Nikolay Krasimirov Dimitrov

Probabilistic Meteorological Characterization for Turbine Loads

Beyond the existing, limited IEC prescription to describe fatigue loads on wind turbines, we look towards probabilistic characterization of the loads via analogous characterization of the atmospheric flow, particularly for todays taller turbines with rotors well above the atmospheric surface layer. Based on both data from multiple sites as well as theoretical bases from boundary-layer meteorology and atmospheric turbulence, we offer probabilistic descriptions of shear and turbulence intensity, elucidating the connection of each to the other as well as to atmospheric stability and terrain. These are used as input to loads calculation, and with a statistical loads output description, they allow for improved design and loads calculations.

The Science of Making Torque from Wind 2014 (TORQUE 2014)

The 186 papers in this volume constitute the proceedings of the fifth Science of Making Torque from Wind conference, which is organized by the European Academy of Wind Energy (EAWE, www.eawe.eu). The conference, also called Torque 2014, is held at the Technical University of Denmark (DTU) 17–20 June 2014. The EAWE conference series started in 2004 in Delft, the Netherlands. In 2007 it was held in Copenhagen, in 2010 in Heraklion, Greece, and then in 2012 in Oldenburg, Germany. The global yearly production of electrical energy by wind turbines has grown approximately by 25% annually over the last couple of decades and covers now 2–3% of the global electrical power consumption. In order to make a significant impact on one of the large challenges of our time, namely global warming, the growth has to continue for a decade or two yet. This in turn requires research and education in wind turbine aerodynamics and wind resources, the two topics which are the main subjects of this conference. Similar to the growth in electrical power production by wind is the growth in scientific papers about wind energy. Over the last decade the number of papers has also grown by about 25% annually, and many research based companies all over the world are founded. Hence, the wind energy research community is rapidly expanding and the Torque conference series offers a good opportunity to meet and exchange ideas. We hope that the Torque 2014 will heighten the quality of the wind energy research, while the participants will enjoy each others company in Copenhagen. Many people have been involved in producing the Torque 2014 proceedings. The work by more than two hundred reviewers ensuring the quality of the papers is greatly appreciated. The timely evaluation and coordination of the reviews would not have been possible without the work of sixteen section editors all from DTU Wind Energy: Christian Bak, Andreas Bechmann, Ferhat Bingol, Ebba Dellwik, Nikolay Dimitrov, Gregor Giebel, Martin O L Hansen, Dorte Juul Jensen, Gunner Larsen, Helge Aagaard Madsen, Jakob Mann, Anand Natarajan, Ole Rathmann, Ameya Sathe, Jens Norkaer Sorensen and Niels Norkaer Sorensen, who are all co-editors of these proceedings. The resources provided by the Center for Computational Wind Turbine Aerodynamics and Atmospheric Turbulence funded by the Danish Council for Strategic Research grant no. 09-067216 and the Danish Ministry of Science, Innovation and Higher Education Technology and Production, grant no. 11- 117018 are gratefully acknowledged. We are also immensely indebted to the very responsive help and support from the editorial team at IoP, especially Sarah Toms and Anete Ashton, during the reviewing process of these proceedings. We are looking forward to meeting you in Copenhagen and also to Torque 2016, which will take place at the Technical University of Munich, Germany. Roskilde, Denmark, June 2014 Ebba Dellwik, Ameya Sathe and Jakob Mann Technical University of Denmark

Bayesian inference model for fatigue life of laminated composites

A probabilistic model for estimating the fatigue life of laminated composite plates is developed. The model is based on lamina-level input data, making it possible to predict fatigue properties for a wide range of laminate configurations. Model parameters are estimated by Bayesian inference. The reference data used consists of constant-amplitude cycle test results for four laminates with different layup configurations. The paper describes the modeling techniques and the parameter estimation procedure, supported by an illustrative application.

Mapping Wind Farm Loads and Power Production - A Case Study on Horns Rev 1

This paper describes the development of a wind turbine (WT) component lifetime fatigue load variation map within an offshore wind farm. A case study on the offshore wind farm Horns Rev I is conducted with this purpose, by quantifying wake effects using the Dynamic Wake Meandering (DWM) method, which has previously been validated based on CFD, Lidar and full scale load measurements. Fully coupled aeroelastic load simulations using turbulent wind conditions are conducted for all wind directions and mean wind speeds between cut-in and cut-out using site specific turbulence level measurements. Based on the mean wind speed and direction distribution, the representative 20-year lifetime fatigue loads are calculated. It is found that the heaviest loaded WT is not the same when looking at blade root, tower top or tower base components. The blade loads are mainly dominated by the wake situations above rated wind speed and the highest loaded blades are in the easternmost row as the dominating wind direction is from West. Regarding the tower components, the highest loaded WTs are also located towards the eastern central location. The turbines with highest power production are, not surprisingly, the ones facing a free sector towards west and south. The power production results of few turbines are compared with SCADA data. The results of this paper are expected to have significance for operation and maintenance planning, where the schedules for inspection and service activities can be adjusted to the requirements arising from the varying fatigue levels. Furthermore, the results can be used in the context of remaining fatigue lifetime assessment and planning of decommissioning.

