Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine learning for frequency estimation of power systems
Applied Soft Computing
Time sequence data mining using time-frequency analysis and soft computing techniques
Applied Soft Computing
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Harmonic distortion monitoring for nonlinear loads using neural-network-method
Applied Soft Computing
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In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach.