Neural Network Based Method for Predicting Nonlinear Load Harmonics

Mazumdar, J.; Harley, R.G.; Lambert, Frank; Venayagamoorthy, G.K.

doi:10.1109/tpel.2007.897109

Cited by 62 publications

(33 citation statements)

References 19 publications

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“…4 shows the modelled neuron used for artificial neural network. ANN is a parallel distributed processing system handling a group of interconnected neurons designed to perform some intelligent task [9,10,11]. A continuous Hopfield neural network with five nodes corresponding to five pulse-positions is designed for the optimization of set of equations given in (5) with an aim to eliminate lower order harmonics.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Thus, it is seen that the sinusoidal PWM technique is preferred over this method for controlling the inverters. To tackle the problem of non-linearity, intelligent techniques like Neural Networks and Evolutionary Programming are to be used [7][8][9][10]. Present work deals with the reduction of harmonics in the output voltage of a single phase inverter which forms a basic part in most of the thyristorised applications.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Controller for Performance Optimization of Single Phase Inverter

Ambekar¹

2012

IJAIA

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Controller for Performance Optimization of Single Phase Inverter

Ambekar¹

2012

IJAIA

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“…and nonlinear loads (power electronic devices, uninterrupted power supplies, computer, etc.). So harmonics, which can cause voltage distortion, failure of sensitive electronic equipment, equipment overheating, etc, occur on power system (Mazumdar et al 2007). …”

Section: Introductionmentioning

confidence: 99%

A Self Adjustable FACTS Device and Controller for Distribution Systems

Ozkop¹,

Altaş²,

Sharaf³

2017

AKU-J. Sci. Eng.

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In this paper, a FACTS based dynamic switched C-type filter (DSCTF) compensator scheme for distribution systems is presented with different load characteristics and control strategies. In order to suppress power quality problems and increase overall energy utilization efficiency; design and digital realization of the DSCTF consisting of dynamic control strategies are studied. Matlab/Simulink Software Environment is employed to validate the effectiveness of FACTS DSCTF device. It has been shown that the proposed FACTS-DSCTF is effective to mitigate power quality and energy utilization problems as well as in compensating voltage disturbances and current harmonics. Dağıtım Sistemleri için Kendinden Ayarlanabilir FACTS Cihazı ve Denetleyicisi Anahtar kelimeler FACTS; Dinamik Gerilim Düzenleyicisi; Dağıtım Sistemi; Çok Döngülü Denetim ÖzetBu çalışmada, dağıtım sistemleri için FACTS tabanlı dinamik anahtarlamalı C-tipi filtre (DSCTF) kompanzatörü, farklı denetim stratejileri ve yük karakteristikleri ile sunulmaktadır. Enerji kullanım verimini artırmak ve güç kalitesi problemlerini bastırmak için dinamik denetim stratejilerini içeren DSCTF nin tasarımı ve dijital gerçekleştirilmesi çalışılmıştır. FACTS DSCTF cihazının etkinliğini doğrulamak için Matlab/Simulink yazılım ortamı kullanılmıştır. Önerilen FACTS-DSCTF in güç kalitesi ve enerji kullanım problemlerini azaltmanın yanı sıra gerilim bozukluklarını ve akım harmoniklerini de kompanzasyonunda etkili olduğunu göstermektedir.

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“…Therefore, how to use the measured harmonic data to develop harmonic current predictor to analyze the harmonic impact of the wind turbines and wind farms on power systems is an important issue [3,[6][7][8][9][10][11][12][13]. Harmonic current prediction is difficult to some extent; thus, many previous studies have used neural networks for harmonic current prediction [14][15][16]. If the harmonic current characteristics can be known in advance, the harmonic current predictor for wind turbines can be realized effectively.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Current Predictors for Wind Turbines

et al. 2013

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Abstract:The harmonic impact caused by wind turbines should be carefully investigated before wind turbines are interconnected. However, the harmonic currents of wind turbines are not easily predicted due to the variations of wind speed. If the harmonic current outputs can be predicted accurately, the harmonic impact of wind turbines and wind farms for power grids can be analyzed efficiently. Therefore, this paper analyzes the harmonic current characteristics of wind turbines and investigates the feasibility of developing harmonic current predictors. Field measurement, data sorting, and analysis are conducted for wind turbines. Two harmonic current predictors are proposed based on the measured harmonic data. One is the Auto-Regressive and Moving Average (ARMA)-based harmonic current predictor, which can be used for real-time prediction. The other is the stochastic harmonic current predictor considering the probability density distributions of harmonic currents. It uses the measured harmonic data to establish the probability density distributions of harmonic currents at different wind speeds, and then uses them to implement a long-term harmonic current prediction. Test results use the measured data to validate the forecast ability of these two harmonic current predictors. The ARMA-based predictor obtains poor performance on some harmonic orders due to the stochastic characteristics of harmonic current caused by the variations of wind speed. Relatively, the prediction results of stochastic harmonic current predictor show that the harmonic currents of a wind turbine in long-term operation can be effectively analyzed by the established probability density distributions. Therefore, the proposed stochastic harmonic current predictor is helpful in OPEN ACCESSEnergies 2013, 6 1315 predicting and analyzing the possible harmonic problems during the operation of wind turbines and wind farms.

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Neural Network Based Method for Predicting Nonlinear Load Harmonics

Cited by 62 publications

References 19 publications

Artificial Neural Network Controller for Performance Optimization of Single Phase Inverter

Artificial Neural Network Controller for Performance Optimization of Single Phase Inverter

A Self Adjustable FACTS Device and Controller for Distribution Systems

Harmonic Current Predictors for Wind Turbines

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