Constrained neural network-based identification of harmonic sources

Hartana, R.K.; Richards, G.G.

doi:10.1109/28.195908

Cited by 22 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI techniques for HSE encompass Artificial Neural Networks (ANNs) and its variants, Bayesian Learning (BL), Fuzzy Inference System (FIS), and hybrid techniques. In early works of [19] and [20], ANN was combined with traditional HSE and constrained estimation, respectively, to estimate current injection of harmonic sources. The estimates by ANNs were treated as pseudo-measurements and modified in the complementary stages of either HSE or constrained estimation.…”

Section: B Assessment Of the State-of-the-artmentioning

confidence: 99%

Harmonic Current Estimation of Unmonitored Harmonic Sources With a Novel Oversampling Technique for Limited Datasets

et al. 2022

View full text Add to dashboard Cite

In modern power systems, harmonics are amongst the significant issues attributed to renewable energy sources and nonlinear loads. Direct harmonic monitoring of the entire power system may be too costly or impractical, and measured data could be limited. In this paper, a new methodology is proposed to estimate harmonic current rms values of unmonitored harmonic sources, based on harmonic voltage rms magnitudes only, measured at a limited number of monitored buses. A new technique of output curve-normalization is employed in pre-processing. Subsequently, a method is proposed to refine the architecture of the Artificial Neural Networks' (ANNs), after which ANN-based harmonic current estimators are developed for each harmonic order and each harmonic source. Furthermore, a novel Neural Oversampling Consensus Algorithm for Regression (NOCAR) is proposed to improve estimation accuracy. K-Nearest Neighbor (KNN) and ANN are combined in developing NOCAR. A comparison is made with state-of-the-art techniques by using synthetic data, which demonstrates both the proposed method's robustness and its capability to perform when minimal information is available. The implementation for real data demonstrates the efficiency of the ANNbased harmonic current estimators with oversampling. The influence of the number of harmonic meters is investigated, revealing the ability of this data-driven technique to reduce the number of harmonic meters, and hence monitoring costs. Moreover, the correlation between different harmonic orders is studied, with results suggesting that, unlike the widely accepted notion, this correlation should not be ignored in harmonic analysis. This study highlights the advantages of integrating intelligent techniques into harmonic monitoring systems. INDEX TERMSArtificial neural networks, harmonic current estimation, harmonic voltage monitors, oversampling. NOMENCLATURE b k+1 j Bias of node j in Layer k+1. D Dataset for ANN training and testing. d k+1 j

show abstract

Section: B Assessment Of the State-of-the-artmentioning

confidence: 99%

Harmonic Current Estimation of Unmonitored Harmonic Sources With a Novel Oversampling Technique for Limited Datasets

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Up to now, there have been several ways to estimate harmonics that are defined as unwanted components in an alternating waveform having distortion. For example, we can name the discrete Fourier transform methods [5], the mode estimation techniques [6], data exploration tools [7], independent component analysis [8], and neural networks [9]. Suresh Kumar et al [10] we used genotype algorithms, the minimum squared genetic algorithms, the optimization of the least squared hybrid particles an adaptive neural network in order to estimate harmonics in the power system.…”

Section: Introductionmentioning

confidence: 99%

The estimate of amplitude and phase of harmonics in power system using the extended kalman filter

Kamarposhti¹,

Solyman²

2021

Bulletin EEI

View full text Add to dashboard Cite

Nowadays, the amplitude of the harmonics in the power grid has increased unwittingly due to the increasing use of the nonlinear elements and power electronics. It has led to a significant reduction in power quality indicators. As a first step, the estimate of the amplitude, and the phase of the harmonics in the power grid are essential to resolve this problem. We use the Kalman filter to estimate the phase, and we use the minimal squared linear estimator to assess the amplitude. To test the aforementioned method, we use terminal test signals of the industrial charge consisting of the power converters and ignition coils. The results show that this algorithm has a high accuracy and estimation speed, and they confirm the proper performance in instantaneous tracking of the parameters.

show abstract

Independent Component Analysis for Harmonic Studies

Ribeiro

2009

Time‐Varying Waveform Distortions in Power Systems

View full text Add to dashboard Cite

Constrained neural network-based identification of harmonic sources

Cited by 22 publications

References 6 publications

Harmonic Current Estimation of Unmonitored Harmonic Sources With a Novel Oversampling Technique for Limited Datasets

Harmonic Current Estimation of Unmonitored Harmonic Sources With a Novel Oversampling Technique for Limited Datasets

The estimate of amplitude and phase of harmonics in power system using the extended kalman filter

Independent Component Analysis for Harmonic Studies

Contact Info

Product

Resources

About