Journal
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume 23, Issue 6, Pages 2738-2745Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2008.2005097
Keywords
AC motor drives; harmonic analysis; harmonic distortion; neural network applications; neural network architecture
Categories
Funding
- National Science Foundation
Ask authors/readers for more resources
Echo state networks (ESNs) are a special form of recurrent neural network (RNN). Complexities with existing algorithms have thus far limited supervised training techniques for RNNs from widespread use. When it comes to practical applications, multilayer perceptron neural networks (MLPNs) still dominate. This paper investigates the application of ESNs for the prediction of true current harmonics of a nonlinear load in the presence of distorted supply voltage. The determination of true harmonic current injection by individual loads is complicated by the fact that the supply voltage waveform at the point of common coupling (PCC) is distorted by other loads at the PCC or further upstream. Experimental results are presented with the proposed ESN algorithm applied to a laboratory test circuit. The difference between the measured current harmonics and the predicted ESN results are quantified in the form of a new parameter. The uniqueness of the proposed harmonic prediction method is that the results are obtained without disrupting the operation of any load and only require the acquisition of voltage and current waveforms. This method is applicable for both single- and three-phase loads.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available