Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer

Wind energy is one of the most promising solutions to energy crisis and environmental pollution, so it is being developed rapidly. Wind turbine power curves (WTPCs) play an important role in wind energy assessment, turbine condition monitoring, and power grid dispatching. However, there are two challenges in WTPC modeling: model selection and parameter optimization. Many parametric and non-parametric models have been developed to characterize WTPCs, but none can always perform the best due to the complex wind regimes. In this paper, considering the simple structure and interpretability of model parameters, a set of parametric WTPC models is constructed to adapt to the variability of wind regimes, and the optimal candidate will be selected from the set according to their performances. As to the process of parameter optimization, a novel loss function, which considers the asymmetric error characteristic of WTPC modeling, is proposed, and a hybrid intelligent optimization method named GWO-BSA, which makes full use of the advantages of grey wolf optimizer and backtracking search algorithm, is designed. Finally, a novel WTPC modeling strategy, which combines the candidate model set, error characteristic-based loss function, and GWO-BSA, is proposed to obtain better power curves. Experimental results show that (1) GWO-BSA shows faster convergence speed and higher optimization accuracy than single optimization algorithms; (2) the proposed error characteristic-based loss function has better performance than the commonly used symmetric loss functions; and (3) compared with some popular artificial intelligence-based models, the designed WTPC modeling strategy produces better WTPCs under different wind regimes.

Automated summary

This study introduces a new parametric wind turbine power curve model to adapt to complex wind regimes, and designs a hybrid intelligent optimization method to optimize model parameters. Experimental results demonstrate that this method exhibits faster convergence speed and higher optimization accuracy.