Combined Wind Research Articles

Numerical Analysis and Modeling of a Semi-Submersible Floating Wind Turbine Platform with Large Amplitude Motions Subjected to Extreme Wind and Wave Loads

The objective of this study is to predict the large amplitude motions of floating wind turbine platforms and to emphasize the significance of nonlinear forces when these platforms are subjected to combined wind and wave loads. The analysis utilizes the 5 MW OC4 semi-submersible model. First, we couple the OpenFAST v3.1.0 with SIMDYN, validate the effectiveness of the coupled program, and highlight the considerable impact of nonlinearity on the results, particularly in relation to the heave and pitch motions of offshore wind platforms under extreme environmental conditions. We then discuss the primary reasons for this phenomenon. Ultimately, this study proposes an optimized model aimed at mitigating the nonlinear effects associated with such conditions.

Control of nonlinear offshore platforms using semi-active tuned mass damper inerter under combined wave and wind loads

Control of nonlinear offshore platforms using semi-active tuned mass damper inerter under combined wave and wind loads

Design and comparative analysis of mooring systems for a combined wind and wave energy system at intermediate water depth

Design and comparative analysis of mooring systems for a combined wind and wave energy system at intermediate water depth

Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements

Offshore wind turbine structures experience combined wind and wave loading during their lifetime, and the cyclic characteristics of these loads significantly impact the fatigue life of the support structure. Continuous monitoring of stress-related quantities, such as strain time histories at hotspot locations of the structure, can help to estimate the fatigue life. However, sparse measurements from the substructure necessitate using a digital twin as a tool for structural health monitoring by creating a virtual model. This virtual model relies on real-time input loads for virtual sensing and stress-related quantities prediction. However, the exact loading on these structural systems is often unknown or estimated with considerable uncertainty. This paper implements a recursive window-based Bayesian estimator for input load estimation and virtual sensing of acceleration and strain time history at unmeasured critical locations using output-only measurements. The estimator is numerically and experimentally validated in the context of input load estimation for an offshore wind turbine, and the results are compared with a traditional augmented Kalman filter and a linear regression approach for the quasi-static component of loading. The method is first demonstrated through a numerical study using a finite element model of a 6 MW offshore wind turbine, where input wind load time histories are accurately estimated. Then, the framework is applied to the real data measured from an offshore wind turbine in the North Sea. The study demonstrates the window-based Bayesian input estimator as a promising tool for the digital twinning of offshore wind turbines, demonstrating superior accuracy in input load estimations and full response predictions compared to the augmented Kalman filter and linear regression methods without the constraint of collocated measurements at input load locations.

Fatigue analysis of offshore wind turbines with soil-structure interaction and various pile types

Monopile offshore wind turbines (OWTs) are susceptible to fatigue damage, and time domain fatigue analysis is time-consuming and unsuitable for simulating various loading conditions. In this study, frequency domain fatigue analysis of OWTs with soil-structure interaction (SSI) and different monopile designs (the conventional monopile, monopiles with an extension, a wheel, and four spoiler ribs) is conducted. First, detailed 3D finite element models of OWTs are developed. The tower, monopile, and soil are modelled using solid elements, and SSI is accounted for using the Mohr-Coulomb failure criterion and surface-to-surface contact. Next, the power spectral densities of wind and wave loads on the tower and monopile are derived. Subsequently, the fatigue damages of the OWTs are evaluated under individual and combined wind and wave loads. The results show that damage increases with wave height, peaking at the rated wind speed and wave frequency close to the first frequency of the OWTs. Wind-induced damage is significantly greater than wave-induced damage, and the combined wind and wave loads produce more damage than the sum of the individual loads. Additionally, alternative monopile designs, particularly the monopile with a wheel, can reduce fatigue damage, though stress concentration effects on the wheel and ribs require careful consideration.

