Amplitude and phase estimations of power system harmonics using deep learning framework

Severoglu, Nagihan; Salor, Özgül

doi:10.1049/iet-gtd.2019.1491

Cited by 9 publications

(12 citation statements)

References 30 publications

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“…Consequently, to examine the model accuracy in a new THD level, the predicted I g,7 is compared to its measured values at grid voltage THD = 10% in Table 4. Additionally, the accuracy of proposed CLSK method is compared with the method presented in [7] and [28] in this Table. This table reveals that the error of CLSK method in a new THD condition is calculated between 7.5% and 30% at four randomly selected points.…”

Section: Comparing Predicted Current Harmonics With Experimental Resultsmentioning

confidence: 99%

“…Consequently, to examine the model accuracy in a new THD level, the predicted I g,7 is compared to its measured values at grid voltage THD = 10% in Table 4. Additionally, the accuracy of proposed CLSK method is compared with the method presented in [7] and [28] 3 Comparing the accuracy of the proposed CLSK method with [7] and [28] at THD = 12% as a considered THD level at the multi-converter condition 7.5% and 30% at four randomly selected points. According to Table 4, although the prediction error of the proposed method is increased at a new THD condition, it is still less than [7].…”

Section: 2mentioning

confidence: 99%

“…To analyse and estimate the current harmonics of a nonlinear load, authors in [25][26][27] have suggested black-box modelling using time-invariant preservation system theory. Besides, deep learning together with the convolutional neural network have been used to model the current harmonics of the system by [28]. However, the impacts of other loads and distribution network on the predicted currents have not been considered in these works.…”

Section: Introductionmentioning

confidence: 99%

“…Comparing the accuracy of the proposed CLSK method with[7] and[28] at THD = 12% as a considered THD level at the multi-converter condition Comparing the accuracy of the proposed CLSK method with[7] and[28] at THD = 10% as a new THD level at the multi-converter condition…”

mentioning

confidence: 99%

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Modelling and prediction of current harmonics generated by power converters in distribution networks

Moradi

Yaghoobi

Alduraibi

et al. 2021

IET Generation Trans & Dist

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Predicting current harmonics in distribution networks is essential to find harmonic losses and power quality indices. In order to predict the current harmonics, the effect of grid impedance and voltage distortions on grid-connected power converters need to be considered. The effects of these parameters on current harmonics can be nonlinear, which complicates the system analysis. Consequently, this paper investigates the impacts of background voltage harmonics, grid impedance, and multi-parallel grid-connected converters on current harmonics at a point of common coupling. Moreover, a novel method called combined least squares and kriging (CLSK) is proposed to predict the current harmonics after evaluating the level of system nonlinearity. In the proposed CLSK method, the prediction model is developed by measured voltage and current harmonics at the point of common coupling to identify and predict the current harmonics. This analysis shows that using the proposed CLSK method, the accuracy of the current harmonic prediction is increased compared to the existing methods. The accuracy and the efficiency of the proposed CLSK method are verified by lab experimental results and field measurement data.

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Section: Comparing Predicted Current Harmonics With Experimental Resultsmentioning

confidence: 99%

“…Consequently, to examine the model accuracy in a new THD level, the predicted I g,7 is compared to its measured values at grid voltage THD = 10% in Table 4. Additionally, the accuracy of proposed CLSK method is compared with the method presented in [7] and [28] 3 Comparing the accuracy of the proposed CLSK method with [7] and [28] at THD = 12% as a considered THD level at the multi-converter condition 7.5% and 30% at four randomly selected points. According to Table 4, although the prediction error of the proposed method is increased at a new THD condition, it is still less than [7].…”

Section: 2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Modelling and prediction of current harmonics generated by power converters in distribution networks

Moradi

Yaghoobi

Alduraibi

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…The method mentioned above for detecting harmonic currents cannot work with good performance under an electrical system with a DC quantity occurs. A neural network (NN) has a learning and training property that can quickly and accurately estimate the harmonic current components [26][27][28][29][30][31][32][33][34][35][36]. The NN can be designed to counteract the drawback of the traditional detection methods with good performance.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Detection for Shunt Active Power Filter Using ADALINE Neural Network

Janpong

Areerak

2021

Energies

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This paper presents an efficient harmonic detection for real-time generation of the reference current fed to a shunt active power filter using the ADALINE neural network. This proposed method is a single layer with 101 nodes generating the coefficients referred to as weights of the reference current model. It effectively overcomes the drawback of the current technology, which is instantaneous power theory (PQ). The proposed method was implemented on the TMS320F28335 DSP board and tested against MATLAB with Simulink as a hardware-in-loop (HIL) structure. This method gives a good performance by producing a precise reference current in a short period with uncomplicated calculation. It also efficiently can eliminate individual harmonic current. The achieved percentage of total harmonic distortion (%THD) in the current is reduced following the IEEE standard, while the power factor can be maintained to unity.

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A statistical test of phase difference via wavelet method and its application to the spread of air pollution

Wan,

Wei,

Xia

2024

Stoch Environ Res Risk Assess

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Amplitude and phase estimations of power system harmonics using deep learning framework

Cited by 9 publications

References 30 publications

Modelling and prediction of current harmonics generated by power converters in distribution networks

Modelling and prediction of current harmonics generated by power converters in distribution networks

Harmonic Detection for Shunt Active Power Filter Using ADALINE Neural Network

A statistical test of phase difference via wavelet method and its application to the spread of air pollution

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