In recent years, the socio-economic development of the population, the growth of commercial and industrial sectors, as well as the ever-increasing installation of new electrical loads, have generated great evolution in demand of electricity consumption. In turn, to obtain more efficient systems, the manufacturers have produced equipment more energy efficient for residential, commercial and industrial use. However, these loads due their non-linearities, have contributed significantly to the increase in harmonic distortion levels of voltage and current, raising the concern of the power sector managers with respect to the power quality, mainly, due to the difficulty in the identification of the origin of the harmonic distortion. Therefore, to anticipate the harmonic effects and meet the current legislation, through computational techniques, this work emphasis is placed on the common coupling point (CCP) of consumers and utility, regardless of consumption characteristics and loads, to assess the harmonic impacts in his grid, besides comparing the reliability level of the techniques through the mean absolute error (MAE). The proposed methodology uses the Electrical Power Quality System (SISQEE) software that allows the use of three different computational techniques, such as Linear Regression, Artificial Neural Networks and Regression Trees, to evaluate the harmonic contribution of each feeder at the point of interest of the chosen electric grid. To prove the validity of the methodology, two case studies, based on real measurements at a university and at an industrial district, was carried out with a minimum sampling period of seven days using power quality analyzers, according to the distribution procedures by ANEEL (PRODIST). As a result of the power quality, it was verified how much each feeder impacts the voltage and current distortion at the CCP, besides classifying the feeders in relation to their respective impacts in the studied electrical grid. Also, as a result, the studies allowed the evaluation of performance between the different techniques, with different time intervals (weekly, daily and per load level), allowing to classify the behavior and reliability of each technique in each period. As a conclusion of the work, the proposed methods and analyzes presented allow managers to perform a more efficient mitigation action of the harmonic impacts caused in the electrical network and, also, to identify the differences between the techniques and their degree of reliability, in accordance with the time intervals studied.
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