Avaliação da distorção harmônica total de tensão no ponto de acoplamento comum industrial usando o processo KDD baseado em medição ; Evaluation of total voltage harmonic distortion at the industrial joint coupling point using the KDD-based measurement process

In the last decades, the transformation industry has provided the introduction of increasingly faster and more energy efficient products for residential, commercial and industrial use, however these loads due to their non-linearity have contributed significantly to the increase of distortion levels harmonic of voltage as a result of the current according to the Power Quality indicators of the Brazilian electricity distribution system. The constant increase in the levels of distortions, especially at the point of common coupling, has generated in the current day a lot of concern in the concessionaires and in the consumers of electric power, due to the problems that cause like losses of the quality of electric power in the supply and in the installations of the consumers and this has provided several studies on the subject. In order to contribute to the subject, this thesis proposes a procedure based on the Knowledge Discovery in Database - KDD process to identify the impact loads of harmonic distortions of voltage at the common coupling point. The proposed methodology uses computational intelligence and data mining techniques to analyze the data collected by energy quality meters installed in the main loads and the common coupling point of the consumer and consequently establish the correlation between the harmonic currents of the nonlinear loads with the harmonic distortion at the common coupling point. The proposed process consists in analyzing the loads and the layout of the location where the methodology will be applied, in the choice and installation of the QEE meters and in the application of the complete KDD process, including the procedures for collection, selection, cleaning, integration, transformation and reduction, mining, interpretation, and evaluation of data. In order to contribute, the data mining techniques of Decision Tree and Naïve Bayes were applied and several algorithms were tested for the algorithm with the most significant results for this type of analysis as presented in the results. The ...