Moving Discretized Control Set Model Predictive Control with Dominant Parameter Identification Strategy for Dual Active Bridge Converters
The dual active bridge (DAB) converter has grown significantly as one of the most important units for energy distribution, connecting various types of renewable energy sources with the DC microgrid. For controlling the DAB converter, moving discretized control set model predictive control (MDCS-MPC)...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/4/563 |
| _version_ | 1797297561282805760 |
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| author | Tan-Quoc Duong Sung-Jin Choi |
| author_facet | Tan-Quoc Duong Sung-Jin Choi |
| author_sort | Tan-Quoc Duong |
| collection | DOAJ |
| description | The dual active bridge (DAB) converter has grown significantly as one of the most important units for energy distribution, connecting various types of renewable energy sources with the DC microgrid. For controlling the DAB converter, moving discretized control set model predictive control (MDCS-MPC) is considered one of the most effective methods because of its advantages, such as high dynamic performance and multiobjective control. However, MDCS-MPC strongly depends on the accuracy of system parameters. Meanwhile, the system parameters can be changed due to temperature drift, manufacturing tolerance, age, and operating circumstances. As a result, the steady-state performance of the output voltage of MDCS-MPC is affected. Motivated by this, this paper proposes MDCS-MPC combined with the parameter identification technique to improve the steady-state performance of the output voltage of the DAB converter. Then, analysis of the percentage of the steady-state error of the output voltage is defined on six model parameters, and sensitivity analysis of two dominant parameters is chosen. After that, a straightforward least-squares analysis (LSA) technique is used to identify the two parameters online. The proposed method is verified through simulation in several different operating scenarios to verify its effectiveness. |
| first_indexed | 2024-03-07T22:23:06Z |
| format | Article |
| id | doaj.art-b747a992100b460b9d2d3eb31e08d2af |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-07T22:23:06Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj.art-b747a992100b460b9d2d3eb31e08d2af2024-02-23T15:26:10ZengMDPI AGMathematics2227-73902024-02-0112456310.3390/math12040563Moving Discretized Control Set Model Predictive Control with Dominant Parameter Identification Strategy for Dual Active Bridge ConvertersTan-Quoc Duong0Sung-Jin Choi1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaThe dual active bridge (DAB) converter has grown significantly as one of the most important units for energy distribution, connecting various types of renewable energy sources with the DC microgrid. For controlling the DAB converter, moving discretized control set model predictive control (MDCS-MPC) is considered one of the most effective methods because of its advantages, such as high dynamic performance and multiobjective control. However, MDCS-MPC strongly depends on the accuracy of system parameters. Meanwhile, the system parameters can be changed due to temperature drift, manufacturing tolerance, age, and operating circumstances. As a result, the steady-state performance of the output voltage of MDCS-MPC is affected. Motivated by this, this paper proposes MDCS-MPC combined with the parameter identification technique to improve the steady-state performance of the output voltage of the DAB converter. Then, analysis of the percentage of the steady-state error of the output voltage is defined on six model parameters, and sensitivity analysis of two dominant parameters is chosen. After that, a straightforward least-squares analysis (LSA) technique is used to identify the two parameters online. The proposed method is verified through simulation in several different operating scenarios to verify its effectiveness.https://www.mdpi.com/2227-7390/12/4/563moving discretized control set model predictive controlparameter identificationleast-squares analysisdual active bridge converterDC–DC converter |
| spellingShingle | Tan-Quoc Duong Sung-Jin Choi Moving Discretized Control Set Model Predictive Control with Dominant Parameter Identification Strategy for Dual Active Bridge Converters Mathematics moving discretized control set model predictive control parameter identification least-squares analysis dual active bridge converter DC–DC converter |
| title | Moving Discretized Control Set Model Predictive Control with Dominant Parameter Identification Strategy for Dual Active Bridge Converters |
| title_full | Moving Discretized Control Set Model Predictive Control with Dominant Parameter Identification Strategy for Dual Active Bridge Converters |
| title_fullStr | Moving Discretized Control Set Model Predictive Control with Dominant Parameter Identification Strategy for Dual Active Bridge Converters |
| title_full_unstemmed | Moving Discretized Control Set Model Predictive Control with Dominant Parameter Identification Strategy for Dual Active Bridge Converters |
| title_short | Moving Discretized Control Set Model Predictive Control with Dominant Parameter Identification Strategy for Dual Active Bridge Converters |
| title_sort | moving discretized control set model predictive control with dominant parameter identification strategy for dual active bridge converters |
| topic | moving discretized control set model predictive control parameter identification least-squares analysis dual active bridge converter DC–DC converter |
| url | https://www.mdpi.com/2227-7390/12/4/563 |
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