Voltage regulation using recurrent wavelet fuzzy neural network-based dynamic voltage restorer

Cheng I. Chen; Yeong Chin Chen; Chung Hsien Chen; Yung Ruei Chang

doi:10.3390/en13236242

Voltage regulation using recurrent wavelet fuzzy neural network-based dynamic voltage restorer

Cheng I. Chen, Yeong Chin Chen, Chung Hsien Chen, Yung Ruei Chang

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Dynamic voltage restorers (DVRs) are one of the effective solutions to regulate the voltage of power systems and protect sensitive loads against voltage disturbances, such as voltage sags, voltage fluctuations, et cetera. The performance of voltage compensation with DVRs relies on the robustness to the power quality disturbances and rapid detection of voltage disturbances. In this paper, the recurrent wavelet fuzzy neural network (RWFNN)-based controller for the DVR is developed. With positive-sequence voltage analysis, the reference signal for the DVR compensation can be accurately obtained. In order to enhance the response time for the DVR controller, the RWFNN is introduced due to the merits of rapid convergence and superior dynamic modeling behavior. From the experimental results with the OPAL-RT real-time simulator (OP4510, OPAL-RT Technologies Inc., Montreal, Quebec, Canada), the effectiveness of proposed controller can be verified.

Original language	English
Article number	6242
Journal	Energies
Volume	13
Issue number	23
DOIs	https://doi.org/10.3390/en13236242
State	Published - 1 Dec 2020

Keywords

Voltage regulation
dynamic voltage restorer (DVR)
positive-sequence voltage analysis
power quality
recurrent wavelet fuzzy neural network (RWFNN)-based controller

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10.3390/en13236242

Development of Monitoring and Protection System for Ac Microgrid Compatible with Iec Power Quality Standards(3/3)
Chen, C.-I. (PI)
1/08/20 → 31/07/21
Project: Research
Research of High-Performance Operation and Power Conditiong in Ac Microgrid( II )
Lin, F.-J. (PI)
1/01/20 → 31/03/21
Project: Research

Cite this

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title = "Voltage regulation using recurrent wavelet fuzzy neural network-based dynamic voltage restorer",

abstract = "Dynamic voltage restorers (DVRs) are one of the effective solutions to regulate the voltage of power systems and protect sensitive loads against voltage disturbances, such as voltage sags, voltage fluctuations, et cetera. The performance of voltage compensation with DVRs relies on the robustness to the power quality disturbances and rapid detection of voltage disturbances. In this paper, the recurrent wavelet fuzzy neural network (RWFNN)-based controller for the DVR is developed. With positive-sequence voltage analysis, the reference signal for the DVR compensation can be accurately obtained. In order to enhance the response time for the DVR controller, the RWFNN is introduced due to the merits of rapid convergence and superior dynamic modeling behavior. From the experimental results with the OPAL-RT real-time simulator (OP4510, OPAL-RT Technologies Inc., Montreal, Quebec, Canada), the effectiveness of proposed controller can be verified.",

keywords = "Voltage regulation, dynamic voltage restorer (DVR), positive-sequence voltage analysis, power quality, recurrent wavelet fuzzy neural network (RWFNN)-based controller",

author = "Chen, {Cheng I.} and Chen, {Yeong Chin} and Chen, {Chung Hsien} and Chang, {Yung Ruei}",

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AU - Chen, Cheng I.

AU - Chen, Yeong Chin

AU - Chen, Chung Hsien

AU - Chang, Yung Ruei

PY - 2020/12/1

Y1 - 2020/12/1

N2 - Dynamic voltage restorers (DVRs) are one of the effective solutions to regulate the voltage of power systems and protect sensitive loads against voltage disturbances, such as voltage sags, voltage fluctuations, et cetera. The performance of voltage compensation with DVRs relies on the robustness to the power quality disturbances and rapid detection of voltage disturbances. In this paper, the recurrent wavelet fuzzy neural network (RWFNN)-based controller for the DVR is developed. With positive-sequence voltage analysis, the reference signal for the DVR compensation can be accurately obtained. In order to enhance the response time for the DVR controller, the RWFNN is introduced due to the merits of rapid convergence and superior dynamic modeling behavior. From the experimental results with the OPAL-RT real-time simulator (OP4510, OPAL-RT Technologies Inc., Montreal, Quebec, Canada), the effectiveness of proposed controller can be verified.

AB - Dynamic voltage restorers (DVRs) are one of the effective solutions to regulate the voltage of power systems and protect sensitive loads against voltage disturbances, such as voltage sags, voltage fluctuations, et cetera. The performance of voltage compensation with DVRs relies on the robustness to the power quality disturbances and rapid detection of voltage disturbances. In this paper, the recurrent wavelet fuzzy neural network (RWFNN)-based controller for the DVR is developed. With positive-sequence voltage analysis, the reference signal for the DVR compensation can be accurately obtained. In order to enhance the response time for the DVR controller, the RWFNN is introduced due to the merits of rapid convergence and superior dynamic modeling behavior. From the experimental results with the OPAL-RT real-time simulator (OP4510, OPAL-RT Technologies Inc., Montreal, Quebec, Canada), the effectiveness of proposed controller can be verified.

KW - Voltage regulation

KW - dynamic voltage restorer (DVR)

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KW - power quality

KW - recurrent wavelet fuzzy neural network (RWFNN)-based controller

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Voltage regulation using recurrent wavelet fuzzy neural network-based dynamic voltage restorer

Abstract

Keywords

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Projects

Development of Monitoring and Protection System for Ac Microgrid Compatible with Iec Power Quality Standards(3/3)

Research of High-Performance Operation and Power Conditiong in Ac Microgrid( II )

Cite this