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