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Wind turbine gearbox condition monitoring (C&M) is a key technology to promote wind farm maintenance cost reduction and power generation improvement. Existing gearbox C&M methods usually adopt the component‐by‐component modelling approach. Firstly, this approach is inefficient in modelling; secondly, due to the thermal conduction effect, abnormalities in one gearbox component usually affect other components, making it difficult to identify the source of faults. To solve these problems, a normal behaviour model (NBM) combining data adaptive noise reduction and an improved variational auto‐encoder (VAE) is proposed, which can monitor the operational condition of multiple components of one gearbox simultaneously and takes into account the correlation between components when warning of the specific abnormal component. As verified by practical cases, the method balances the modelling accuracy and efficiency of the multi‐component NBM and achieves effective early warning and accurate localization of gearbox abnormalities. The proposed model has lower false alarm and missed alarm rates compared to other single‐component and multi‐component NBMs. [ABSTRACT FROM AUTHOR]

The wind farm flow (WFF) models, which are enabled to predict the power output of downstream, located turbines within a wind farm. A WFF model consists of two main model components: a single wake model and a superposition model. Two WFF models, the Root Sum Square (RSS) superposition model incorporated into the single wake models (e.g. Bastankah and Porté-Agel wake (BPA) model and Jensen wake model), have been extensively applied in engineering. But the WFF models above-mentioned tend to overestimate the power deficit of the whole wind farm. Hence, a novel entrainment wind farm flow (NEWFF) model is proposed in this paper, which is a combination of a modified linear entrainment wake (MLEW) model and the RSS superposition model. Different from the previous linear entrainment wake (LEW) model, the MLEW model developed in this paper considers the impacts of terrain roughness variables on the wake distribution of downstream wind turbines. The MLEW model significantly improves the accuracy of the wake simulation over the advanced BPAW model and JW model, as well as the LEW model, as shown in two cases from the TNO wind tunnel. Finally, several cases of Horns Rev offshore wind farm and Lillgrund offshore wind farm are utilized to validate the NEWFF model. Compared with the latest advanced Zong and Agel superposition wake (ZASW) model and BPA wind farm flow (BPAWFF) model, it has been demonstrated that the prediction results obtained with the NEWFF model exhibit the best agreements with measured power data under full and partial wake conditions. [ABSTRACT FROM AUTHOR]

The wake model for stand-alone turbine is an essential ingredient of wind farm wake research, which is of great concern for optimization of wind turbine layout, power generation simulation, and operation control on a wind farm. One-dimensional wake models (e.g., Jensen model and Frandsen model) based on single conservation theorem were approximately described by top-hat shape, which cannot represent the non-uniform distribution characteristics of wind turbine wake in the crosswind direction accurately and tend to underestimate the velocity deficit. Different from the above-mentioned models, entrainment wake model (EWM) can conserve both of mass and momentum within the control volume, which improves the accuracy of wake simulation to a certain extent. But the top-hat hypothesis limits the wake prediction accuracy of EWM. In this case, a novel two-dimensional EWM is proposed for the first time in this paper. Different from the advanced two-dimensional Bastankah and Porté-Agel wake model and Jensen-Gaussian wake model, the proposed model considers the influence of variable entrainment parameter on wake recovery, of which the key entrainment parameter was considered as a variable that is associated with downstream distance, ambient turbulence, and wake turbulence. The proposed Gaussian EWM has been validated by the data from Garrad Hassan (GH) wind tunnel laboratory experiments, Sexberium wind farm measurements, and the field measurements with lidar in Northwestern China. It has been concluded that the proposed model can predict the wake velocity distribution most accurately and provides the minimal power prediction error under various scenarios. [ABSTRACT FROM AUTHOR]

