Multivariable sequential model predictive control of LCL‐type grid connected inverter
Abstract The conventional single variable indirect model predictive control (SIMPC) of LCL‐type three‐level grid‐ connected inverter (GCI) is to indirectly control the grid current by controlling the output current of the inverter, which usually produces large errors and reduces the system anti‐dist...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-03-01
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| Series: | IET Power Electronics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/pel2.12408 |
| _version_ | 1827999271283064832 |
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| author | Hui Zhang Ran Tao Zhiliang Li Xiao Zhang Zhixun Ma |
| author_facet | Hui Zhang Ran Tao Zhiliang Li Xiao Zhang Zhixun Ma |
| author_sort | Hui Zhang |
| collection | DOAJ |
| description | Abstract The conventional single variable indirect model predictive control (SIMPC) of LCL‐type three‐level grid‐ connected inverter (GCI) is to indirectly control the grid current by controlling the output current of the inverter, which usually produces large errors and reduces the system anti‐disturbance performance. Although the previously proposed multivariable weighted model predictive control (MWMPC) method of LCL‐type GCI can directly control many variables, such as grid current, it requires setting the weight coefficient for each variable and involves a substantial amount of calculation. To due with these problems, this paper proposes a multivariable sequential model predictive control (MSMPC) method for LCL‐type three‐level GCI. The inverter output current, the filter capacitor voltage, and the grid current are gradually predicted and optimized under the variable control sequence of the LCL‐type GCI mathematical model. Under the premise of achieving direct control of the variables above, this method can reduce the number of weighting coefficients and gradually contract the optimization space to reduce the computation required for multivariable prediction and optimization. This paper compares the proposed MSMPC to the conventional SIMPC and MWMPC in steady state, transient state performance, and anti‐disturbance performance. The effectiveness of the proposed MSMPC is demonstrated through simulation and experimentation. |
| first_indexed | 2024-04-10T05:55:56Z |
| format | Article |
| id | doaj.art-2d76d803e6944276844b1fea449ef7b2 |
| institution | Directory Open Access Journal |
| issn | 1755-4535 1755-4543 |
| language | English |
| last_indexed | 2024-04-10T05:55:56Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Power Electronics |
| spelling | doaj.art-2d76d803e6944276844b1fea449ef7b22023-03-03T12:18:46ZengWileyIET Power Electronics1755-45351755-45432023-03-0116455857410.1049/pel2.12408Multivariable sequential model predictive control of LCL‐type grid connected inverterHui Zhang0Ran Tao1Zhiliang Li2Xiao Zhang3Zhixun Ma4School of Electrical Engineering China University of Mining and Technology Xuzhou ChinaSchool of Electrical Engineering China University of Mining and Technology Xuzhou ChinaSchool of Electrical Engineering China University of Mining and Technology Xuzhou ChinaSchool of Electrical Engineering China University of Mining and Technology Xuzhou ChinaNational Maglev Transportation Engineering R&D Center Tongji University Shanghai ChinaAbstract The conventional single variable indirect model predictive control (SIMPC) of LCL‐type three‐level grid‐ connected inverter (GCI) is to indirectly control the grid current by controlling the output current of the inverter, which usually produces large errors and reduces the system anti‐disturbance performance. Although the previously proposed multivariable weighted model predictive control (MWMPC) method of LCL‐type GCI can directly control many variables, such as grid current, it requires setting the weight coefficient for each variable and involves a substantial amount of calculation. To due with these problems, this paper proposes a multivariable sequential model predictive control (MSMPC) method for LCL‐type three‐level GCI. The inverter output current, the filter capacitor voltage, and the grid current are gradually predicted and optimized under the variable control sequence of the LCL‐type GCI mathematical model. Under the premise of achieving direct control of the variables above, this method can reduce the number of weighting coefficients and gradually contract the optimization space to reduce the computation required for multivariable prediction and optimization. This paper compares the proposed MSMPC to the conventional SIMPC and MWMPC in steady state, transient state performance, and anti‐disturbance performance. The effectiveness of the proposed MSMPC is demonstrated through simulation and experimentation.https://doi.org/10.1049/pel2.12408grid‐connected inverterLCL filtermodel predictive controlmultivariable sequential control |
| spellingShingle | Hui Zhang Ran Tao Zhiliang Li Xiao Zhang Zhixun Ma Multivariable sequential model predictive control of LCL‐type grid connected inverter IET Power Electronics grid‐connected inverter LCL filter model predictive control multivariable sequential control |
| title | Multivariable sequential model predictive control of LCL‐type grid connected inverter |
| title_full | Multivariable sequential model predictive control of LCL‐type grid connected inverter |
| title_fullStr | Multivariable sequential model predictive control of LCL‐type grid connected inverter |
| title_full_unstemmed | Multivariable sequential model predictive control of LCL‐type grid connected inverter |
| title_short | Multivariable sequential model predictive control of LCL‐type grid connected inverter |
| title_sort | multivariable sequential model predictive control of lcl type grid connected inverter |
| topic | grid‐connected inverter LCL filter model predictive control multivariable sequential control |
| url | https://doi.org/10.1049/pel2.12408 |
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