Optimization of shafting and excitation dual damping controller for combined pumped storage and wind power system

The combined pumped storage and wind power system (CPSWPS) generates the problem of low-frequency oscillations, which leads to complex dynamic characteristics and regulation control. Thus, this paper presents an optimization of the shafting and excitation dual damping controller (SEDDC) for CPSWPS. Firstly, detailed equations of the CPSWPS are constructed. Then, a novel shafting damping controller (SDC) and SEDDC are designed and applied in CPSWPS. Furthermore, an improved sparrow search algorithm that combines random walk and golden sine strategy (RGSSA) is proposed. Finally, an optimization method of SEDDC is presented, and its robustness and universality are verified. The results indicate that the designed SDC has more regulation advantages than traditional rotor-side damping controller, and SEDDC can effectively enhance the damping characteristics of CPSWPS subsystems. Compared to traditional optimization algorithms and sparrow search algorithm variants, the proposed RGSSA has a faster convergence speed and better search results. Optimization of SEDDC significantly improves the damping performance of the CPSWPS and enhances the regulation quality of the unit speed in pumped storage power stations and wind power stations. The SEDDC optimized by RGSSA is more effective in low-frequency oscillation suppression than conventional optimization algorithm, which has high robustness and universality under multi-condition disturbance.

Modal Parameter Identification of Jacket-Type Offshore Wind Turbines Under Operating Conditions

Operational modal analysis (OMA) is essential for long-term health monitoring of offshore wind turbines (OWTs), helping identifying changes in structural dynamic characteristics. OMA has been applied under parked or idle states for OWTs, assuming a linear and time-invariant dynamic system subjected to white noise excitations. The impact of complex operating environmental conditions on structural modal identification therefore requires systematic investigation. This paper studies the applicability of OMA based on covariance-driven stochastic subspace identification (SSI-COV) under various non-white noise excitations, using a DTU 10 MW jacket OWT model as a basis for a case study. Then, a scaled (1:75) 10 MW jacket OWT model test is used for the verification. For pure wave conditions, it is found that accurate identification for the first and second FA/SS modes can be achieved with significant wave energy. Under pure wind excitations, the unsteady servo control behavior leads to significant identification errors. The combined wind and wave actions further complicate the picture, leading to more scattered identification errors. The SSI-COV based modal identification method is suggested to be reliably applied for wind speeds larger than the rated speed and with sufficient wave energy. In addition, this method is found to perform better with larger misalignment of wind and wave directions. This study provides valuable insights in relation to the engineering applications of in situ modal identification techniques under operating conditions in real OWT projects.

Assessment of cooling capacity of chimney-enhanced cross-ventilation systems for kindergartens in African cities

This study proposed a novel approach for naturally ventilated buildings to address the challenges of rising temperatures and increased urban heat island effect in African cities. Existing research often overlooks the performance of combined wind and buoyancy-driven systems in the context of climate change. This research introduced a novel chimney-enhanced cross-ventilation configuration that effectively combined both wind and buoyancy effects for optimal performance. By conducting CFD simulations and detailed building energy simulations, the study aimed to quantify the contributions of these driving forces to assess the performance of the proposed innovative ventilation approach in various urban settings, and analyze its adaptability to future climate scenarios. The cross-ventilation system showed superior performance to a single-sided ventilation solution with identical opening areas. The proposed solution achieved airflow rates up to 20 times higher than that of the single-sided alternative, even in urban environments shielded by tall buildings, due to its ability to effectively harness both wind and stack effects. Consequently, this allowed an improvement in thermal comfort, shown by the higher fraction of occupied time within the thermal comfort range, in comparison with single-sided ventilation. Furthermore, the cross-ventilation system could significantly decrease energy use by mechanical cooling systems by up to 31 %, when compared to the single-sided solution. Finally, the use of night cooling further increased energy savings, and significantly reduced peak mechanical cooling thermal loads. Overall, the chimney-enhanced cross-ventilation system is a promising solution for improving indoor environmental quality and energy efficiency in buildings in African cities, since it is particularly well-suited for the climate change-induced challenges in that continent. The findings of this study can inform the design and implementation of sustainable building practices, promoting the adoption of natural ventilation strategies to mitigate the impacts of climate change.

