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Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations
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Yin, Xiuxing , Zhao, Xiaowei, Lin, Jin and Karcanias, Aris (2020) Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations. Energy, 202 . 117739. doi:10.1016/j.energy.2020.117739 ISSN 0360-5442.
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WRAP-Reliability-aware-multi-objective-predictive-heuristic-optimizations-Zhao-2020.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2239Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.energy.2020.117739
Abstract
In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
Library of Congress Subject Headings (LCSH): | Wind power plants, Wind power plants -- Simulation methods, Wind power, Wind power -- Mathematical models, Predictive control , Machine learning | |||||||||
Journal or Publication Title: | Energy | |||||||||
Publisher: | Elsevier Ltd | |||||||||
ISSN: | 0360-5442 | |||||||||
Official Date: | 1 July 2020 | |||||||||
Dates: |
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Volume: | 202 | |||||||||
Article Number: | 117739 | |||||||||
DOI: | 10.1016/j.energy.2020.117739 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 4 May 2020 | |||||||||
Date of first compliant Open Access: | 1 May 2021 | |||||||||
RIOXX Funder/Project Grant: |
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