Software-in-the-Loop Combined Reinforcement Learning Method for Dynamic Response Analysis of FOWTs

Abstract

Floating offshore wind turbines (FOWTs) still face many challenges on how to better predict the dynamic responses. Artificial intelligence (AI) brings a new solution to overcome these challenges with intelligent strategies. A new AI technology-based method, named SADA, is proposed in this paper for the prediction of dynamic responses of FOWTs. Firstly, the methodology of SADA is introduced with the selection of Key Disciplinary Parameters (KDPs). The AI module in SADA was built in a coupled aero-hydro-servo-elastic in-house program DARwind and the policy decision is provided by the machine learning algorithms deep deterministic policy gradient (DDPG). Secondly, a set of basin experimental results of a Hywind Spar-type FOWT were employed to train the AI module. SADA weights KDPs by DDPG algorithms' actor network and changes their values according to the training feedback of 6DOF motions of Hywind platform through comparing the DARwind simulation results and that of experimental data. Many other dynamic responses that cannot be measured in basin experiment could be predicted in higher accuracy with this intelligent DARwind. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform's motions can be predicted by AI-based DARwind with higher accuracy, for example the maximum error of surge motion is reduced by 21%. This proposed SADA method takes advantage of numerical-experimental method and the machine learning method, which brings a new and promising solution for overcoming the handicap impeding direct use of traditional basin experimental technology in FOWTs design.

Analysis and Revision of Torque Formula for Hydro-viscous Clutch

“…In fact, it is very difficult to accurately measure each oil film thickness of HVC, since the clearance between friction plates is so small. However, if the structure of HVC is improved from the ordinary type to the synchronous type (as is shown in Figure 13) [5,6], the oil film thickness can be indirectly calculated according to the piston force analysis. to the synchronous type (as is shown in Figure 13) [5,6], the oil film thickness can be indirectly calculated according to the piston force analysis.…”

“…However, if the structure of HVC is improved from the ordinary type to the synchronous type (as is shown in Figure 13) [5,6], the oil film thickness can be indirectly calculated according to the piston force analysis. to the synchronous type (as is shown in Figure 13) [5,6], the oil film thickness can be indirectly calculated according to the piston force analysis. Figure 13 also shows the force modeling of the piston in the synchronous type of HVC.…”

“…In recent years, HVC has been used in super large engineering applications, such as tunnel boring machines, and higher requirements for its control accuracy are put forward. More and more artificial intelligence learning algorithms are used in the control strategy of HVC [4][5][6][7].…”

A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed

“…Furthermore, the proposed method significantly reduced the mechanical load on the blade root and effectively restrained platform pitching motion when compared to gain scheduling PI individual pitch control. A novel AI-based method [94], termed SADA (Software-in-the-Loop Combined Artificial Intelligence Method for Dynamic Response Analysis of FOWTs), was introduced for predicting the dynamic responses of FOWTs. The SADA methodology involves selecting key disciplinary parameters (KDPs), and its AI module is integrated into the in-house program DARwind, which encompasses a coupled aero-hydro-servo-elastic system.…”

A novel multi-dimensional reliability approach for floating wind turbines under power production conditions

“…Offshore wind turbine design has undoubtedly been vital for effective wind energy generation. FOWTs are continuously exposed to stochastic wind and wave loads; therefore, extreme load characteristics are crucial for FOWT operation and design in such conditions (Graf et al, 2018;Li et al, 2018;Xu et al, 2018;Xu et al, 2020;Chen et al, 2021;.…”