Forecasting extreme offshore wind turbine responses with a novel transformation technique

A novel method for the distribution and extrapolation of extreme sea state parameters

Distributed real-time hybrid simulation method for dynamic response evaluation of floating wind turbines

The beginning times of storms within a day are often required for stochastic modeling purposes and for studies on plant growth. This study investigated the variation in frequency distributions of storm initiation time (SI time) within a day due to elevation changes and month. Actual storms without 24 h constraints were used, as opposed to simply bursts of precipitation within a 24 h period. Two methods of characterizing and quantifying these distributions were investigated: kernel density estimation (KDE), and a mixed doubly truncated normal (MDTN) distribution method using nonlinear curve fitting subject to bounds on the parameters. Parameter estimation methods were also investigated. Data came from the raingauge network maintained by the USDA-ARS at the Reynolds Creek Experimental Watershed in southwest Idaho over a 982 m elevation gradient. There was no difference between frequency distributions of SI time with elevation or precipitation type over the 147 km2 study area. There was a significant shift in SI-time distribution from earlier in the morning in late fall and winter to early afternoon during the spring and summer. Both the KDE and MDTN methods accurately characterized the observed histograms, which included near-uniform, single-mode, and bimodal distributions. The MDTN method worked well most of the time (~97%) but can have mathematical convergence problems. An SI-time analysis based on a 24 h cycle starting at 2100 h yielded a better fit to the data than a standard day defined to start at midnight using the MDTN method. Exploratory regressions between the four MDTN parameters and several readily available independent variables did not yield consistent or significant predictive relationships. Cumulative distributions for either the KDE or MDTN methods are suggested for stochastic modeling purposes on a monthly basis, as they represent well observed histograms of SI times. The KDE method is suggested for use because of its simplicity in ungauged areas as long as neighboring data are available. The methods have utility for characterizing time variation of other weather elements.

Extreme Response Prediction of a Multi-Column Tension-Leg-Type Floating Wind Turbine under Operating Conditions

The significant motion response of semi-submersible floating offshore wind turbines in marine environments poses challenges for platform stability control and power generation efficiency. Traditional stabilization methods demonstrate limitations in response speed and control effectiveness under complex sea conditions. This paper develops a comprehensive 12-degree-of-freedom coupled dynamics model that integrates platform motion, tower flexibility, rotor dynamics, and gyroscopic stabilization systems. By incorporating the gyro-stabilization system, the stability control of the platform’s pitch and roll motions is significantly improved. The model employs the Kane method, which comprehensively considers the coupling effects between the wind turbine, platform, and gyro, providing a higher precision dynamic response simulation. Based on this model, an innovative PSO-optimized fuzzy control strategy is proposed, utilizing intelligent particle swarm optimization algorithms to adjust controller parameters for optimal performance under various environmental conditions. Simulation results demonstrate that the proposed active control strategy offers significant advantages, achieving up to 37.56% pitch angle RMS vibration suppression and 44.23% tower-top displacement RMS vibration suppression under still water conditions, with peak suppression rates of 21.45% and 27.77% respectively under normal sea conditions, while maintaining 39.04% and 24.58% peak suppression rates in extreme sea conditions. In random sea conditions, the peak suppression rates remain at 38.16% and 17.83% respectively. This study significantly improves platform stability and structural load characteristics through the modeling of the gyro-stabilization system and the use of PSO-optimized fuzzy control, providing a reliable technical solution for floating wind turbine applications in complex marine environments.

We propose a generalization of our previous kernel density estimation (KDE) method for estimating luminosity functions (LFs). This new upgrade further extends the application scope of our KDE method, making it a very flexible approach that is suitable to deal with most bivariate LF calculation problems. From the mathematical point of view, usually the LF calculation can be abstracted as a density estimation problem in the bounded domain of . We use the transformation-reflection KDE method () to solve the problem, and introduce an approximate method () based on one-dimensional KDE to deal with the small sample size case. In practical applications, the different versions of LF estimators can be flexibly chosen according to the Kolmogorov–Smirnov test criterion. Based on 200 simulated samples, we find that for both cases of dividing or not dividing redshift bins, especially for the latter, our method performs significantly better than the traditional binning method . Moreover, with the increase of sample size n, our LF estimator converges to the true LF remarkably faster than . To implement our method, we have developed a public, open-source Python toolkit, called kdeLF. With the support of kdeLF, our KDE method is expected to be a competitive alternative to existing nonparametric estimators, due to its high accuracy and excellent stability. kdeLF is available online at GitHub with further extensive documentation available.

