Individual Wind Turbines Research Articles

Evaluation of Gaussian wake models under different atmospheric stability conditions: Comparison with large eddy simulation results

The calculation of the velocity deficit in the wake of individual wind turbines is a fundamental part of the wind farm analysis. A good approximation of the wake deficit behind a single wind turbine will improve the power estimation for downwind turbines. Large-eddy simulation (LES) is a research tool widely used in studying the velocity deficit and turbulence intensity in the wake. However, the computational cost of the LES prevents its application in wind farm performance analysis and control. Existing analytical wake models provide a fast estimation of the velocity deficit and the wake expansion rate downstream from the rotor. The Gaussian wake models use a Gaussian distribution to improve the prediction of the wake velocity deficit. With the number of analytical models available, an extensive evaluation of their performance under different flow parameters is needed. In this work, we simulate a wake of a single wind turbine using the LES code PALM (Parallelized LES Model) combined with an actuator disc model with rotation. We compare the computed flow field with the predictions made by Gaussian models and fit their parameters to obtain the best possible fit for the wake field data as computed by LES.

Analytical three‐dimensional wind flow model for real‐time wind farm simulation

Wind energy conversion systems have gained an essential share of the electricity supply and are increasingly competitive with conventional power plants based on fossil fuel. Wind turbines are expected to continue their increase in rated power and size, exceeding 10 MW and more, imposing major challenges concerning not only the operation of future power grids but also mechanical stress on individual wind turbines, their aerodynamic interaction and holistic wind farm control strategies as well as simulation models being able to cover all these aspects. This study deals with requirements of a real-time capable three-dimensional wind flow model for wind farm simulation, covering key issues from wind modelling through wake interactions to prospects of advanced control strategies. Building upon this, a modular framework is proposed and implemented by integrating existing validated analytical models. Simulation results demonstrating the model capabilities are presented, discussed and evaluated concerning future research focus.

Deep learning approach for wind speed forecasts at turbine locations in a wind farm

In a wind farm, individual turbines disturb the wind field by generating wakes, so wind speeds at various turbine locations are different. From the perspective of wind farm control, there is an interest in dynamic optimization of the power reference for each individual wind turbine, and the wind speed forecast at each turbine location is hence required. This paper develops a joint model of convolutional neural network (CNN) and the gated recurrent units (GRU) to forecast the wind speed at turbine locations. This model employs a two-layer architecture. At the lower-layer, the spatial features are automatically extracted by CNN. The extracted spatial features describe the spatial correlations among multiple wind turbines. At the upper-layer, GRU learns the temporal correlations across the extracted spatial features. This joint model is trained in an integrated manner. A salient characteristic of this model is that it extracts high-level spatial-temporal features from wind data. These automatically learnt features capture the spatial-temporal wind dynamics and interactions in a wind farm, thus being informative and appropriate for the forecasting at specific turbine locations. The simulation on actual data demonstrates the effectiveness of the presented model.

Rejecting wake-rotor overlapping load disturbances: An extension to active power control of wind farms

This paper studies an extension to the active power control of wind farms for further structural load alleviation of waked wind turbines. We demonstrate that the structural fatigue loading of a downstream turbine, which its rotor overlaps with wakes of its upwind turbine, can be significantly alleviated, while the wind farm power production follows a power reference signal. A load variations model is proposed, based on the multi-rotor concept, to sense the variations of the wind velocity and the associated loading across the rotor area of a single wind turbine. Then, an individual rotor control is proposed to reject the rotor-wake overlapping load disturbances about an operating condition, commanded with the closed-loop APC at the wind farm level. The applicability and key features of the controller are discussed with a wind farm example consisting of 2×2 turbines. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures and its interaction with an atmospheric boundary layer. The effectiveness of the proposed load disturbance rejection is evaluated using the damage equivalent load of the tower base fore-aft bending and torsional moments of the individual wind turbines.

Experimental analysis of time delays in wind turbine wake interactions

Wind farm control is a current hot topic that deals with the capacity to improve the overall wind power production of a farm by controlling individual wind turbines in order to mitigate their wake effects. The dynamic properties of these different strategies need to be taken into account in order to improve wind farm control models. In this work a post-processing of SCADA data acquired with a high sampling frequency on two neighboring wind turbines is performed through inter-correlation functions in order to assess the time delays between the wind turbine responses depending on wind direction and wind speed.

