Wind turbine fatigue estimation is based on time\uffe2\uff80\uff90consuming Monte Carlo simulations for various wind conditions, followed by cycle\uffe2\uff80\uff90counting procedures and the application of engineering damage models. The outputs of the fatigue simulations are large in volume and of high dimensionality, as they typically consist of estimates on finite\uffe2\uff80\uff90element computational meshes. The strain and stress tensor time series, which are the primary quantities of interest when considering the problem of fatigue estimation, are dictated by complex vibration characteristics due to the coupled effect of aerodynamics, structural dynamics, geometrically non\uffe2\uff80\uff90linear mechanics, and control. A Variational Auto\uffe2\uff80\uff90Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross\uffe2\uff80\uff90section of a simulated wind turbine blade. The VAE is conditioned on historical data that correspond to coarse wind\uffe2\uff80\uff90field measurement statistics, such as mean hub\uffe2\uff80\uff90height wind speed, standard deviation of hub\uffe2\uff80\uff90height wind speed and shear exponent. In the absence of direct measurements of structural loads, the proposed technique finds applications in making long\uffe2\uff80\uff90term probabilistic deterioration predictions from historical Supervisory, Control, and Data Acquisition (SCADA) data, while capturing the inherent aleatoric uncertainty due to the incomplete information on strain time series of the wind turbine structure, when only SCADA data statistics are available.
", "keywords": ["CVAE", "deep generative models", "high dimensional simulation outputs", "uncertainty quantification", "TJ807-830", "blade root fatigue", "conditional variational autoencoder", "SCADA", "wind turbine blade", "7. Clean energy", "Renewable energy sources"]}, "links": [{"href": "https://doi.org/10.1002/we.2621"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Wind%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/we.2621", "name": "item", "description": "10.1002/we.2621", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/we.2621"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-11T00:00:00Z"}}, {"id": "10.1007/978-3-319-67443-8_22", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-22T16:14:33Z", "type": "Report", "created": "2017-10-12", "title": "Operational Damage Localization of Wind Turbine Blades", "description": "Open AccessISBN:978-3-319-67443-8", "keywords": ["Damage localization", "Wind turbines; Operational conditions; Damage localization; Principal component analysis; Mode shape curvatures", "Wind turbines", "Principal component analysis", "Mode shape curvatures", "02 engineering and technology", "Operational conditions", "7. Clean energy", "0201 civil engineering"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/978-3-319-67443-8"}, {"href": "http://link.springer.com/content/pdf/10.1007/978-3-319-67443-8_22"}, {"href": "https://doi.org/10.1007/978-3-319-67443-8_22"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/978-3-319-67443-8_22", "name": "item", "description": "10.1007/978-3-319-67443-8_22", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/978-3-319-67443-8_22"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-13T00:00:00Z"}}, {"id": "10.1016/j.proeng.2017.09.285", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:16:18Z", "type": "Journal Article", "created": "2017-09-12", "title": "A substructure approach for fatigue assessment on wind turbine support structures using output-only measurements", "description": "Open AccessFatigue constitutes a major and highly-uncertain safety-related factor for wind turbines. In order to ensure a reliable fatigue assessment of such structures, it is essential that stress predictions be based on the actual structural behaviour. The response identification of operational wind turbines in a global framework constitutes a challenging problem due to the uncertainties associated with the variability of the wind loading and the dynamics of the rotor. In reducing these uncertainties, this study proposes a substructuring approach, which abolishes the need for modelling the intricate and time-varying dynamics of the rotor. Instead, response prediction is performed on a substructure model of the tower and the effect of wind loads and servo dynamics is accounted for via the estimated interface forces at the top of the support structure. The application is based on synthetic vibration data generated via the FAST software and an output-only Bayesian filter employing the structural model of the support structure. The effectiveness of the proposed framework is presented in terms of fatigue damage estimates at different locations on the tower.", "keywords": ["Wind turbine; Dynamic substructuring; Input-state estimation; Response identification; Fatigue damage", "Response identification", "Input-state estimation", "Dynamic substructuring", "Fatigue damage", "02 engineering and technology", "Wind turbine", "7. Clean energy", "0201 civil engineering"]}, "links": [{"href": "https://doi.org/10.1016/j.proeng.2017.09.285"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Procedia%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.proeng.2017.09.285", "name": "item", "description": "10.1016/j.proeng.2017.09.285", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.proeng.2017.09.285"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-01-01T00:00:00Z"}}, {"id": "10.1016/j.proeng.2017.09.509", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:16:18Z", "type": "Journal Article", "created": "2017-09-12", "title": "Fatigue assessment of a wind turbine blade when output from multiple aero-elastic simulators are available", "description": "Open