The combined wind and wave concept semisubmersible wind energy and flap-type wave energy converter was developed in the EU FP7 project MARINA Platform. It consists of a four-column semisubmersible with a 5-MW wind turbine placed on top of the central column and three flap-type wave energy converters on top of three pontoons that connect the four columns. Numerical and experimental studies have been performed to demonstrate the functionality and the survivability of the combined concept. In extreme conditions, both wind turbine and wave energy converters are set in a protection mode which reduces the dynamic loads and responses. In this article, different methods for predicting long-term (50-year) extreme responses considering the wind and wave conditions at two given European offshore sites are carried out, and structural response quantities are calculated, compared and presented. The full long-term analysis was performed and regarded as the reference method, and the corresponding results are compared with the modified environmental contour method and the environmental contour method. The response quantities studied here are the axial forces and bending moments of the semisubmersible wind energy and flap-type wave energy converter, including those of the wind turbine (blade, shaft and tower), arms of the flap-type wave energy converters and mooring lines, as well as the platform motion in 6 degrees of freedom. The extreme responses that are dominated by the aerodynamic loadings are effectively calculated either by the full long-term analysis or the modified environmental contour method. Compared to the full long-term analysis, the environmental contour method gives an under-prediction of the long-term extreme responses of quantities related to the wind turbine (e.g. internal loads of blades and tower). For the extreme responses that are dominated by the hydrodynamic loadings, all the three methods provide similar results.

The first step in improving traffic safety is identifying hazardous situations. Based on traffic accidents’ data, identifying hazardous situations in roads and the network is possible. However, in small areas such as intersections, especially in maneuvers resolution, identifying hazardous situations is impossible using accident’s data. In this paper, time-to-collision (TTC) as a traffic conflict indicator and kernel density estimation (KDE) method have been used to identify hazardous situations. KDE applies smooth function on critical TTC value events, this surface indicates risk changes. The maximum quantity of this function represents the hazardous situations. To assess and implement the presented method, left-turn on unsignalized intersection has been chosen. TTC data are determined by automated video analysis and coordinating TTC smaller than threshold value was used as input data in KDE method. Hazardous situations have been identified and the factors that caused them have been recognized using these results and performing safety audit. Two countermeasures are proposed to improve safety of left-turn in study location.

Extreme value prediction of the load-effect responses of complex offshore structures such as the floating wind turbine (FWT) is crucial in ultimate limit state (ULS) design. This paper considers two cases to understand the feasibility of the bivariate correction on the extreme load and motion responses of a 10-MW semi-submersible type FWT. The empirical anchor tension force and surge motion used in this study are obtained from the FAST simulation tool (developed by the National Renewable Energy Laboratory) with the load cases stimulated at under-rated, rated and above rated speeds. Then, the bivariate correction method is applied to model FWT extreme response for a 5-years return period prediction with a 95% confidence interval (CI), based on just 2 min short response record. The proposed methodology permits accurate correction of the bivariate extreme value in case of, for example, corrupted measurement sensor data. Based on the proposed novel method’s performance, it is concluded that the bivariate correction method can offer better robust and precise bivariate predictions of coupled surge motion and anchor tension of the FWT.

