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An Application of Decision-Tree-Based Support Vector Machines to Fault Diagnosis for Transformer

In: Proceedings of 20th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Cui-ling Zhang

    (Northeastern University)

  • Da-zhi Wang

    (Northeastern University)

  • Xue-chen Jiang

    (Northeastern University)

  • Yi Ning

    (Northeastern University)

Abstract

Based on the uncertainty of generating mechanism, the complexity of data processing, and the limitations of transformer fault sample data access, the fault diagnosis model is established using the method of Vector projection on Decision-tree-based support vector machines, that combining one-to-rest with rest-to-rest classification can solve the multi-classification problem better currently. The method of Vector projection aiming at N classification problem, just construct (N − 1) SVM classifiers and have no unrecognized sector and the classify process is faster. The classification according to calculate center distance of class and divisibility measure among classes to determine five kinds of fault location of transformer, which has better generalization ability. Test show that this method comparing with traditional three ratio method and neural network increase correct-sentence rat in fault diagnosis, which has better utility value.

Suggested Citation

  • Cui-ling Zhang & Da-zhi Wang & Xue-chen Jiang & Yi Ning, 2013. "An Application of Decision-Tree-Based Support Vector Machines to Fault Diagnosis for Transformer," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, edition 127, pages 473-485, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-40063-6_48
    DOI: 10.1007/978-3-642-40063-6_48
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