AccessAero-elasticity is a term that refers to the interaction between the aerodynamic, inertial and elastic loads when a structure is exposed to fluid flow such as turbulent wind inflow. Various commercial and research-based simulators are available to compute the wind turbine aero-elastic loads. These aero-elastic simulators are of varying complexity and might bear different underlying assumptions, pertaining to physics, mathematical and computational formulations. However, currently established practice dictates that the adopted aero-elastic simulators are verified and validated on the basis of measurements from test wind turbines. As a result, it is generally hard to establish one simulator as superior to another in terms of their predicted output. The objective in this paper is to statistically aggregate the fatigue load on a wind turbine blade when simultaneous simulations are performed using multiple simulators. The simulators of the wind turbine blade are of varying fidelity, and uncertainty in the modelling and assumptions on the model inputs are implicitly included, and taken into account in the statistical analysis. The main concept followed here is that rather than treating the output of the simulators as individual information sources, we consider them as part of an ensemble, which can be clustered and then aggregated to predict the \u201cmost likely\u201d fatigue load, hence reducing the inherent model-form uncertainty.", "keywords": ["Finite elements", "Uncertainty", "Wind turbine; Aeroelasticity; Uncertainty; Fatigue; Ensemble Aggregation; Data fusion; Finite elements; Machine learning", "02 engineering and technology", "Data fusion", "7. Clean energy", "01 natural sciences", "0201 civil engineering", "Ensemble Aggregation", "Machine learning", "Aeroelasticity", "0101 mathematics", "Wind turbine", "Fatigue"]}, "links": [{"href": "https://doi.org/10.1016/j.proeng.2017.09.509"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Procedia%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.proeng.2017.09.509", "name": "item", "description": "10.1016/j.proeng.2017.09.509", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.proeng.2017.09.509"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-01-01T00:00:00Z"}}, {"id": "10.1016/j.renene.2021.02.003", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:16:19Z", "type": "Journal Article", "created": "2020-11-05", "title": "Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression", "description": "We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input random variables to output DELs in the blades and towers of the up-wind and wake-affected wind turbines. A sensitivity analysis based on the hyperparameters of the GPR and Kullback-Leibler divergence is conducted to assess the effect of different input on the obtained DELs. We provide qualitative recommendations for a minimal set of necessary and sufficient input random variables to minimize the error in the DEL predictions on the wake-affected wind turbine. Extensive simulations are performed comprising different random variables, including wind speed, turbulence intensity, shear exponent and inflow horizontal skewness. Furthermore, we include random variables related to the blades lift and drag coefficients with direct impact on the rotor aerodynamic induction, which governs the evolution and transport of the meandering wake. In addition, different spacing between the wind turbines and W\u00f6hler exponents for calculation of DELs are considered. The maximum prediction normalized mean squared error, obtained in the tower base DELs in the fore-aft direction of the wake affected wind turbine, is less than 4%. In the case of the blade root DELs, the overall prediction error is less than 1%. The proposed scheme promotes utilization of sparse structural monitoring (loads) measurements for improving diagnostics on wake-affected turbines.
", "keywords": ["bepress|Physical Sciences and Mathematics|Physics|Engineering Physics", "engrXiv|Engineering|Risk Analysis", "engrXiv|Engineering|Other Engineering", "bepress|Engineering", "engrXiv|Engineering|Mechanical Engineering|Fluid Mechanics", "bepress|Engineering|Mechanical Engineering", "engrXiv|Engineering|Mechanical Engineering", "bepress|Engineering|Mechanical Engineering|Applied Mechanics", "Gaussian Process Regression", "02 engineering and technology", "7. Clean energy", "Virtual sensing", "wind turbine", "bepress|Engineering|Computational Engineering", "engrXiv|Engineering|Civil and Environmental Engineering", "0202 electrical engineering", " electronic engineering", " information engineering", "uncertainty", "Fatigue", "wake", "engrXiv|Engineering|Civil and Environmental Engineering|Structural Engineering", "Uncertainty", "engrXiv|Engineering|Mechanical Engineering|Applied Mechanics", "Bayesian Calibration", "engrXiv|Engineering|Engineering Physics", "bepress|Engineering|Risk Analysis", "engrXiv|Engineering", "bepress|Engineering|Civil and Environmental Engineering", "engrXiv|Engineering|Computational Engineering", "Wake", "bepress|Engineering|Aerospace Engineering|Aerodynamics and Fluid Mechanics", "bepress|Engineering|Civil and Environmental Engineering|Structural Engineering", "fatigue", "bepress|Engineering|Other Engineering", "Sensitivity analysis", "Wind turbine", "Bayesian Gaussian process regression"]}, "links": [{"href": "https://doi.org/10.1016/j.renene.2021.02.003"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Renewable%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.renene.2021.02.003", "name": "item", "description": "10.1016/j.renene.2021.02.003", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.renene.2021.02.003"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-05T00:00:00Z"}}, {"id": "10.1201/9781351174664-382", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-22T16:18:17Z", "type": "Report", "created": "2018-12-15", "title": "Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data", "description": "Open