Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter

Owing to the use of nonlinear loads in the distribution side, there are power quality issues such as voltage swell/sag, harmonics, flickers, voltage imbalance, and outage. The harmonics in power system affect the quality of power and hence a suitable methodology is vital to mitigate the harmonics an...

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Autores principales:	K. R. Sugavanam, K. Mohana sundaram, R. Jeyabharath, P. Veena
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	srf theory shunt active filter hysteresis current controller five-level cascaded inverter convolution neural network artificial neural network Control engineering systems. Automatic machinery (General) TJ212-225 Automation T59.5
Acceso en línea:	https://doaj.org/article/ed4b2f57321d4bcbad91e418e977f4fc
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spelling	oai:doaj.org-article:ed4b2f57321d4bcbad91e418e977f4fc2021-11-04T15:00:41ZConvolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter0005-11441848-338010.1080/00051144.2021.1985703https://doaj.org/article/ed4b2f57321d4bcbad91e418e977f4fc2021-10-01T00:00:00Zhttp://dx.doi.org/10.1080/00051144.2021.1985703https://doaj.org/toc/0005-1144https://doaj.org/toc/1848-3380Owing to the use of nonlinear loads in the distribution side, there are power quality issues such as voltage swell/sag, harmonics, flickers, voltage imbalance, and outage. The harmonics in power system affect the quality of power and hence a suitable methodology is vital to mitigate the harmonics and compensation of reactive power. In this paper, CNN (Convolutional Neural Network)-based harmonic mitigation is performed. A 5-level cascaded H-bridge inverter is employed as a shunt active filter in which the reference current is generated by the SRF theory, incorporating CNN for harmonic extraction. The DC-link potential across capacitor is retained by means of ANN (Artificial Neural Network) controller whose behaviour is compared with a proportional controller as well as FLC. The gating pulse for the cascaded inverter is generated by means of PWM generator incorporated with Hysteresis Current Controller (HCC). By this control strategy, the harmonics in the current and voltage get mitigated; subsequently, the reactive power compensation is achieved with unity power factor. By implementing the five-level inverter, the THD and the settling time are minimized. The performance of the system is analysed using MATLAB for nonlinear load and the hardware is implemented with FPGA Spartan 6E. The THD of 0.93% is accomplished in simulation and 1.4% in the hardware execution.K. R. SugavanamK. Mohana sundaramR. JeyabharathP. VeenaTaylor & Francis Grouparticlesrf theoryshunt active filterhysteresis current controllerfive-level cascaded inverterconvolution neural networkartificial neural networkControl engineering systems. Automatic machinery (General)TJ212-225AutomationT59.5ENAutomatika, Vol 62, Iss 3-4, Pp 471-485 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	srf theory shunt active filter hysteresis current controller five-level cascaded inverter convolution neural network artificial neural network Control engineering systems. Automatic machinery (General) TJ212-225 Automation T59.5
spellingShingle	srf theory shunt active filter hysteresis current controller five-level cascaded inverter convolution neural network artificial neural network Control engineering systems. Automatic machinery (General) TJ212-225 Automation T59.5 K. R. Sugavanam K. Mohana sundaram R. Jeyabharath P. Veena Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter
description	Owing to the use of nonlinear loads in the distribution side, there are power quality issues such as voltage swell/sag, harmonics, flickers, voltage imbalance, and outage. The harmonics in power system affect the quality of power and hence a suitable methodology is vital to mitigate the harmonics and compensation of reactive power. In this paper, CNN (Convolutional Neural Network)-based harmonic mitigation is performed. A 5-level cascaded H-bridge inverter is employed as a shunt active filter in which the reference current is generated by the SRF theory, incorporating CNN for harmonic extraction. The DC-link potential across capacitor is retained by means of ANN (Artificial Neural Network) controller whose behaviour is compared with a proportional controller as well as FLC. The gating pulse for the cascaded inverter is generated by means of PWM generator incorporated with Hysteresis Current Controller (HCC). By this control strategy, the harmonics in the current and voltage get mitigated; subsequently, the reactive power compensation is achieved with unity power factor. By implementing the five-level inverter, the THD and the settling time are minimized. The performance of the system is analysed using MATLAB for nonlinear load and the hardware is implemented with FPGA Spartan 6E. The THD of 0.93% is accomplished in simulation and 1.4% in the hardware execution.
format	article
author	K. R. Sugavanam K. Mohana sundaram R. Jeyabharath P. Veena
author_facet	K. R. Sugavanam K. Mohana sundaram R. Jeyabharath P. Veena
author_sort	K. R. Sugavanam
title	Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter
title_short	Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter
title_full	Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter
title_fullStr	Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter
title_full_unstemmed	Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter
title_sort	convolutional neural network-based harmonic mitigation technique for an adaptive shunt active power filter
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/ed4b2f57321d4bcbad91e418e977f4fc
work_keys_str_mv	AT krsugavanam convolutionalneuralnetworkbasedharmonicmitigationtechniqueforanadaptiveshuntactivepowerfilter AT kmohanasundaram convolutionalneuralnetworkbasedharmonicmitigationtechniqueforanadaptiveshuntactivepowerfilter AT rjeyabharath convolutionalneuralnetworkbasedharmonicmitigationtechniqueforanadaptiveshuntactivepowerfilter AT pveena convolutionalneuralnetworkbasedharmonicmitigationtechniqueforanadaptiveshuntactivepowerfilter
_version_	1718444808057389056

Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter

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