Detecção e Correção de Outliers em Curvas de Potência Utilizando Técnica Híbrida de Inteligência Artificial

Abstract

The distribution substations power curves are often affected by outliers: very discrepant measurements of the curve standard behavior. If the presence of outliers is very large, the internal studies and analysis of the power utilities developed from the history of the data collected may be compromised. In order to detect and correct atypical data, a complementary module for the Supervisory System was developed. In the previous work of the authors, two techniques were developed based on artificial intelligence: fuzzy logic and artificial neural networks. In this work is presented a hybrid technique that uses the best performances of the previous ones. Also developed in this paper, is an outlier detection method based on the standard behavior of the curve and the distribution of the collected samples. The performances of the techniques are compared using real data from a substation over 6 years.