Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Harmonic identification using parallel neural networks in single-phase systems
Applied Soft Computing
ANN based online voltage estimation
Applied Soft Computing
Multifeedback-Layer Neural Network
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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Nowadays, harmonic distortion in electric power systems is a power quality problem that has been attracting significant attention of engineering and scientific community. In order to evaluate the total harmonic distortion caused by particular nonlinear loads in power systems, the harmonic current components estimation becomes a critical issue. This paper presents an efficient approach to distortion metering, based on artificial neural networks applied to harmonic content estimation of load currents in single-phase systems. The harmonic content is computed using the estimation of amplitudes and phases of the first five odd harmonic components, which are carried out considering the waveform variations of current drained by nonlinear loads, within previously known limits. The proposed online monitoring method requires low computational effort and does not demand a specific number of samples per period at a fixed sampling rate, resulting in a low cost harmonic tracking system. The results from neural networks harmonic identification method are compared to the truncated fast Fourier transform algorithm. Besides, simulation and experimental results are presented to validate the proposed approach.