Modelling cluster wakes and wind farm blockage

We present two new models for wind turbine interaction effects and a recipe for combining them. The first model is an extension of the Park model, which explicitly incorporates turbulence, both the ambient atmospheric turbulence and the turbulence generated in the wake itself. This Turbulence Optimized Park model is better equipped to describe wake recovery over long distances such as between wind farms, where the wake expansion slows down as the turbine-generated turbulence decays. The second model is a first version of a full engineering wind farm blockage model. In the same vein as the wake model it adds blockage contributions from the individual wind turbines to form an aggregated wind farm scale blockage effect that can be incorporated directly into the park power curve and annual energy calculations. The wake model and the blockage model describe downstream and upstream turbine interaction effects, respectively. They are coupled as the outputs of one model are the inputs to the other model and vice versa. We describe how this coupling is achieved through an iterative process. We give early stage examples of the validation of the two models and discuss how they might be further validated and improved in the future.

A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

Due to the significant increase of the number of wind-based electricity generation systems, it is important to have accurate information on their operational characteristics, which are typically obtained by processing large amounts of measurements from the individual wind turbines (WTs) and from the whole wind farms (WFs). For further processing of these measurements, it is important to identify and remove bad quality or abnormal data, as otherwise obtained WT and WF models may be biased, or even inaccurate. There are wide ranges of both causes and manifestations of these bad/abnormal data, which are often denoted with the common general term “outlier”. This paper reviews approaches for the detection and treatment of outliers in processing WT and WF measurements, starting from the discussion of the commonly measured parameters, variables and resolutions, as well as the corresponding requirements and recommendations in related standards. Afterwards, characteristics and causes of outliers reported in existing literature are discussed and illustrated, as well as the requirements for the data rejection in related standard. Next, outlier identification methods are reviewed, followed by a review of approaches for testing the success of outlier removal procedures, with a discussion of their potential negative effects and impact on the WT and WF models. Finally, the paper indicates some issues and concerns that could be of interests for the further research on the detection and treatment of outliers in processing WT and WF measurements.

Study of resonance issues between DFIG-based offshore wind farm and HVDC transmission

Wide-frequency range of resonances can occur in HVDC-connected DFIG-based offshore wind farm (OWF), thus induce large harmonic distortion or endanger system stability. Previous studies usually treat OWF as a single-machine system without considering the switching states of individual wind turbines and collecting cables, or ignore the converter responses to wind speed variation and generation control order. In this paper, such factors are included in the modelling of OWF. Their impacts on resonance frequency, amplification level and the occurrence of negative damping are analyzed applying the resonance mode analysis method. Harmonic stability and power quality issues are demonstrated for the frequency range from several Hz to a few kHz. Simulations in MATLAB/Simulink validate the analytical results. Additionally, resonance damping strategies are discussed regarding the identified resonances in widely spread frequencies.

A methodology for reliability assessment and prognosis of bearing axial cracking in wind turbine gearboxes

This article describes an interdisciplinary methodology to calculate the probability of failure for bearing axial cracking, the dominant failure mode in the intermediate and high-speed stages of many wind turbine gearboxes. This approach is mainly a physics-domain method with needed inputs from the data domain. The gearbox and bearing design along with operations data and component failure records from a wind power plant provide the input to physics-based models and define axial cracking damage metrics. The physics-domain models predict the bearing loads and sliding velocities, which are the essential elements for quantifying the accumulated frictional energy. Both accumulated frictional energy and electrical energy generation are proposed as damage metrics for bearing axial cracking. A first-order reliability method is then used to compare the proposed damage metrics to failure threshold functions and calculate the probability of failure of each individual bearing. Although the probability of failure for the failed turbines is not separated from the population, a feature engineering analysis shows the potential of frictional energy as a damage metric when combined with roller loads, bearing sliding speed, lubricant type, and terrain features. Through statistical analysis of historical data, the proposed methodology enables reliability assessment of axial cracking in individual wind turbine bearings and connects the reliability forecast with turbine design and operations.

Partial repowering analysis of a wind farm by turbine hub height variation to mitigate neighboring wind farm wake interference using mesoscale simulations

Repowering augments the power generation capacity of a wind farm in order to enhance the exploitation of the high mean wind speed regions. In this study, a partial repowering strategy has been discussed for an onshore commercial-scale wind farm with deteriorated performance under the influence of wakes originating from upstream wind farms. The mesoscale Weather Research and Forecasting (WRF) model with Wind Farm Parameterization has been employed to evaluate the wind speed and power deficit observed by the individual wind turbine generators. Wakes induced by the upstream wind farms are observed to cause a reduction in wind speed and power output by up to 15% and 35%, respectively. The predicted power was also validated by the observed data for each turbine generator. The hub heights of selected wind turbines with the highest power deficit are varied to alleviate the influence of upstream wind farm wake interference. Power output and wind shear profiles have been evaluated at new hub heights of 61.5 m and 100 m, and results are compared with the existing hub height turbine generators at 80 m. Power output reduced by up to 12% at the lower hub height of 61.5 m as compared to the default case, while an increase of about 13.6% is observed for the 100 m hub height case. A mean increase of 7.5% is estimated in the total wind power generation from the wind farm under evaluation. This paper presents a theoretical and engineering platform for partial repowering of wind farms with depleted generation due to inter-farm wake interaction.

