Stratégie d'évaluation de l'état des transformateurs : esquisse de solutions pour la gestion intégrée des transformateurs vieillissants

Abstract : This PhD thesis deals the assessment method of the state of power transformers filled with oil. It brings a new approach by implementing classification methods and data mining dedicated to transformer maintenance. It proposes a strategy based on two new oil health indicators built from an adaptive Neuro-Fuzzy Inference System (ANFIS). Two classifiers were built on a labeled learning database. The Naive Bayes classifier was retained for the detection of fault from gases dissolved in oil. A simple and efficient flowchart for evaluating the condition of transformers is proposed. It allows a quick analysis of the parameters resulting from physicochemical analyzes of oil and dissolved gases. Using unsupervised classification techniques through the methods of kmeans and fuzzy C-means allowed to reconstruct operating periods of a transformer, with some particular faults. It has also been demonstrated how these methods can be used as tool to help the maintenance of a group of transformers from available oil analysis data.
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Samuel Eke. Stratégie d'évaluation de l'état des transformateurs : esquisse de solutions pour la gestion intégrée des transformateurs vieillissants. Autre. Université de Lyon, 2018. Français. ⟨NNT : 2018LYSEC013⟩. ⟨tel-02138989⟩

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