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Fault detection of power transformers using genetic programming method
- Source :
- Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).
- Publication Year :
- 2005
- Publisher :
- IEEE, 2005.
-
Abstract
- This paper proposes a novel method for insulation fault detection of power transformer using the genetic programming (GP) method. Fault detection can be seen as a problem of multi-class classification. GP is a way of automatically constructing computer programs using a process analogous to biological evolution. GP methods of problem solving have a great advantage in their power to represent solutions to complex classification problems. The flexibility of representation gives GP the capacity to represent classification problems with means unavailable to other techniques such as neural networks. A binary tree (Bi-tree) structure is presented to transfer an N-class problem into N-1 two-class problems. The proposed method has been tested on the actual records and compared with the conventional methods, fuzzy system method and artificial neural network method. The result shows that GP has advantages over the existing diagnosis methods and provides a new way to solve the problem of fault detection.
- Subjects :
- Binary tree
Artificial neural network
business.industry
Computer science
Process (computing)
Genetic programming
Fuzzy control system
Machine learning
computer.software_genre
Fault detection and isolation
Power (physics)
Genetic algorithm
Artificial intelligence
Representation (mathematics)
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)
- Accession number :
- edsair.doi...........74ed75cb9cd3eb8b19e15072dfb6cf50