as well as enhance their reliability. In order to effectively and reliably monitor power transformers in a substation, the Support Vector Machine is employed to develop Multi-Layer SVM Classifier based on pattern recognition and fault diagnosis system in the proposed approach.
Finally, the collected data associated with both cases in IEC 60599 and historical data in both Taipower system and the fifth container center of Kaohsiung port are selected for computer simulation to demonstrate the effectiveness of the proposed multi-layer SVM classifier. Simulation results of practice data demonstrate the effectiveness and high efficiency of the proposed approach, which makes operation faster and also increases the accuracy of the classification.
|
|---|