AccessSafety and Reliability \u2013 Safe Societies in a Changing World", "keywords": ["0202 electrical engineering", " electronic engineering", " information engineering", "02 engineering and technology", "7. Clean energy", "Decision tree learning for big data on wind turbines"]}, "links": [{"href": "https://doi.org/10.1201/9781351174664-382"}, {"rel": "self", "type": "application/geo+json", "title": "10.1201/9781351174664-382", "name": "item", "description": "10.1201/9781351174664-382", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1201/9781351174664-382"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-15T00:00:00Z"}}, {"id": "10.2749/vancouver.2017.0809", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:19:10Z", "type": "Journal Article", "created": "2021-02-09", "title": "Surrogate Modelling For Fatigue Damage of Wind-Turbine Blades Using Polynomial Chaos Expansions and Non-Negative Matrix Factorization", "description": "A computational approach for the estimation of fatigue degradation of composite wind turbine blades by means of time domain aero-servo-elastic simulations is proposed. Wind turbine blades are subjected throughout their lifetime to highly stochastic loading. Fatigue damage of the composite reinforcement of the wind turbine blades has been identified early on in the wind turbine design practice as a factor driving design. A simple fatigue accumulation model is utilized for the spar cap reinforcement of a wind-turbine blade. Non-Negative Matrix Factorization (NMF) for the damage accumulation random field is used for dimensionality reduction. An approximate computationally efficient model, relying on Polynomial Chaos Expansion (PCE) of the damage state with respect to probabilistically modelled mean wind and turbulence intensity is derived. The framework is exemplified in a case-study of a 1.5MW wind turbine.
", "keywords": ["thin walled composite beam", "Composite fatigue", "long term fatigue assessment", "Aero-elastic simulation", "Gram-Schmidt PCE", "Wind Turbine", "0202 electrical engineering", " electronic engineering", " information engineering", "fatigue degradation", "NMF", "02 engineering and technology", "7. Clean energy", "0201 civil engineering"], "contacts": [{"organization": "Mylonas, Charilaos, Abdallah, Imad, Chatzi, Eleni,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.2749/vancouver.2017.0809"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IABSE%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2749/vancouver.2017.0809", "name": "item", "description": "10.2749/vancouver.2017.0809", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2749/vancouver.2017.0809"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-01-01T00:00:00Z"}}, {"id": "10.3390/s17040720", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-22T16:19:29Z", "type": "Journal Article", "created": "2017-03-30", "title": "A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach", "description": "The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe\uffe2\uff80\uff99s renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool.
", "keywords": ["operational spectrum", "Chemical technology", "time varying autoregressive moving average (TV-ARMA) models", "Operational spectrum", "wind turbines; data-driven framework; uncertainty propagation; operational spectrum; time varying autoregressive moving average (TV-ARMA) models; polynomial chaos expansion (PCE)", "Data-driven framework", "uncertainty propagation", "TP1-1185", "02 engineering and technology", "7. Clean energy", "data-driven framework", "Article", "0201 civil engineering", "13. Climate action", "wind turbines", "polynomial chaos expansion (PCE)", "Uncertainty propagation", "Wind turbines", "Data-driven framework; Operational spectrum; Polynomial chaos expansion (PCE); Time varying autoregressive moving average (TV-ARMA) models; Uncertainty propagation; Wind turbines", "Polynomial chaos expansion (PCE)", "Time varying autoregressive moving average (TV-ARMA) models"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/4/720/pdf"}, {"href": "https://doi.org/10.3390/s17040720"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s17040720", "name": "item", "description": "10.3390/s17040720", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s17040720"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-30T00:00:00Z"}}, {"id": "10.3929/ethz-b-000482924", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:19:35Z", "type": "Journal Article", "created": "2021-05-03", "title": "Vibration\u2010based monitoring of a small\u2010scale wind turbine blade under varying climate and operational conditions. Part II: A numerical benchmark", "description": "Open AccessThis paper constitutes the numerical companion of the experimental work on vibration-based monitoring of a small-scale wind turbine (WT) blade. In this second part, a numerical benchmark is established for condition assessmentof a Windspot 3.5-kW WT blade. The aim is to supplement the companion experimental work with a physical model exposed to diverse operational conditions, loading scenarios, and damage patterns that are not easily explorable and controllable in the laboratory. To this end, a finite element (FE) model of the considered blade is developed and subjected to a number of artificial damage scenarios, which are dynamically tested under both environmental and operational variability. The paper offers a detailed description of the numerical benchmark and the underlying assumptions, as