Power Transformer Fault Diagnosis Using Support Vector Machines and Artificial Neural Networks with Clonal Selection Algorithms Optimization

• Published in: Knowledge-Based Intelligent Information and Engineering Systems Vol. 4251; pp. 179 - 186
• Main Authors: Cho, Ming-Yuan, Lee, Tsair-Fwu, Gau, Shih-Wei, Shih, Ching-Nan
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Information systems. Data bases; Memory organisation. Data processing; Power Transformer; Software; Support Vector Machine; Support Vector Machine Classifier; Test Success; Time Series Forecast; Performance evaluation; Knowledge engineering; Statistical analysis; Redundancy; Classifier; Neural network; Power transformer; Optimization; Radial basis function; Kernels; Coding; Vector support machine; Artificial intelligence; Fault diagnostic; Clone; Multilayer network
• ISBN: 3540465359; 9783540465355
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11892960_22

This paper presents an innovative method based on Artificial Neural Network (ANN) and multi-layer Support Vector Machine (SVM) 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. 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.