Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations. (1st July 2020)

• Record Type: Journal Article
• Authors: Yin, Xiuxing; Zhao, Xiaowei; Lin, Jin; Karcanias, Aris
• Is Part Of: Energy. Volume 202(2020)
• Publication Date: 2020-07-01
• Subjects: Wind farm reliability -- Predictive control -- Relevance vector machine -- Evolutionary algorithms; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117739
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: A data-driven relevance vector machine wind farm model is proposed. A reliability aware multi-objective predictive control framework is designed. The effectiveness of the above control framework is verified.