An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 1
Recently, there has been an increased interest in the use of artificial neural networks in various branches of the electric power industry including relay protection. Аrtificial neural networks are one of the fastest growing areas in artificial intelligence technology. Recently, there has been an in...
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Belarusian National Technical University
2021-12-01
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| Series: | Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика |
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| Online Access: | https://energy.bntu.by/jour/article/view/2117 |
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| author | Yu. V. Rumiantsev F. A. Romaniuk |
| author_facet | Yu. V. Rumiantsev F. A. Romaniuk |
| author_sort | Yu. V. Rumiantsev |
| collection | DOAJ |
| description | Recently, there has been an increased interest in the use of artificial neural networks in various branches of the electric power industry including relay protection. Аrtificial neural networks are one of the fastest growing areas in artificial intelligence technology. Recently, there has been an increased interest in the use of аrtificial neural networks in the electric power engineering, including relay protection. Existing microprocessor-based relay protection devices use a traditional digital signal processing of the monitored signals which is reduced to a multiplying the values of successive samples of the monitored current and voltage signals by predetermined coefficients in order to calculate their RMS values. In this case, the calculated RMS values often do not reflect the real processes occurring in the protected electrical equipment due to, for example, current transformer saturation because of the DC component presence in the fault current. When the current transformer is saturated, its secondary current waveform has a characteristic non-periodic distorted form, which is significantly differs from its primary (true) waveform, which causes underestimation of the calculated RMS value of the secondary current compared to its true value. In its turn, this causes to a trip time delay or even to a relay protection devices operation failure. The use of аrtificial neural networks in conjunction with a traditional digital signal processing provides a different approach to the functioning of both the measuring and logical parts of the microprocessor-based relay protection devices, which significantly increases the speed and reliability of such relay protection devices in comparison with their traditional implementation. A possible application of the аrtificial neural networks for the relay protection purposes is the fault occurrence detection and its type identification, current transformer secondary current waveform distortion restoration due to its saturation up to its true value, detection the distorted and undistorted sections of the current transformer secondary current waveform during its saturation, primary power equipment abnormal operating modes detection, for example, power transformer magnetizing current inrush. The article describes in detail the stages of the practical implementation of the аrtificial neural networks in the MATLAB-Simulink environment by the example of its use to restore the distorted current transformer secondary current waveform due to saturation. |
| format | Article |
| id | doaj-art-e87aae15daa24d04a3bc85866424dbb8 |
| institution | Kabale University |
| issn | 1029-7448 2414-0341 |
| language | Russian |
| publishDate | 2021-12-01 |
| publisher | Belarusian National Technical University |
| record_format | Article |
| series | Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика |
| spelling | doaj-art-e87aae15daa24d04a3bc85866424dbb82025-02-03T05:20:02ZrusBelarusian National Technical UniversityИзвестия высших учебных заведений и энергетических объединенний СНГ: Энергетика1029-74482414-03412021-12-0164647949110.21122/1029-7448-2021-64-6-479-4911784An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 1Yu. V. Rumiantsev0F. A. Romaniuk1Belarusian National Technical UniversityBelarusian National Technical UniversityRecently, there has been an increased interest in the use of artificial neural networks in various branches of the electric power industry including relay protection. Аrtificial neural networks are one of the fastest growing areas in artificial intelligence technology. Recently, there has been an increased interest in the use of аrtificial neural networks in the electric power engineering, including relay protection. Existing microprocessor-based relay protection devices use a traditional digital signal processing of the monitored signals which is reduced to a multiplying the values of successive samples of the monitored current and voltage signals by predetermined coefficients in order to calculate their RMS values. In this case, the calculated RMS values often do not reflect the real processes occurring in the protected electrical equipment due to, for example, current transformer saturation because of the DC component presence in the fault current. When the current transformer is saturated, its secondary current waveform has a characteristic non-periodic distorted form, which is significantly differs from its primary (true) waveform, which causes underestimation of the calculated RMS value of the secondary current compared to its true value. In its turn, this causes to a trip time delay or even to a relay protection devices operation failure. The use of аrtificial neural networks in conjunction with a traditional digital signal processing provides a different approach to the functioning of both the measuring and logical parts of the microprocessor-based relay protection devices, which significantly increases the speed and reliability of such relay protection devices in comparison with their traditional implementation. A possible application of the аrtificial neural networks for the relay protection purposes is the fault occurrence detection and its type identification, current transformer secondary current waveform distortion restoration due to its saturation up to its true value, detection the distorted and undistorted sections of the current transformer secondary current waveform during its saturation, primary power equipment abnormal operating modes detection, for example, power transformer magnetizing current inrush. The article describes in detail the stages of the practical implementation of the аrtificial neural networks in the MATLAB-Simulink environment by the example of its use to restore the distorted current transformer secondary current waveform due to saturation.https://energy.bntu.by/jour/article/view/2117artificial neural networkrelay protectioncurrent transformersaturationmatlab-simulink |
| spellingShingle | Yu. V. Rumiantsev F. A. Romaniuk An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 1 Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика artificial neural network relay protection current transformer saturation matlab-simulink |
| title | An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 1 |
| title_full | An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 1 |
| title_fullStr | An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 1 |
| title_full_unstemmed | An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 1 |
| title_short | An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 1 |
| title_sort | artificial neural network developed in matlab simulink for reconstruction a distorted secondary current waveform part 1 |
| topic | artificial neural network relay protection current transformer saturation matlab-simulink |
| url | https://energy.bntu.by/jour/article/view/2117 |
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