Correlated power time series of individual wind turbines: A data driven model approach

Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function.

Multi-Scale Simulation of Wind Farm Performance during a Frontal Passage

Predicting the response of wind farms to changing flow conditions is necessary for optimal design and operation. In this work, simulation and analysis of a frontal passage through a utility scale wind farm is achieved for the first time using a seamless multi-scale modeling approach. A generalized actuator disk (GAD) wind turbine model is used to represent turbine–flow interaction, and results are compared to novel radar observations during the frontal passage. The Weather Research and Forecasting (WRF) model is employed with a nested grid setup that allows for coupling between multi-scale atmospheric conditions and turbine response. Starting with mesoscale forcing, the atmosphere is dynamically downscaled to the region of interest, where the interaction between turbulent flows and individual wind turbines is simulated with 10 m grid spacing. Several improvements are made to the GAD model to mimic realistic turbine operation, including a yawing capability and a power output calculation. Ultimately, the model is able to capture both the dynamics of the frontal passage and the turbine response; predictions show good agreement with observed background velocity, turbine wake structure, and power output after accounting for a phase shift in the mesoscale forcing. This study demonstrates the utility of the WRF-GAD model framework for simulating wind farm performance under complex atmospheric conditions.

Wake meandering in a model wind turbine array in a high Reynolds number turbulent boundary layer

Wake meandering is the dynamic shift observed in the spatial location of a wind turbine wake as it evolves downstream, considered to be caused primarily by the interaction of large flow structures in the atmospheric boundary layer with the turbulent wake. Experiments were conducted in a large boundary layer wind tunnel to investigate meandering in the wakes of both individual model wind turbines and in a wind turbine array with a large number of model turbines (19 rows x 5 columns = 95 model turbines). Unlike bluff body vortex shedding, the meandering phenomenon for a turbine wake is not characterized by a well-pronounced peak in the frequency domain, but rather by a broad spread over a low-frequencies range. For individual turbine models, both rotating three-bladed models and non-rotating porous disks, frequencies and peak energies observed in the velocity spectrum in the wake return to those of the incoming high Reynolds number turbulent boundary layer. Wake meandering also presents itself in the large wind turbine array. Here, frequencies and peak energies observed in the velocity spectrum in the wake do not return to those of the incoming turbulent boundary layer. Instead, a lower dominant frequency corresponding to the wind velocity at hub height and array spacing is observed within the array, indicating forcing of the meandering by the array itself (a type of resonance). Higher peak energies are observed in the array due to large flow structures representative of the turbine spacing.

Consensus-Based Demand Response of PMSG Wind Turbines With Distributed Energy Storage Considering Capability Curves

This article proposes a distributed consensus-based demand response control for permanent magnet synchronous generator (PMSG)-based wind turbines using standalone distributed battery energy storage systems (BESSs). The proposed controller cooperatively regulates the output power of individual wind turbines and BESSs in a wind farm to deliver active and reactive powers to the load in varying wind speed conditions. Capability curves of the wind turbines and BESSs are considered in the design. In addition, a virtual leader is designed to regulate the voltage and frequency of the combined PMSG-BESS units at the point of common coupling. Simulations on modified IEEE 14-bus power system are performed to validate the proposed design.

FMMA and FMECA for analysis of reliability of a wind turbine

This work is designed to highlight the technical data regarding reliability and maintainability of a modern-day Wind Turbine. With the ever-increasing demand for cleaner energy and reduction of greenhouse gases, Wind Turbines offer a feasible solution in contributing towards a greener future as a whole. In spite of this, they must provide both a reliable form of energy production and an economic benefit to corporations investing in this technology, as an individual wind turbine can be considered a large financial investment, both to set-up and to maintain.

