Application of Neural Networks for Data Modeling of Power Systems with Time Varying Nonlinear Loads
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Nowadays power distribution systems typically operate with nonsinusoidal voltages and currents. Harmonic currents from nonlinear loads propagate through the system and cause harmonic pollution. The premise of IEEE 519 is that there exists a shared responsibility between utilities and customers regarding harmonic control. Maintaining reasonable levels of harmonic voltage distortion depends upon customers limiting their harmonic current injections and utilities controlling the system impedance characteristics. Measurements of current taken at the point of common coupling (PCC) to a customer are expected to determine whether the customer is in compliance with IEEE 519. These measurements yield the combination of nonlinear load harmonics and nonlinear current due to supply voltage harmonics and typically the customer is required to take corrective actions to compensate the harmonics. This paper presents a neural network scheme whereby, it is possible to do data modeling of the customer's impedance and predict the resulting voltage distortion at the PCC if the customer were to take corrective actions. Experimental results from field measurements are provided. The proposed scheme is applicable to single as well as three phase systems.