Dynamic response and power performance of a combined semi-submersible floating wind turbine and point absorber wave energy converter array

With the global demand for renewable energy rising, offshore renewable energy development has gained more attention. The combination of wind and wave energy is a new trend. Ensuring stability in combined systems is crucial for efficiency of absorbing energy. A novel combined wind and wave energy system with a semi-submersible floating wind turbine (FWT) and an array of six torus-shaped point absorber wave energy converters (WECs) is proposed. The dynamic response of combined system is investigated using 3D potential flow theory by comparing to the original system. The effects of power take-off (PTO) damping, WEC float shape and seasonal variation on the dynamic response and power performance of combined system are studied. The results show that the addition of WEC array improves the stability and power production of combined system. Meanwhile, the total power of combined system is approximately 2.5%–6.5 % higher than that of original system. PTO damping mainly affects the heave motion of combined system. As PTO damping increases, the first peak of mean power of WEC array shifts towards the long period, while the second peak of that shifts towards the short period. The conical-bottom WEC generates the most power compared to the flat-bottom WEC, hemispherical-bottom WEC and concave-bottom WEC. The combined system generates the most power in winter, and the total annual electricity output can be up to 2.99 × 104 MWh.

Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques

Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plant located in Italy and used the relevant climate series to determine the quantity of energy produced. We forecast the quantity of energy as well as income through machine learning techniques and more traditional statistical and econometric models. We evaluate the results by splitting the dataset into estimation windows and test windows, and using a backtesting technique. In particular, we found evidence that ML regression techniques outperform results obtained with traditional econometric models. Regarding the models used to achieve this goal, the objective is not to propose original models but to verify the effectiveness of the most recent machine learning models for this important application, and to compare them with more classic linear regression techniques.

Deep learning methods for modeling infrasound transmission loss in the middle atmosphere

Accurate modeling of infrasound transmission losses (TLs) is essential to assess the performance of the global International Monitoring System (IMS) infrasound network. Among existing propagation modeling tools, parabolic equation method (PE) enables TLs to be finely modeled, but its computational cost does not allow exploration of a large parameter space for operational monitoring applications. To reduce computation times, Brissaud et al. (2022) explored the potential of convolutional neural networks (CNNs) trained on a large set of regionally simulated wavefields (>1000 km from the source) to predict TLs with negligible computation times compared to PE simulations. However, this new method shows difficulties in upwind conditions, especially at low frequencies, and causal issues with winds at large distances from the source affecting ground TLs close to the source. In this study, we have developed an optimized CNN network designed to minimize prediction errors while predicting TLs from globally simulated combined temperature and wind fields spanning over propagation ranges of 4000 km. Our approach enhances the previously proposed one by implementing key optimizations that improve the overall architecture performances. The implemented model predicts TLs with an average error of 20 dB in the whole frequency band (0.1-4 Hz) and explored realistic atmospheric scenarios.

Temperature rise of high-speed bearing in gearbox of 5 MW wind turbine based on Bayesian-LightGBM and improved PSO-SVM troubleshooting

5MW wind turbine gearbox high-speed bearing temperature rise failure is one of the important factors affecting the stable operation of the wind turbine, accurate prediction and timely diagnosis can effectively improve the efficiency of the wind turbine. In this paper, a combined modelling wind turbine gearbox high-speed bearing temperature rise prediction method based on Bayesian-LightGBM and improved PSO-SVM is proposed with a 5 MW wind turbine as the research object. Firstly, the initial dimensionality reduction of SCADA data is performed by sparse random projection matrix, which reduces the redundant data. Secondly, feature selection is performed on the remaining data using Bayesian-LightGBM to identify 13 key input feature parameters. Then, the hyperparameters of the PSO algorithm are optimised using Bayesian algorithm and further, the optimised PSO algorithm is applied to identify the SVM parameters. Finally, a simulation experiment platform is established based on MATLAB to verify the temperature rise of high-speed bearings in gearboxes of wind turbines by example calculation and comparative analysis. The results show that the model established in this paper is effective, the prediction results are accurate and the performance is stable, and then compared with the algorithms such as PSO-SVM, SVM, BP, etc, the coefficient of determination of the algorithm is greater than 0.994 in both the training set and the test set, and the average decision percentage error is around 1.88% in both.