well as the spectrum of operational conditions, the measured quantities, and the wind load model. Finally, we provide an overview and demonstration of the stand-alone application for time history analysis and generation of synthetic vibration data, which is made available via an open-access code in Sonkyo-Benchmark repository", "keywords": ["condition assessment", "structural health monitoring", "operational and environmental variability", "13. Climate action", "0202 electrical engineering", " electronic engineering", " information engineering", "wind turbine blade", "02 engineering and technology", "numerical benchmark", "condition assessment; damage detection; numerical benchmark; operational and environmental variability; structural health monitoring; system identification; wind turbine blade", "7. Clean energy", "damage detection", "system identification"]}, "links": [{"href": "https://doi.org/10.3929/ethz-b-000482924"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Structural%20Control%20and%20Health%20Monitoring", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000482924", "name": "item", "description": "10.3929/ethz-b-000482924", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000482924"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-03T00:00:00Z"}}, {"id": "10.5281/zenodo.4896835", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:21:05Z", "type": "Report", "title": "Virtual loads predictions of wake-affected wind turbines: Gaussian process regression and deep neural networks", "description": "Load analysis of wind turbines may be performed either via physics-based models or via direct measurement. On the first case, loads are calculated with aero-elastic models, based on significant assumptions on the mechanical and aeroelastic properties of the structure and the acting forces (wind, wave and control). Otherwise, loads can be directly measured based on a sensor network, which entails increased costs due to installation, maintenance and calibration of sensors and IT infrastructure. These costs can be manageable for a single wind turbine but become substantial on densely instrumented wind farms. In turn, the increased costs negatively affect the levelized cost of energy. This is, in fact, the main reason why stakeholders shy away from applying monitoring technologies in wind farms. To overcome the above challenges, we need an alternative way of estimating the loads which would involve a reduced number of sensors while replicating the actual load measurement scenario. To this end, we propose data-driven models to predict the loads acting on different components of a wind turbine. These models use SCADA, wind inflow and other variables to predict loads in components of interest of a wind turbine. We have already successfully demonstrated this concept in the past on simulated wind turbine Damage Equivalent Loads (DELs) based on Gaussian Process regression [1,2], and on real wind turbine data [3]. In this work, we validate this approach on actual wind turbine data from the Alpha Ventus Wind Farm obtained within the framework of the Research Alpha Ventus (RAVE) project. Two Senvion turbines are selected for this study. One of the wind turbines is used to train and validate a regression model to predict the tower base DELs based on SCADA, wind inflow and other environmental variables. Afterwards, the trained model is used to predict the loads in the second wind turbine. Load prediction is attained with two machine learning methods, the first one based on Gaussian Process Regression (GPR) and the second one based on Artificial Neural Networks (ANN). For the first one, a decision tree is used to separate the different operating modes of the wind turbine (idling, operating and transitioning). The decision tree is built on simple heuristics on a subset of SCADA variables (mean and standard deviation of the rotor RPM and blade pitch angle). Subsequently, a GPR is built for each one of the operating modes. In the second method, the SCADA variables are fed to the ANN after undergoing an initial transformation for data compression and collinearity reduction.", "keywords": ["machine learning", "structural health monitoring", "13. Climate action", "wind turbines", "virtual sensing", "7. Clean energy"], "contacts": [{"organization": "Avenda\u00f1o-Valencia, Luis David, Abdallah, Imad, Venu, Anish, Chatzi, Eleni,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4896835"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4896835", "name": "item", "description": "10.5281/zenodo.4896835", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4896835"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.3474632", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-22T16:21:01Z", "type": "Journal Article", "title": "Probabilistic fault diagnostics using ensemble time-varying decision tree learning", "description": "Probabilistic fault diagnostics using ensemble time-varying decision tree learning on wind turbines. Simulations using Simulink and an embedded FAST aeroelastic model of the 5MW reference wind turbine. Error cases: (1) No Error, (2) Yaw Error, Corrected, (3) Yaw Error, yaw actuator stuck, (4) Pitch angle sensor, stuck at constant value of 5 deg.", "keywords": ["Fault diagnostics", "Machine learning", "Wind turbines", "Decision tree", "7. 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This data results from the optimisation of the blade's pitching kinematics using a genetic algorithm at two tip-speed ratios: 1.5 and 3.2. The aerodynamic forces for all tested individuals and their pitch profiles are shared. A full descripton of the data content and how to use it is given in the readme.txt file. 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