Probabilistic fatigue life of balsa cored sandwich composites subjected to transverse shear

A probabilistic fatigue life model for end-grain balsa cored sandwich composites subjected to transverse shear is proposed. The model is calibrated to measured three-point bending constant-amplitude fatigue test data using the maximum likelihood method. Some possible applications of the probabilistic model are obtaining characteristic S–N curves corresponding to a given survival probability, and calibrating partial safety factors for material fatigue. The latter is demonstrated by a calibration performed using reliability analysis with the first-order reliability method. The measured variance in balsa shear properties, for both static strength and fatigue failure, is higher than the variance normally observed in the properties for fiber-reinforced polymer composite laminates. This could be attributed to the fact that end-grain balsa wood is the product of a naturally occurring growth process, which cannot be controlled to the same extent as an industrial manufacturing processes. The large variance in the probabilistic model for fatigue life is reflected in the corresponding calibrated partial safety factors, which are higher than the factors usually associated with synthetic materials such as fiber-reinforced laminates.

Reducing Wind Turbine Load Simulation Uncertainties by Means of a Constrained Gaussian Turbulence Field

We demonstrate a method for incorporating wind measurements from multiple-point scanning lidars into the turbulence fields serving as input to wind turbine load simulations. The measurement values are included in the analysis by applying constraints to randomly generated turbulence fields. A numerical study shows the application of the constrained turbulence method to load simulations on a 10MW wind turbine model, using two example lidar patterns – a 5-point pattern forming a square with a central point, and a circular one. Based on the results of this study, we assess the influence of applying the proposed method on the statistical uncertainty in wind turbine extreme and fatigue loads.

Probabilistic Modelling of Fatigue Life of Composite Laminates Using Bayesian Inference

A probabilistic model for estimating the fatigue life of laminated composite plates subjected to constant-amplitude or variable-amplitude loading is developed. The model is based on lamina-level input data, making it possible to predict fatigue properties for a wide range of laminate configurations. Model parameters are estimated by Bayesian inference. The reference data used consists of constant-amplitude fatigue test results for a multi-directional laminate subjected to seven different load ratios. The paper describes the modelling techniques and the parameter estimation procedure, supported by an illustrative application and result assessment.

Risk-based approach for rational categorization of damage observations from wind turbine blade inspections

This study provides a risk-based assessment procedure for wind turbine blade damages observed during visual inspections. A decision model is presented which identifies the cost-optimal intervention based on assessed damage severity. This is achieved by defining procedures for model-based estimation of probability of consequences for specific failure modes, and by analysing the costs associated with different scenarios for intervention. In addition, the procedure provides a risk-based, quantitative interpretation of damage severity categories used in wind turbine blade inspection practices. In the present paper, the workflow and example categorization are demonstrated on two specific faults in wind turbine blades: leading edge erosion damage, and trailing edge crack.

Assessing the Utility of Early Warning Systems for Detecting Failures in Major Wind Turbine Components

This paper provides enhancements to normal behaviour models for monitoring major wind turbine components and a methodology to assess the monitoring system reliability based on SCADA data and decision analysis. Typically, these monitoring systems are based on fully data-driven regression of damage sensitive-parameters. Firstly, the problem of selecting suitable inputs for building a temperature model of operating main bearings is addressed, based on a sensitivity study. This shows that the dimensionality of the dataset can be greatly reduced while reaching sufficient prediction accuracy. Subsequently, performance quantities are derived from a statistical description of the prediction error and used as input to a decision analysis. Two distinct intervention policies, replacement and repair, are compared in terms of expected utility. The aim of this study is to provide a method to quantify the benefit of implementing the online system from an economic risk perspective. Under the realistic hypotheses made, the numerical example shows for instance that replacement is not convenient compared to repair.

Wind turbine site-specific load estimation using artificial neural networks calibrated by means of high-fidelity load simulations

Previous studies have suggested the use of reduced-order models calibrated by means of high-fidelity load simulations as means for computationally inexpensive wind turbine load assessments; the so far best performing surrogate modelling approach in terms of balance between accuracy and computational cost has been the polynomial chaos expansion (PCE). Regarding the growing interest in advanced machine learning applications, the potential of using Artificial Neural-Network (ANN) based surrogate models for improved simplified load assessment is investigated in this study. Different ANN model architectures have been evaluated and compared to other types of surrogate models (PCE and quadratic response surface). The results show that a feedforward neural network with two hidden layers and 11 neurons per layer, trained with the Levenberg Marquardt backpropagation algorithm is able to estimate blade root flapwise damage-equivalent loads (DEL) more accurately and faster than a PCE trained on the same data set. Further research will focus on further model improvements by applying different training techniques, as well as expanding the work with more load components.