Higher Onshore Wind Energy Potentials Revealed by Kilometer‐Scale Atmospheric Modeling

AbstractReliable and highly resolved information about onshore wind energy potential (WEP) is essential for expanding renewable energy to eventually achieve carbon neutrality. In this pilot study, simulated 60 m wind speeds (ws60m) from a km‐scale, convection‐permitting 3.3 km‐resolution ICON‐LAM simulation and often‐used 31 km‐resolution ERA5 reanalysis are evaluated at 18 weather masts. The estimated ICON‐LAM and ERA5 WEPs are then compared using an innovative approach with 1.8 million eligible wind turbine placements over southern Africa. Results show ERA5 underestimates ws60m with a Mean Error (ME) of −1.8 m s−1 (−27%). In contrast, ICON‐LAM shows a ME of −0.1 m s−1 (−1.8%), resulting in a much higher average WEP by 48% compared to ERA5. A combined Global Wind Atlas‐ERA5 product reduces the ws60m underestimation of ERA5 to −0.3 m s−1 (−4.7%), but shows a similar average WEP compared to ERA5 resulting from the WEP spatial heterogeneity.

Ocean wave and wind power harnessed by hybrid nanogenerator

Charge self-shuttling improves power density for combined wind and water wave energy generators.

Co-Design of a Wind–Hydrogen System: The Effect of Varying Wind Turbine Types on Techno-Economic Parameters

Green hydrogen is crucial for achieving climate neutrality and replacing fossil fuels in processes that are hard to electrify. Wind farms producing electricity and hydrogen can help mitigate stress on electricity grids and enable new markets for operators. While optimizing wind farms for electricity production is well-established, optimizing combined wind–hydrogen systems is a relatively new research field. This study examines the potential profit of wind–hydrogen systems by conducting a case study of an onshore wind farm near the North Sea. Varying turbine types from high wind-speed turbines (with high annual energy production) to low wind-speed turbines (with high full-load hours) are examined. Findings indicate that in a combined hydrogen system, the low wind-speed turbines, which are sub-optimal for mere electricity production, yield lower levelized costs of hydrogen at a higher hydrogen production. Although high wind-speed turbines generate higher profits under current market conditions, at high hydrogen prices and low electricity prices, low wind-speed turbines can yield higher total profit at this site. Therefore, an integrated optimization approach of wind–hydrogen systems can, in certain cases, lead to better results compared to an isolated, sequential optimization of each individual system.

Optimization of multiple rotational inertia double tuned mass damper for offshore wind turbines under earthquake, wind, and wave loads

This study introduces a framework for designing an optimal multiple rotational inertia double tuned mass damper (MRIDTMD) with multiple tuning frequencies to effectively mitigate vibrations in offshore wind turbines (OWTs) subjected to combined wind, wave, and seismic loads. The framework comprises a coupled numerical model of an OWT with an MRIDTMD, an intelligent optimization algorithm, and parallel computing technology. First, coupled governing equations of motion for an OWT with an MRIDTMD under seismic conditions are derived based on multibody dynamics and fully coupled analysis theories in FAST v8. Subsequently, an MRIDTMD submodule is developed and integrated into FAST v8 to establish a coupled analysis model for an OWT with MRIDTMDs using an updated simulation tool. Furthermore, an intelligent optimization algorithm and parallel computing technology are introduced to establish the framework, and the MRIDTMDs are optimized. Moreover, the efficiency of the optimized MRIDTMD is assessed based on observed reductions in the OWT responses under combined seismic cases. Subsequently, comparisons with an optimized multiple tuned mass damper (MTMD) and a parametric study are conducted. The effectiveness and robustness of the optimized MRIDTMD and the improved mitigation effects of the MRIDTMD compared with the MTMD owing to the additional inerter are proved.

Numerical modelling and design of cold-formed steel battens subject to pull-out failures under bushfire conditions

Thin and high strength cold-formed steel (CFS) battens and claddings serve as non-combustible elements in roof and wall cladding systems, aligning with the recommendations of Australian bushfire standards. However, they are susceptible to significant damage from heat and strong winds experienced during bushfire events, particularly in the form of batten pull-out failures. Such failures may occur at the screw connections between cladding and battens, or battens and trusses/rafters. However, pull-out failures often occur in the thinner CFS battens, when the screw fastener pulls out of the batten’s top flange, leading to the loss of entire cladding and compromising the integrity of the building envelope. This study used finite element modelling of a range of CFS batten configurations to investigate their pull-out failures under combined wind action and bushfire conditions. Finite element models were developed to simulate the pull-out failures at elevated temperatures, validated using experimental results and used in a parametric study to investigate the effects of relevant parameters. Using the results, this study has proposed suitable design equations for predicting the pull-out capacities of CFS battens at elevated temperatures. Engineers can use them to design bushfire safe CFS cladding systems.

