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This paper presents a procedure to estimate the impacts on voltage harmonic distortion at a point of interest due to multiple nonlinear loads in the electrical network. Despite artificial neural networks (ANN) being a widely used technique for the solution of a large amount and variety of issues in electric power systems, including harmonics modeling, its utilization to establish relationships among the harmonic voltage at a point of interest in the electric grid and the corresponding harmonic currents generated by nonlinear loads was not found in the literature, thus this innovative procedure is considered in this article. A simultaneous measurement campaign must be carried out in all nonlinear loads and at the point of interest for data acquisition to train and test the ANN model. A sensitivity analysis is proposed to establish the percent contribution of load currents on the observed voltage distortion, which constitutes an original definition presented in this paper. Initially, alternative transient program (ATP) simulations are used to calculate harmonic voltages at points of interest in an industrial test system due to nonlinear loads whose harmonic currents are known. The resulting impacts on voltage harmonic distortions obtained by the ATP simulations are taken as reference values to compare with those obtained by using the proposed procedure based on ANN. By comparing ATP results with those obtained by the ANN model, it is observed that the proposed methodology is able to classify correctly the impact degree of nonlinear load currents on voltage harmonic distortions at points of interest, as proposed in this paper.

This paper proposes the development of a three-phase state estimation algorithm, which ensures complete observability for the electric network and a low investment cost for application in typical electric power distribution systems, which usually exhibit low levels of supervision facilities and measurement redundancy. Using the customers´ energy bills to calculate average demands, a three-phase load flow algorithm is run to generate pseudo-measurements of voltage magnitudes, active and reactive power injections, as well as current injections which are used to ensure the electrical network is full-observable, even with measurements available at only one point, the substation-feeder coupling point. The estimation process begins with a load flow solution for the customers´ average demand and uses an adjustment mechanism to track the real-time operating state to calculate the pseudo-measurements successively. Besides estimating the real-time operation state the proposed methodology also generates nontechnical losses estimation for each operation state. The effectiveness of the state estimation procedure is demonstrated by simulation results obtained for the IEEE 13-bus test network and for a real urban feeder.

Global energy systems are undergoing a transition process towards renewable energy and energy efficiency practices. Induction motors play an important role in this energy transformation process since they are widely used as industrial loads, representing more than 53% of global energy consumption. With more countries adopting minimum energy performance standards through more efficient induction motors, comparisons between these new technologies in the presence of electrical disturbances must be systematically evaluated before adopting a substitution policy in the industry. To this end, this work presents a comparative analysis of the impact of harmonic voltages on the performance and temperature rise of electric motors classes IE2, IE3 and IE4 in the same operational conditions in view of future substitutions. The results show that under ideal operating conditions the IE4 class permanent magnet motor has better performance in terms of consumption and temperature, however presenting non-linear characteristics. In the presence of voltage harmonics, this scenario changes completely according to the harmonic content. Finally, aiming to analyze the harmonics influence in the motor temperature rise a statistical analysis by means of Spearman correlation matrices is presented.