Multivariable wind turbine power curve model is crucial for the performance degradation evaluation of wind turbine. However, the existing models have low modeling accuracy without considering the wind energy capture difference caused by segment control, and the inputs cannot represent the actual wind energy resources owing to the neglect of the short-term self-dependence of environmental parameters, which makes it difficult to ensure the performance degradation evaluation accuracy of wind turbine. To address these problems, a multivariable wind turbine power curve modeling method considering segment control differences and short-time self-dependence is proposed. First, an abnormal data cleaning method based on timing matching and bidirectional quartile algorithm is proposed to clean the abnormal data. Then, the multivariate wind turbine power curve model based on piece-wise regression of multiple environmental parameters is constructed, and applied to the performance degradation evaluation of wind turbine. The results demonstrated that the proposed abnormal data cleaning method can effectively solve the problem that the data categories of transition region between abnormal and normal data cannot be identified. Meanwhile, the proposed multivariable wind turbine power curve model can effectively improve the modelling accuracy and ensure the performance degradation evaluation accuracy of wind turbine under different wind energy resources. • The timing matching and bidirectional quartile algorithm for abnormal data cleaning. • A multivariable power curve model by piece-wise regression of multiple parameters. • The proposed model considers wind energy capture difference of segment control. • The proposed model includes short-time self-dependence of environmental parameters. • Correct evaluation of wind turbine performance degradation under different resources. [ABSTRACT FROM AUTHOR]

The Reynolds‐averaged Navier–Stokes (RANS) method coupling with the actuator disc model (ADM) is considered as a promising numerical simulation technology of wind turbine wake, and it is widely utilised in the aerodynamics of wind turbines and optimal layout of wind farms. The k−ε turbulence model is widely adopted, among the RANS‐based turbulence models. However, the k−ε turbulence model easily overestimates the turbulence viscosity in the wake, which results in fast recovery of wake velocity and failure in wake forecasting. In addition, ADM with the oversimplified geometrical structure ignores the effects of nacelle and tower on the wind turbine wake, which further lowers the accuracy of wake simulation. Therefore, the numerical simulation of wind turbine wake based on the extended k−ε turbulence model of EI Kasmi coupling with ADM considering nacelle and tower is proposed. Comparing the results of Marchwood Engineering Laboratories (MEL) ABL wind tunnel measurements and TNO wind tunnel experiments, it has been found that the proposed model improves the simulation effect for the near wake and has a certain contribution to the wake prediction accuracy overall. [ABSTRACT FROM AUTHOR]

The wind speed sequences at different spatial positions have a certain spatiotemporal coupling relationship. It is of great significance to analyze the clustering effect of the wind farm(s) and reduce the adverse impact of large-scale wind power integration if we can grasp this relationship at multiple scales. At present, the physical method cannot optimize the time-shifting characteristics in real time, and the research scope is concentrated on the wind farm. The statistical method cannot quantitatively describe the temporal relationship and the speed variation among wind speed sequences at different spatial positions. To solve the above problems, a quantification method of wind speed time-shifting characteristics based on wind process is proposed in this paper. Two evaluation indexes, the delay time and the decay speed, are presented to quantify the time-shifting characteristics. The effectiveness of the proposed method is verified from the perspective of the correlation between wind speed sequences. The time-shifting characteristics of wind speed sequences under the wind farms scale and the wind turbines scale are studied, respectively. The results show that the proposed evaluation method can effectively achieve the quantitative analysis of time-shifting and could improve the results continuously according to the actual wind conditions. Besides, it is suitable for any spatial scale. The calculation results can be directly applied to the wind power system to help obtain the more accurate output of the wind farm. [ABSTRACT FROM AUTHOR]

Accurate modeling of the wind turbine power curve (WTPC) is crucial for calculating the theoretical wind power for the integration of renewable energies and electric vehicles. However, existing WTPC modeling methods cannot simultaneously guarantee high modeling accuracy and efficiency for data samples with a large amount of accumulated abnormal data. To address this problem, this paper presents a WTPC modeling method based on interval extreme probability density, which does not require complicated and time-consuming abnormal data cleaning and can significantly improve the modeling efficiency while guaranteeing high modeling accuracy. To verify the applicability and validity of the proposed method, firstly, WTPC models were constructed using actual operation data from 12 wind turbines in a Chinese wind farm and were compared with the manufacturer's power curve and with WTPC modeling methods based on abnormal data cleaning algorithms. Secondly, the theoretical wind power was calculated and compared with the commonly used manufacturer's power curve. The results demonstrated that the proposed WTPC modeling method has high modeling accuracy and efficiency and can improve the calculation accuracy of the theoretical wind power effectively, providing more reliable data support for the integrated planning of renewable energies and electric vehicles in nearby regions. • Wind turbine power curve modeling based on interval extreme probability density. • A modeling method without complicated and time-consuming abnormal data cleaning. • Wind turbine power curves can be modeled with high accuracy and efficiency. • The proposed model supports the planning of renewable energies and electric vehicles. [ABSTRACT FROM AUTHOR]