“Assessment of combined wind and wave energy in European coastal waters using satellite altimetry.”

In this study, the combined wind and wave energy potential assessment is presented for various locations in European coastal waters. The objective is to investigate the feasibility of satellite altimetry-based assessments of combined wind/wave renewable energy potential on the European shelf. The study is motivated by the potential reduction in energy supply variability by combining wind and wave. The method consists of using a homogenized multi-mission altimeter database available by the European Space Agency Sea State Climate Change Initiative (Sea_State_cci) that comprises 26 years of data, from January 1991 to December 2018, that allows extending the time range and spatial coverage to estimate site wind and wave power densities. An empirical model is used to estimate the wave energy period from the altimeter Ku band significant wave height and radar backscatter coefficient required to compute the wave power density. The results show that wind/wave energy is relatively correlated in the Mediterranean but not in the North Atlantic sites studied. Thus, the Western North Atlantic sites are the most adequate places for wind and wave farms, from the point of view of combined exploitation.The different characteristics of the studied sites show some correspondence between variability and mean wave power, which is an essential input to a marine renewable energy strategy in any jurisdiction. The level of overall variability decreases with an increase in mean wave power, related to the higher power swell waves are not highly correlated with the local wind.

Wind Speed Forecasting Based on Phase Space Reconstruction and a Novel Optimization Algorithm

The wind power generation capacity is increasing rapidly every year. There needs to be a corresponding development in the management of wind power. Accurate wind speed forecasting is essential for a wind power management system. However, it is not easy to forecast wind speed precisely since wind speed time series data are usually nonlinear and fluctuant. This paper proposes a novel combined wind speed forecasting model that based on PSR (phase space reconstruction), NNCT (no negative constraint theory) and a novel GPSOGA (a hybrid optimization algorithm that combines global elite opposition-based learning strategy, particle swarm optimization and the genetic algorithm) optimization algorithm. SSA (singular spectrum analysis) is firstly applied to decompose the original wind speed time series into IMFs (intrinsic mode functions). Then, PSR is employed to reconstruct the intrinsic mode functions into input and output vectors of the forecasting model. A combined forecasting model is proposed that contains a CBP (cascade back propagation network), RNN (recurrent neural network), GRU (gated recurrent unit), and CNNRNN (convolutional neural network combined with recurrent neural network). The NNCT strategy is used to combine the output of the four predictors, and a new optimization algorithm is proposed to find the optimal combination parameters. In order to validate the performance of the proposed algorithm, we compare the forecasting results of the proposed algorithm with different models on four datasets. The experimental results demonstrate that the forecasting performance of the proposed algorithm is better than other comparison models in terms of different indicators. The DM (Diebold–Mariano) test, Akaike’s information criterion and the Nash–Sutcliffe efficiency coefficient confirm that the proposed algorithm outperforms the comparison models.

Using Wind Energy to Carry out Basketball Special Physical Fitness Training—Calculation and Research on Aerodynamic Efficiency of Integrated Wind Turbine in Stadiums and Gymnasiums

This study investigates the feasibility of using wind energy for basketball special physical fitness training by combining wind turbines in stadiums and gyms. The investigation focuses on measuring the aerodynamic efficiencies of the combined wind turbine systems. It starts with an analysis of wind energy utilization for fitness training, which signifies the potential benefit of harnessing the renewable energy in sports facilities. The investigation into the aerodynamic efficiency of the combined wind turbine systems, considers factors such as speed of wind, size of turbine, and energy output. Numerical simulation and measurements are conducted to determine the optimal design and placement of wind turbines in stadiums and gymnasiums. The results demonstrate the potential of wind energy to supplement traditional power sources in sports facilities, and provide sustainable energy solutions while boosting fitness and environmental awareness. The study reveals that with an average speed of 8 m/s, a properly integrated wind turbine system in a stadium or gymnasium can generate approximately 11-23% of the facility's electricity demand. This translates to an annual energy production of 510-1200 kWh per turbine, depending on the specific location and design considerations. The aerodynamic efficacy of the integrated wind turbine system is estimated to be around 40-50%, indicating a significant potential for energy savings and environmental impact reduction.