Abstract
Classifying data is a key process for extracting relevant information out of a database. A relevant classification problem is classifying the condition of a transformer based on its chromatography data. It is a useful problem formulation as its solution makes it possible to repair the transformer with less expenditure given that a correct classification of the equipment status is available. In this paper, we propose a Differential Evolution algorithm that evolves Perceptron Decision Trees to classify transformers from their chromatography data. Our approach shows that it is possible to evolve classifiers to identify failure in power transformers with results comparable to the ones available in the literature.
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Keywords
- Differential Evolution
- Fault Detection
- Power Transformer
- Differential Evolution Algorithm
- Good Objective Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Freitas, A.R.R., Pedrosa Silva, R.C., Guimarães, F.G. (2013). Differential Evolution and Perceptron Decision Trees for Fault Detection in Power Transformers. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_15
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DOI: https://doi.org/10.1007/978-3-642-32922-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32921-0
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