Recurrent neural networks trained with backpropagation through time algorithm to estimate nonlinear load harmonic currents

Generation of harmonics and the existence of waveform pollution in power system networks are important problems facing the power utilities. The determination of harmonic currents injected into a power network by a nonlinear load is complicated when the supply voltage waveform to the load is distorted by other loads and not a pure sinusoid. This paper proposes a neural network solution to this problem. A recurrent neural network trained with the backpropagation through time training algorithm is used to find a way of distinguishing between the so-called load harmonics and supply harmonics, without disconnecting the load from the network. The 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 and 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. This paper is particularly useful in determining whether the utility or the customer side has a higher contribution to harmonic pollution in a network. Hence, this method would be helpful in settling utility-customer disputes over who is responsible for harmonic pollution. Index Terms--Harmonics, metering, power quality, recurrent neural networks (RNNs).