A Novel Method for Predicting Harmonic Current Injection from Non-Linear Loads using Neural Networks

Joy Mazumdar
Frank C. Lambert
Ganesh K. Venayagamoorthy, Missouri University of Science and Technology
Ronald G. Harley

This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1385

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Generation of harmonics and the existence of waveform pollution in power system networks is one of the major problems facing the utilities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a nonlinear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. A recurrent neural network trained with the backpropagation through time (BPTT) training algorithm is used to find a way of distinguishing between the load harmonics and supply harmonics, without disconnecting the load from the network. The biggest advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This technology could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument.