Comparação de Classificadores na Análise de Gases Dissolvidos

Jânio Coutinho CanutoDiego Oliveira Cardoso


A Análise de Gases Dissolvidos (AGD) é uma poderosa técnica para diagnóstico de falhas incipientes em transformadores isolados a óleo. A utilização de classificadores inteligentes tem se tornado uma prática cada vez mais recorrente nesta área, sendo a Rede Neural Artificial (RNA) um dos algoritmos mais populares. Neste trabalho, busca-se comparar três classificadores de rápida implementação com a RNA, método já estabelecido, porém mais complexo. Os classificadores são: o k-ésimo vizinho mais próximo (KNN), um baseado no Discriminante Linear de Fisher e um na métrica de Mahalanobis. Dentre esses, os melhores resultados foram obtidos pelo KNN e a RNA, com 81,97% e 85,25% de acertos durante a validação, seguidos pelo Discriminante de Fisher e Mahalanobis, ambos com 70,05%. Todos os classificadores tiveram um bom desempenho, mostrando superioridade a algumas técnicas clássicas como a de Rogers e Doernenburg para os dados analisados. Dessa forma, os três algoritmos se mostraram uma alternativa à RNA, com implementação mais rápida e com menor peso computacional, cabendo dar destaque ao KNN.

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