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- Publisher Website: 10.1109/ISGT-Europe47291.2020.9248882
- Scopus: eid_2-s2.0-85097331306
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Conference Paper: A Revisit Towards Optimal Control Modes for AI Enabled Wind Power Plants
| Title | A Revisit Towards Optimal Control Modes for AI Enabled Wind Power Plants |
|---|---|
| Authors | |
| Keywords | wind power plant overall power maximization set-point tracking online optimal control |
| Issue Date | 2020 |
| Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800214 |
| Citation | 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, 26-28 October 2020, p. 804-808 How to Cite? |
| Abstract | Active power regulation of a wind power plant in presence of wake interactions is a challenging issue in industry. To address this issue, this paper is aimed to optimally coordinate all wind turbines in the wind power plant by using artificial intelligence (AI) enabled control schemes. Two typical operating modes (i.e. maximum power point tracking (MPPT) mode and set point tracking (SPT) mode) are revisited by the proposed control framework. Distinguished from the conventional optimization based methods, online control can be achieved via the proposed framework as it has merits on 1) bypassing the time-consuming optimization with high nonlinearity and non-convexity; and 2) high computational efficiency with simple matrix calculation for MPPT and SPT. Simulation results verify the effectiveness of the proposed control framework, which suggests a high potential of AI edge computing in the future wind power management. |
| Persistent Identifier | http://hdl.handle.net/10722/306885 |
| ISBN |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lyu, X | - |
| dc.contributor.author | Jia, Y | - |
| dc.contributor.author | Liu, T | - |
| dc.date.accessioned | 2021-10-22T07:41:00Z | - |
| dc.date.available | 2021-10-22T07:41:00Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.citation | 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, 26-28 October 2020, p. 804-808 | - |
| dc.identifier.isbn | 9781728171012 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/306885 | - |
| dc.description.abstract | Active power regulation of a wind power plant in presence of wake interactions is a challenging issue in industry. To address this issue, this paper is aimed to optimally coordinate all wind turbines in the wind power plant by using artificial intelligence (AI) enabled control schemes. Two typical operating modes (i.e. maximum power point tracking (MPPT) mode and set point tracking (SPT) mode) are revisited by the proposed control framework. Distinguished from the conventional optimization based methods, online control can be achieved via the proposed framework as it has merits on 1) bypassing the time-consuming optimization with high nonlinearity and non-convexity; and 2) high computational efficiency with simple matrix calculation for MPPT and SPT. Simulation results verify the effectiveness of the proposed control framework, which suggests a high potential of AI edge computing in the future wind power management. | - |
| dc.language | eng | - |
| dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800214 | - |
| dc.relation.ispartof | IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe) | - |
| dc.rights | IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe). Copyright © IEEE. | - |
| dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
| dc.subject | wind power plant | - |
| dc.subject | overall power maximization | - |
| dc.subject | set-point tracking | - |
| dc.subject | online optimal control | - |
| dc.title | A Revisit Towards Optimal Control Modes for AI Enabled Wind Power Plants | - |
| dc.type | Conference_Paper | - |
| dc.identifier.email | Liu, T: taoliu@eee.hku.hk | - |
| dc.identifier.authority | Liu, T=rp02045 | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1109/ISGT-Europe47291.2020.9248882 | - |
| dc.identifier.scopus | eid_2-s2.0-85097331306 | - |
| dc.identifier.hkuros | 328516 | - |
| dc.identifier.spage | 804 | - |
| dc.identifier.epage | 808 | - |
| dc.publisher.place | United States | - |