Research data for PhD thesis "Wind Turbine Load Control and Estimation: Advancements by Coordinate Transformations"

This dataset supports the doctoral research <strong>“Wind Turbine Load Control and Estimation: Advancements by Coordinate Transformations,”</strong> which aims to advance state-of-the-art wind turbine structural load mitigation and estimation by exploiting coordinate transformations in control and estimation frameworks. The research is methodological and computational, focusing on the development, analysis, and evaluation of advanced convex economic model predictive control (CEMPC), quasi-linear parameter-varying MPC, periodic load estimation, and modulation–demodulation control techniques for mitigating tower and blade fatigue loads while maintaining optimal power production. Data were generated through numerical simulations and computational experiments using dynamic wind turbine models, including transformed representations in power, energy, and demodulated coordinates, rather than through field or experimental measurements. The dataset consists of simulation input data, model parameters, control and estimation algorithm implementations, and processed simulation outputs that underpin the analyses and results presented in the four core studies of the thesis

本数据集支撑博士学位论文<strong>“风力发电机组载荷控制与估计：基于坐标变换的技术进展”</strong>，该研究旨在通过在控制与估计框架中应用坐标变换技术，提升风力发电机组结构载荷抑制与载荷估计领域的前沿技术水平。本研究属于方法论与计算类研究，聚焦于先进凸经济模型预测控制（convex economic model predictive control, CEMPC）、准线性变参数模型预测控制（quasi-linear parameter-varying MPC）、周期性载荷估计以及调制-解调控制技术的开发、分析与验证，目标是在维持最优功率输出的同时，抑制塔筒与叶片的疲劳载荷。本数据集的数据并非通过现场实测或实验测量获取，而是基于动态风力发电机组模型，通过数值仿真与计算实验生成，其中包含功率坐标、能量坐标以及解调坐标下的变换形式数据。本数据集涵盖仿真输入数据、模型参数、控制与估计算法实现代码，以及经过预处理的仿真输出结果，这些数据支撑了该博士论文四项核心研究的分析与结论。