The ever-growing use of wind energy makes necessary the optimization of turbine operations through pitch angle controllers and their maintenance with early fault detection. It is crucial to have accurate and robust models imitating the behavior of wind turbines, especially to predict the generated power as a function of the wind speed. Existing empirical and physics-based models have limitations in capturing the complex relations between the input variables and the power, aggravated by wind variability. Data-driven methods offer new opportunities to enhance wind turbine modeling of large datasets by improving accuracy and efficiency. In this study, we used physics-informed neural networks to reproduce historical data coming from 4 turbines in a wind farm, while imposing certain physical constraints to the model. The developed models for regression of the power, torque, and power coefficient as output variables showed great accuracy for both real data and physical equations governing the system. Lastly, introducing an efficient evidential layer provided uncertainty estimations of the predictions, proved to be consistent with the absolute error, and made possible the definition of a confidence interval in the power curve.

翻译：随着风能利用的持续增长，通过桨距角控制器优化风力发电机运行及其基于早期故障检测的维护变得至关重要。建立准确且鲁棒的模型来模拟风力发电机行为，特别是根据风速预测发电功率，具有关键意义。现有经验模型与物理模型在捕捉输入变量与功率间复杂关系方面存在局限性，而风速变异性进一步加剧了这一问题。基于数据驱动的方法为利用大数据集改进风力发电机建模提供了新机遇，可提升模型的准确性与效率。本研究采用物理信息神经网络对某风电场4台风力发电机的历史数据进行再现，同时向模型施加特定的物理约束。以功率、扭矩和功率系数为输出变量构建的回归模型，在真实数据拟合及系统物理方程满足方面均展现出高精度。最后，引入高效的证据层实现了预测的不确定性估计，该估计与绝对误差保持一致性，并能在功率曲线上定义置信区间。