Based on the characteristics of the power curves of wind turbine generator systems and the principles of the support vector machine (SVM), a piecewise support vector machine (PSVM) model is proposed in this article to improve the precision of short-term wind-power prediction systems. The operation data from a wind farm in north China are used to verify the proposed model, and the average mean error and root mean squared error of the PSVM model are 4.76% and 68.83 kW less than that of an SVM model respectively. Results of parameter optimization confirm the robustness of the PSVM model. [ABSTRACT FROM AUTHOR]

Wind speed spatial variation in the whole wind wheel rotation plane caused by wind shear and tower shadow effect becomes more obvious with the increase of rotor diameter and tower height, thus the hub height wind speed is not sufficient to represent the actual wind speed effect and the rotor equivalent wind speed should be used. However, the existing rotor equivalent wind speed calculation models do not consider wind turbine operation characteristics, which cannot match the actual power control effect, thus ignoring the power loss caused by actual power control deviation. To address this problem, a rotor equivalent wind speed calculation method based on equivalent power is proposed in this paper, which comprehensively considers wind shear, tower shadow and wind turbine operation characteristics. The rationality of the proposed model was qualitatively evaluated from different power control stages of wind turbine and the validity was quantitatively verified from the aspect of theoretical power calculation. The results demonstrated that the proposed model can effectively reflect the variation law of rotor equivalent wind speed under the actual control effect of different power control stages, which contains the power loss caused by actual power control deviation, thus improving the theoretical power calculation accuracy. • Rotor equivalent wind speed calculation model based on equivalent power is proposed. • Wind shear, tower shadow and wind turbine operation characteristics are considered. • The proposed model matches the actual power control effect of wind turbine. • The proposed model contains the power loss caused by actual power control deviation. • The proposed method can improve theoretical power accuracy verified by real cases. [ABSTRACT FROM AUTHOR]

In a wind farm some wind turbines may be affected by multiple upwind wakes. The commonly used approach in engineering to simulate the interaction effect of different wakes is to combine the single analytical wake model and the interaction model. The higher turbulence level and shear stress profile generated by upwind turbines in the superposed area leads to faster wake recovery. The existing interaction models are all analytical models based on some simple assumptions of superposition, which cannot characterize this phenomenon. Therefore, in this study, a mixing coefficient is introduced into the classical energy balance interaction model with the aim of reflecting the effect of turbulence intensity on velocity recovery in multiple wakes. An empirical expression is also given to calculate this parameter. The performance of the new model is evaluated using data from the Lillgrund and the Horns Rev I offshore wind farms, and the simulations agree reasonably with the observations. The comparison of different interaction model simulation results with measured data show that the calculation accuracy of this new interaction model is high, and the mean absolute percentage error of wind farm efficiency is reduced by 5.3% and 1.58%, respectively, compared to the most commonly used sum of squares interaction model. [ABSTRACT FROM AUTHOR]

