Modelling harmonics drawn by nonlinear loads

Lamich, M.; Balcells, J.; Mon, J.; Corbalan, Montserrat; Griful, Eulalia

doi:10.1109/cpe.2015.7231055

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…Since the use of harmonic filter reduces the harmonic components of currents (HCC), such a device also changes the harmonic components of voltages (HCV) at the filter connection point [7]. Therefore, the filter connection will change the HCC NL.…”

Section: Adaptive Nl Modeling For the Harmonic Filter Connection Impamentioning

confidence: 99%

“…Such models are rele-vant before the design and installation of the filter. Significant success in this direction was achieved by using ML algorithms that allow for creating adaptive training models capable of approximation of various nonlinear functions [7,10].…”

Section: Adaptive Nl Modeling For the Harmonic Filter Connection Impamentioning

confidence: 99%

“…To create the ML models, the R software environment was used with attraction of "elmNN" and "Multivari-ateRandomForest" packages. The tested EML and RF models were trained on the basis of measurements received from SIMEAS Q and had the following structure: 9 input parameters (voltage of the fundamental harmonic and HCV harmonics 3,5,7,9,11,13,15,17) and output values (fundamental harmonic current and HCC 3, 5, 7, 9 harmonics). At that, the ELM model had one hidden layer with 10 neurons with sinusoidal activation function (Fig.…”

Section: Nl Modeling On the Basis Of Machine Learning Modelsmentioning

confidence: 99%

“…RF model used the following parameters: number of trees, n tree =5; the number of features for the selection of splitting, m feature =5; minimum number of splitting objects min leaf =5. At the intermediate stages of the study, the Bayesian ANN proposed for this task in [7] was also trained and tested. However, in our study of NL ASF modeling this neural network showed a low accuracy.…”

Section: Nl Modeling On the Basis Of Machine Learning Modelsmentioning

confidence: 99%

“…However, it should be noted that a certain advantage of the RF model is that it does not require the analyzed data normalization. The choice of the optimal normalization method is a certain problem for neural network models, including the problem of NL behavior modeling [7].…”

Section: Nl Modeling On the Basis Of Machine Learning Modelsmentioning

confidence: 99%

See 4 more Smart Citations

Analysis and modeling of harmonic distortions in consumer’s distribution networks, containing a powerful non-linear load

et al. 2018

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The paper proposes to simulate a random variability of the nonlinear load in the form of probabilistic characteristics of the amplitudes of harmonic components of powers. The influence of the variability of distribution of amplitude values of the power harmonics on the load flow in the network, power losses and maximum power transmission in lines is studied. The results of design experiments for one of distribution networks of Azerbaijan power system, feeding the non-linear load, are given.

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Section: Adaptive Nl Modeling For the Harmonic Filter Connection Impamentioning

confidence: 99%

Section: Adaptive Nl Modeling For the Harmonic Filter Connection Impamentioning

confidence: 99%

Section: Nl Modeling On the Basis Of Machine Learning Modelsmentioning

confidence: 99%

Section: Nl Modeling On the Basis Of Machine Learning Modelsmentioning

confidence: 99%

Section: Nl Modeling On the Basis Of Machine Learning Modelsmentioning

confidence: 99%

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Analysis and modeling of harmonic distortions in consumer’s distribution networks, containing a powerful non-linear load

et al. 2018

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Enhanced harmonic state estimation in unbalanced three-phase electrical grids based on the Kalman filter and physical scale-down implementation

Molina-Moreno

Medína

Cisneros-Magana

et al. 2020

International Journal of Electrical Power & Energy Systems

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Influence of data resolution in nonlinear loads model for harmonics prediction

Balcells

Lamich

Griful

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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Abstract-This paper describes the influence of data resolution in the agreement of models to predict harmonics generated by nonlinear loads (NLL), basically formed by single phase and three phase rectifiers, eventually combined with linear loads. We assume that the network supplying the NLL has significant impedances and that it is disturbed by other parallel, random and unknown neighbor loads, sharing part of the supply system. The aim of building NLL models is to make predictions on the amount and flow paths of harmonic currents generated by such NLL in case of using parallel filters. In this paper, the models are obtained from sets of (V,I) data taken at a certain point, called measuring point (MP) and are valid to predict the NLL behavior when random known or unknown parallel loads are connected upstream of this point. The technique used to obtain the models studied here is based on Multivariate Multiple Outputs Regression (MMOR) and will not be described in detail in this paper. This method allows obtaining a set of equations giving the current harmonics as a function of voltage harmonics observed at the measuring point (MP). The accordance between model and the experimental results is very dependent on the resolution and accuracy of V and I measurements at the MP and is the core matter of this paper.

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Modelling harmonics drawn by nonlinear loads

Cited by 4 publications

References 11 publications

Analysis and modeling of harmonic distortions in consumer’s distribution networks, containing a powerful non-linear load

Analysis and modeling of harmonic distortions in consumer’s distribution networks, containing a powerful non-linear load

Enhanced harmonic state estimation in unbalanced three-phase electrical grids based on the Kalman filter and physical scale-down implementation

Influence of data resolution in nonlinear loads model for harmonics prediction

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