Short-term extreme value prediction for the structural responses of the IEA 15 MW offshore wind turbine under extreme environmental conditions

We propose a flexible method for estimating luminosity functions (LFs) based on kernel density estimation (KDE), the most popular nonparametric density estimation approach developed in modern statistics, to overcome issues surrounding the binning of LFs. One challenge in applying KDE to LFs is how to treat the boundary bias problem, as astronomical surveys usually obtain truncated samples predominantly due to the flux-density limits of surveys. We use two solutions, the transformation KDE method () and the transformation–reflection KDE method () to reduce the boundary bias. We develop a new likelihood cross-validation criterion for selecting optimal bandwidths, based on which the posterior probability distribution of the bandwidth and transformation parameters forandare derived within a Markov Chain Monte Carlo sampling procedure. The simulation result shows thatandperform better than the traditional binning method, especially in the sparse data regime around the flux limit of a survey or at the bright end of the LF. To further improve the performance of our KDE methods, we develop the transformation–reflection adaptive KDE approach (). Monte Carlo simulations suggest that it has good stability and reliability in performance, and is around an order of magnitude more accurate than using the binning method. By applying our adaptive KDE method to a quasar sample, we find that it achieves estimates comparable to the rigorous determination in a previous work, while making far fewer assumptions about the LF. The KDE method we develop has the advantages of both parametric and nonparametric methods.

1. Although the home range is a fundamental ecological concept, there is considerable debate over how it is best measured. There is a substantial literature concerning the precision and accuracy of all commonly used home range estimation methods; however, there has been considerably less work concerning how estimates vary with sampling regime, and how this affects statistical inferences. 2. We propose a new procedure, based on a variance components analysis using generalized mixed effects models to examine how estimates vary with sampling regime. 3. To demonstrate the method we analyse data from one study of 32 individually marked roe deer and another study of 21 individually marked kestrels. We subsampled these data to simulate increasingly less intense sampling regimes, and compared the performance of two kernel density estimation (KDE) methods, of the minimum convex polygon (MCP) and of the bivariate ellipse methods. 4. Variation between individuals and study areas contributed most to the total variance in home range size. Contrary to recent concerns over reliability, both KDE methods were remarkably efficient, robust and unbiased: 10 fixes per month, if collected over a standardized number of days, were sufficient for accurate estimates of home range size. However, the commonly used 95% isopleth should be avoided; we recommend using isopleths between 90 and 50%. 5. Using the same number of fixes does not guarantee unbiased home range estimates: statistical inferences differ with the number of days sampled, even if using KDE methods. 6. The MCP method was highly inefficient and results were subject to considerable and unpredictable biases. The bivariate ellipse was not the most reliable method at low sample sizes. 7. We conclude that effort should be directed at marking more individuals monitored over long periods at the expense of the sampling rate per individual. Statistical results are reliable only if the whole sampling regime is standardized. We derive practical guidelines for field studies and data analysis.

Comparative analysis of the spatial analysis methods for hotspot identification

A novel design approach for estimation of extreme load responses of a 10-MW floating semi-submersible type wind turbine

The objective of this paper is to carry out response analyses of eight floating wind turbines and compare them together; this is something that is not seen in previous research papers. From this perspective, this paper will compare the response offset regarding the motions of the six degrees of freedom of the respective floating wind turbines. The applied forces that these analyses consider come mainly from constant wind forces applied on the wind turbines’ blades, as well as forces from waves and currents. Different response offset values are considered and compared regarding the different constant wind speeds, as well as the different velocities of waves and currents. This paper also provides various innovative references related to floating wind turbine analyses and software. Validation and verification studies are left for future work due to the complexity of the data provided in this paper. However, some comparisons are made between the obtained analysis results and some external references. The mentioned external references unfortunately have floating wind turbines with different wind and wave environmental conditions, power capacities, and dimensional characteristics. The results of the constant wind dynamic analysis of the eight floating wind turbines studied in this paper have shown that the maximum surge, sway, and heave response offset corresponds to the DTU Spar 1 floating wind turbine. The maximum roll and yaw response offset corresponds to the INO-WINDMOOR floating wind turbine. The maximum pitch response offset corresponds to the WindFloat floating wind turbine. The aero-hydro-servo-elastic method was used in the Sima software to run the analyses. It is a time-domain dynamic analysis, and it uses meters [m] and degrees [°] to describe the response offsets of the different floating wind support structures studied in this paper.