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.

风能是解决能源危机和环境污染最有前景的解决方案之一，因此得到了迅速发展。风力涡轮机功率曲线 (WTPC) 在风能评估、涡轮机状态监测和电网调度中发挥着重要作用。然而，WTPC建模存在两个挑战：模型选择和参数优化。已经开发了许多参数和非参数模型来表征 WTPC，但由于复杂的风况，没有一个模型总能表现最佳。本文考虑模型参数结构简单、可解释性强，构建了一套参数化的WTPC模型以适应风况的变化，并根据其性能从该模型中选出最优候选模型。至于参数优化的过程，提出了一种考虑WTPC建模不对称误差特性的新型损失函数，并设计了一种充分利用灰狼优化器和回溯搜索算法优点的混合智能优化方法GWO-BSA。最后，提出了一种结合候选模型集、基于误差特征的损失函数和 GWO-BSA 的新型 WTPC 建模策略，以获得更好的功率曲线。实验结果表明：（1）GWO-BSA比单一优化算法具有更快的收敛速度和更高的优化精度；(2) 所提出的基于误差特征的损失函数比常用的对称损失函数具有更好的性能；(3) 与一些流行的基于人工智能的模型相比，