Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/23/6242 |
| _version_ | 1797546674660311040 |
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| author | Cheng-I Chen Yeong-Chin Chen Chung-Hsien Chen Yung-Ruei Chang |
| author_facet | Cheng-I Chen Yeong-Chin Chen Chung-Hsien Chen Yung-Ruei Chang |
| author_sort | Cheng-I Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:32:37Z |
| format | Article |
| id | doaj.art-fd9de0410c2848e5b928be95fe0a36fa |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T14:32:37Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fd9de0410c2848e5b928be95fe0a36fa2023-11-20T22:30:14ZengMDPI AGEnergies1996-10732020-11-011323624210.3390/en13236242Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage RestorerCheng-I Chen0Yeong-Chin Chen1Chung-Hsien Chen2Yung-Ruei Chang3Department of Electrical Engineering, National Central University, Taoyuan 32001, TaiwanDepartment of Computer Science and Information Engineering, Asia University, Taichung 41354, TaiwanMetal Industries Research and Development Centre, Taichung 40768, TaiwanInstitute of Nuclear Energy Research, Taoyuan 32546, TaiwanDynamic 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.https://www.mdpi.com/1996-1073/13/23/6242voltage regulationdynamic voltage restorer (DVR)power qualityrecurrent wavelet fuzzy neural network (RWFNN)-based controllerpositive-sequence voltage analysis |
| spellingShingle | Cheng-I Chen Yeong-Chin Chen Chung-Hsien Chen Yung-Ruei Chang Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer Energies voltage regulation dynamic voltage restorer (DVR) power quality recurrent wavelet fuzzy neural network (RWFNN)-based controller positive-sequence voltage analysis |
| title | Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer |
| title_full | Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer |
| title_fullStr | Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer |
| title_full_unstemmed | Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer |
| title_short | Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer |
| title_sort | voltage regulation using recurrent wavelet fuzzy neural network based dynamic voltage restorer |
| topic | voltage regulation dynamic voltage restorer (DVR) power quality recurrent wavelet fuzzy neural network (RWFNN)-based controller positive-sequence voltage analysis |
| url | https://www.mdpi.com/1996-1073/13/23/6242 |
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