Utilizing the secondary wind resources in cities and countryside is a significant way to promote wind power consumption and sustainable transportation. However, the probability of wind speed near the cut-in wind speed increases in such area and resulting in frequent on/off switches as well as large fatigue load of the wind turbine. To address these problems, this paper proposes a switching strategy of the low wind speed wind turbine based on real-time wind process prediction. First, the wavelet decomposition and neural network are employed to predict the time series of wind speed in a real-time manner. Second, based on the historical and predicted wind speed, the typical wind processes are extracted by using the x-means algorithm. Third, seven indexes are defined to quantify the characteristics of the wind process set before developing the corresponding switching strategy for each type of it. Data from NREL are used to validate the proposed models. The results show that, the proposed strategy increases the energy yield, reduces the number of wind turbine switches as well as the power fluctuation. Therefore, the proposed strategy is beneficial to both wind power projects development and electric vehicles charging. • Low wind speed turbine switching by real-time wind process prediction is proposed. • Typical wind process is established by clustering the historical and future winds. • Strategy adjusts dynamically based on seven presented wind characteristic indexes. • The proposed strategy increases the energy yield and reduces the switches. • The proposed strategy improves quantity and quality of wind power for EVs charging. [ABSTRACT FROM AUTHOR]

Highlights • Probabilistic power curve model to quantify uncertainty of wind energy conversion. • Data clearing methods based on intuitive rules and density-based clustering. • Use of new model inputs – pitch angle and wind direction – improves model accuracy. • Novel index is proposed to evaluate performance of probabilistic power curve model. Abstract This paper proposes probabilistic wind turbine power curve (WTPC) models to quantify the uncertainties of energy conversion and highly scattered relationships of actual wind speed to power. First, new model inputs (i.e. pitch angle and wind direction) and novel data clearing methods are presented to improve the model accuracy, which is rare in the previous studies. Second, the models are established based on three nonparametric algorithms, i.e. Monte Carlo, neural network, and fuzzy clustering. Third, to fill the research gap on model evaluation, the desirable properties of a probabilistic WTPC model are defined as expected variance ratio (EVR), and this index is formulated by calculating the cumulative gaps between the simulated and actual power distribution in each wind speed segment. Data from two Chinese wind farms are used to validate and compare the proposed methods using the mainstream deterministic index and the proposed EVR. Results show that (i) new model inputs and data clearing methods are able to improve the accuracy for probabilistic models regardless of the afterwards modelling method; (ii) fuzzy outperforms other probabilistic models. [ABSTRACT FROM AUTHOR]

Vortex generators (VGs) are commonly-used effective flow separation control devices, and are proved to have potential to improve the aerodynamic performance of large wind turbines. In this paper, the flow physics of VGs and how their size affects the aerodynamic performance of a blunt trailing-edge airfoil DU97-W-300 have been investigated using CFD simulations. Based on wind turbine dedicated airfoil with and without VGs respectively, three-dimensional numerical models were established and further validated through the comparisons between the numerical results and the experimental data. The effects of VGs' size were analyzed from several perspectives, such as trailing-edge height, length, short and long spacing between an adjacent pair of VGs. The results indicate that drag penalty is more sensitive to the increase of VG height than lift; an increment of VG length leads to negative effects on both lift and drag; increases of the spacing between an adjacent pair of VGs have positive impact on suppression of separated flow. Additionally, the flow field characteristics were further revealed by the analysis of streamlines and vortices in the wake region. [ABSTRACT FROM AUTHOR]

• Concept of quantile power curve of the wind turbine is proposed. • Quantile loss based neural network algorithm to establish the quantile power curve. • Index to evaluate the performance and uncertainty of a wind turbine is proposed. Wind turbine power curve is an indicator of wind turbine performance and important input of wind farm design or power prediction, therefore can serve the system planning and operation. However, a good power curve model is difficult to obtain because of the uncertain relationship between wind speed and its power output. Existing works focus on a deterministic model or use probabilistic distribution to represent such uncertain relation, which is not easy to be employed by the following decision-makers. This paper presents a novel concept termed as quantile power curve, which generates a series of power curves under any confidence level. Quantile loss based neural network algorithm is proposed to establish the quantile power curve. Index to measure the wind turbine performance and power generation uncertainty is also proposed based on the quantile power curve. Based on the operational data of a Chinese wind farm, the proposed model and index are validated and employed to estimate the wind energy yield when planning a system with wind, solar and electric vehicle charging loads. The results show that quantile power curve provides more comprehensive information about the uncertainty during the power generation process and helps to improve the renewable supply rate to charging loads. [ABSTRACT FROM AUTHOR]