Analysis of the lightning impulse and low-frequency performance of wind farm grounding systems

Previous analysis of the lightning response of wind turbine grounding systems has often considered only one unit. This paper investigates the impulse and low-frequency performance of isolated and interconnected grounding systems of wind turbines, subjected to lightning currents representative of first and subsequent negative downward strokes and return strokes associated with upward negative flashes. The system behavior is studied for different lengths of interconnecting conductor between adjacent grounding systems and for distinct values of soil resistivity considering frequency-dependent soil parameters. It is shown that in the case of lightning currents with shorter front-times, the interconnection improves the impulse response of grounding, reducing the GPR peak, only for soils of higher resistivity, whereas in the case of currents with longer front-times, the interconnecting improves the impulse response in all cases. The relative improvement of the impulse response when the adjacent grounding systems are interconnected becomes more significant with the increase of the soil resistivity. In all cases, interconnecting the grounding systems strongly reduces the low-frequency grounding resistance seen by each individual wind turbine earth termination.

Simulation of turbulent wakes in model wind farm with arbitrary location for wind turbines

Wind energy is an important part of renewable energy sources in the Russian Federation. A new modern wind farm with 28 wind turbines was built on the territory of the Russian Federation, in the Ulyanovsk region in 2017-2018. When designing and operating a wind farm, it is necessary to assess correctly the power of each individual wind turbine. In this paper we used the open SOWFA library based on OpenFOAM 2.4.0 and URANS approach with k-e model of turbulence. The Actuator Line Model for the calculation of the forces from the wind turbines was used in pisoFoamTurbine.ALM solver. To set the boundary conditions at the input, the Precursor method was used. The unstructured grids with different number of cells were built. As a result of the calculation for 14 simulators of wind turbines, the values of the power coefficients Cp and thrust coefficients Ct were also determined. The maximum Cp value was in agreement with the Bets-Zhukovsky law for an ideal wind turbine. The new numerical case with 28 wind turbines was prepared for future simulations. The calculations were performed on a cluster of the UNICFD web-laboratory of ISP RAS. Each calculation was performed on 48-72 cpu cores.

Enhanced FRT and Postfault Recovery Control for MMC-HVDC Connected Offshore Wind Farms

Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) connected offshore wind farms could suffer serious challenges: DC overvoltage during a fault and post-fault DC voltage restoration. In this paper, to mitigate DC overvoltage, a two-stage voltage drop control scheme is proposed for offshore MMC to realize a fast active power reduction, by coordinating with a voltage-dependent active current control of individual wind turbines. The voltage drop depths are designed by considering the offshore de-loading efficiency and the stability of WTs. To improve the post-fault DC voltage recovery performance, an adaptive voltage rise control scheme is proposed to coordinate the recovery of onshore and offshore active power. The recovery trajectory of DC voltage is formulated and further employed to design the onshore active power ramp rate for uninterrupted operation of WTs. Case studies have been conducted on two test systems to validate the proposed method.

Nonparametric wind power forecasting under fixed and random censoring

We consider nonparametric forecasting of wind power for individual wind turbines, allowing for random right censoring as well as two-sided fixed censoring. We propose a very fast estimation algorithm and show that this estimator of the unknown regression function is uniformly consistent. We argue that the key statistical features of the proposed nonparametric regression framework, such as nonlinearities and fixed and random censoring, are all needed in order to properly capture the main characteristics of wind power production functions. We show by a simulation study that the asymptotic properties of the estimator also holds in finite samples. We provide an empirical illustration comparing the forecasting accuracy of the proposed nonparametric regression model to some of the existing and popular forecasting devices used for predicting short to medium term wind power production at the individual turbine level. The empirical results are generally very encouraging.

Fault Characteristic and Low Voltage Ride-Through Requirements Applicability Analysis for a Permanent Magnet Synchronous Generator-Based Wind Farm

As the penetration of wind energy is being dramatically increased, the impact of wind energy on the power system should be roundly studied, especially for the fault characteristics analysis and applicability analysis of low voltage ride-through (LVRT) requirements for a whole wind farm (WF) and an individual wind turbine generator (WTG). This paper firstly describes a detailed modeling of a permanent magnet synchronous generator (PMSG)-based WF and analyzes the fault characteristics of the WF under various fault conditions. The validation of the fault characteristics analysis is carried out with the EMTP-RV generated data, with the consideration of different fault positions, fault types, and wind speeds. The relay protection and the related grid code are also taken into account. In addition, the applicability analysis of LVRT requirements for a WF and a WTG is also implemented, from the points of minimal grid-connection time and minimal dynamic reactive current support ability. The fault characteristic analysis of a PMSG-based WF could be helpful for developing new control or protection methods for a PMSG-based WF. Meanwhile, the applicability analysis of LVRT requirements could serve as a reference for WTG manufacturers, WF administrators, and grid operator.