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- Publisher Website: 10.1016/j.epsr.2024.110815
- Scopus: eid_2-s2.0-85196813973
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Article: Data-driven adaptive predictive frequency control for power systems with unknown and time-varying inertia
| Title | Data-driven adaptive predictive frequency control for power systems with unknown and time-varying inertia |
|---|---|
| Authors | |
| Keywords | Behavioural system theory Data-driven adaptive predictive frequency control Moving horizon estimation Time-varying inertia |
| Issue Date | 1-Sep-2024 |
| Publisher | Elsevier |
| Citation | Electric Power Systems Research, 2024, v. 234 How to Cite? |
| Abstract | This paper proposes a novel data-based adaptive predictive frequency control method for multi-area power systems with unknown and time-varying inertia. Firstly, a data-based representation is built and updated at each instant based on behavioural system theory by using historical input–output data, where a moving horizon estimation method is used to deal with the unknown time-varying inertia issue adaptively. Then, the optimal frequency control signal is computed by solving an optimization problem under the framework of data-based predictive control. Simulation results on a power system with three control areas demonstrate the effectiveness of the proposed method. |
| Persistent Identifier | http://hdl.handle.net/10722/360681 |
| ISSN | 2023 Impact Factor: 3.3 2023 SCImago Journal Rankings: 1.029 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Zhao, Yunzheng | - |
| dc.contributor.author | Liu, Tao | - |
| dc.contributor.author | Hill, David J. | - |
| dc.date.accessioned | 2025-09-13T00:35:43Z | - |
| dc.date.available | 2025-09-13T00:35:43Z | - |
| dc.date.issued | 2024-09-01 | - |
| dc.identifier.citation | Electric Power Systems Research, 2024, v. 234 | - |
| dc.identifier.issn | 0378-7796 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/360681 | - |
| dc.description.abstract | <p>This paper proposes a novel data-based adaptive predictive frequency control method for multi-area power systems with unknown and time-varying inertia. Firstly, a data-based representation is built and updated at each instant based on behavioural system theory by using historical input–output data, where a moving horizon estimation method is used to deal with the unknown time-varying inertia issue adaptively. Then, the optimal frequency control signal is computed by solving an optimization problem under the framework of data-based predictive control. Simulation results on a power system with three control areas demonstrate the effectiveness of the proposed method.</p> | - |
| dc.language | eng | - |
| dc.publisher | Elsevier | - |
| dc.relation.ispartof | Electric Power Systems Research | - |
| dc.subject | Behavioural system theory | - |
| dc.subject | Data-driven adaptive predictive frequency control | - |
| dc.subject | Moving horizon estimation | - |
| dc.subject | Time-varying inertia | - |
| dc.title | Data-driven adaptive predictive frequency control for power systems with unknown and time-varying inertia | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1016/j.epsr.2024.110815 | - |
| dc.identifier.scopus | eid_2-s2.0-85196813973 | - |
| dc.identifier.volume | 234 | - |
| dc.identifier.eissn | 1873-2046 | - |
| dc.identifier.issnl | 0378-7796 | - |