Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems
The purpose of the study presented in the article was to develop a method for the formation of digital twins for distributed generation plants operating as part of cyber–physical power supply systems. A method of forming a digital twin for a system for automatic regulation of the voltage and rotor s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/16/2886 |
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| author | Yuri Bulatov Andrey Kryukov Andrey Batuhtin Konstantin Suslov Ksenia Korotkova Denis Sidorov |
| author_facet | Yuri Bulatov Andrey Kryukov Andrey Batuhtin Konstantin Suslov Ksenia Korotkova Denis Sidorov |
| author_sort | Yuri Bulatov |
| collection | DOAJ |
| description | The purpose of the study presented in the article was to develop a method for the formation of digital twins for distributed generation plants operating as part of cyber–physical power supply systems. A method of forming a digital twin for a system for automatic regulation of the voltage and rotor speed of a synchronous generator is considered. The structure of a digital twin is presented in the form of a multiply connected model using experimental data. The possibility of using a fuzzy inference system, artificial neural networks, and a genetic algorithm for solving the problem is shown. As a result of the research, neuro-fuzzy models of the elements of the distributed generation plant were obtained, which are an integral part of the digital twin. Based on the simulation results, the following conclusions were drawn: the proposed method for constructing an optimized fuzzy model gives acceptable results when compared with experimental data and shows practical applicability in constructing a digital twin. In the future, in order to simplify the model, it is necessary to solve the problem of optimizing the number of rules in the fuzzy inference system. It is also advisable to direct further research to the formation of a complete hierarchical fuzzy system that connects all elements of the digital twin. |
| first_indexed | 2024-03-09T09:52:39Z |
| format | Article |
| id | doaj.art-323310677a214470bda423b10fd39062 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T09:52:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-323310677a214470bda423b10fd390622023-12-01T23:57:27ZengMDPI AGMathematics2227-73902022-08-011016288610.3390/math10162886Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply SystemsYuri Bulatov0Andrey Kryukov1Andrey Batuhtin2Konstantin Suslov3Ksenia Korotkova4Denis Sidorov5Department of Energy, Bratsk State University, 665730 Bratsk, RussiaDepartment of Power Supply and Electrical Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, RussiaDepartment of Energy, Transbaikal State University, 672039 Chita, RussiaDepartment of Power Supply and Electrical Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, RussiaDepartment of Energy, Bratsk State University, 665730 Bratsk, RussiaDepartment of Applied Mathematics, Energy Systems Institute of Siberian Branch of Russian Academy of Sciences, 664033 Irkutsk, RussiaThe purpose of the study presented in the article was to develop a method for the formation of digital twins for distributed generation plants operating as part of cyber–physical power supply systems. A method of forming a digital twin for a system for automatic regulation of the voltage and rotor speed of a synchronous generator is considered. The structure of a digital twin is presented in the form of a multiply connected model using experimental data. The possibility of using a fuzzy inference system, artificial neural networks, and a genetic algorithm for solving the problem is shown. As a result of the research, neuro-fuzzy models of the elements of the distributed generation plant were obtained, which are an integral part of the digital twin. Based on the simulation results, the following conclusions were drawn: the proposed method for constructing an optimized fuzzy model gives acceptable results when compared with experimental data and shows practical applicability in constructing a digital twin. In the future, in order to simplify the model, it is necessary to solve the problem of optimizing the number of rules in the fuzzy inference system. It is also advisable to direct further research to the formation of a complete hierarchical fuzzy system that connects all elements of the digital twin.https://www.mdpi.com/2227-7390/10/16/2886cyber–physical power supply systemsdistributed generation plantsynchronous generatordigital twinfuzzy modeloptimization |
| spellingShingle | Yuri Bulatov Andrey Kryukov Andrey Batuhtin Konstantin Suslov Ksenia Korotkova Denis Sidorov Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems Mathematics cyber–physical power supply systems distributed generation plant synchronous generator digital twin fuzzy model optimization |
| title | Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems |
| title_full | Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems |
| title_fullStr | Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems |
| title_full_unstemmed | Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems |
| title_short | Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems |
| title_sort | digital twin formation method for distributed generation plants of cyber physical power supply systems |
| topic | cyber–physical power supply systems distributed generation plant synchronous generator digital twin fuzzy model optimization |
| url | https://www.mdpi.com/2227-7390/10/16/2886 |
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