Multi-objective deep reinforcement learning for optimal design of wind turbine blade. (February 2023)

• Record Type: Journal Article
• Authors: Wang, Zheng; Zeng, Tiansheng; Chu, Xuening; Xue, Deyi
• Is Part Of: Renewable energy. Volume 203(2023)
• Page Start: 854
• Page End: 869
• Publication Date: 2023-02
• Subjects: Wind turbine design -- Multi-objective optimization -- Deep reinforcement learning -- Deterministic policy gradient -- Stochastic policy gradient; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2023.01.003
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The design of a wind turbine blade is a typical complex multi-objective optimization problem, mostly solved by evolutionary algorithms. However, these methods are not effective due to limitations such as inaccurate solutions on Pareto fronts for high-dimensional problems, numerous iterations and low adaptability to problems with similar conditions. To address these issues, two multi-objective deep reinforcement learning models are introduced in this paper from an entirely different perspective. The first model, namely the multi-objective deep deterministic policy gradient (MO-DDPG), extends the existing popular reinforcement learning algorithm DDPG to multi-objective optimization problems by integrating various techniques including modeling of constraints on high-dimensional spaces and generation of Pareto solutions. The second model, namely the multi-objective deep stochastic policy gradient (MO-DSPG), further improves the MO-DDPG by incorporating a random neural network called restricted Boltzmann machine (RBM). An adaptive random agent is trained to transform multiple deterministic policies into an optimal stochastic policy. In addition, neighborhood-based parameter transfer strategy is applied to MO-DSPG in the model training phase to reduce the computation time. Experiments showed that the aerodynamic performance of the blades is improved by both the MO-DDPG and the MO-DSPG models with the hypervolume increasing an average of 6.67% and 9.25% respectively, compared with the state-of-art models. The computational efficiency of MO-DSPG is improved by using the parameter transfer strategy, with its runtime reduced to 72.52% compared with state-of-art models. Graphical abstract: Image 1