Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions

As the capacity of wind turbines has increased, the loads on crucial components such as a gearbox, a generator, and blades are significantly increasing. An intelligent online monitoring system is indispensable to protect the excessive load on core components and manage a wind farm efficiently. In order to verify new online monitoring and diagnostic methods for such a monitoring system in advance, a wind turbine simulator is essential. For this purpose, we developed a simulator that has similar dynamics to an actual 3 MW wind turbine, and is thereby able to acquire a state of operation that closely resembles that of the 3 MW wind turbine under a variety of wind conditions.This paper describes the implementation of a torque and a collective pitch controller, which is used for a new type of simulator with the intention of exploiting online monitoring and diagnostic methods. The torque and the collective pitch controllers were developed to facilitate variable speed-variable pitch control strategies in the wind turbine simulator. Experiments demonstrated that three control regions were successfully deployed on the simulator, and thereby the simulator was operated at all control regions in a stable and accurate manner. Moreover, the strain and vibration measured from the blade and the gearbox showed different trends at three control regions. Therefore, a new type of simulator is an effective means to develop diagnostic and prognostic algorithms as well as online monitoring methods reflecting the dependency of dynamic characteristics on the control regions.     

Static and dynamic wind turbine simulator using a converter controlled dc motor

This paper describes a new wind turbine simulator for dynamic conditions. The authors have developed an experimental platform to simulate the static and dynamic characteristics of real wind energy conversion system. This system consists of a 3 kW dc motor, which drives a synchronous generator. The converter is a 3 kW single-phase half-controlled converter. MATLAB/Simulink real time control software interfaced to I/O board and a converter controlled dc motor are used instead of a real wind turbine. A MATLAB/Simulink model is developed that obtains wind profiles and, by applying real wind turbine characteristics in dynamics and rotational speed of dc motor, calculates the command shaft torque of a real wind turbine. Based on the comparison between calculated torques with command one, the shaft torque of dc motor is regulated accordingly by controlling armature current demand of a single-phase half-controlled ac–dc converter. Simulation and experimental results confirm the effectiveness of proposed wind turbine simulator in emulating and therefore evaluating various turbines under a wide variety of wind conditions.     

Development of a novel wind turbine simulator for wind energy conversion systems using an inverter-controlled induction motor

A wind turbine simulator for wind energy conversion systems has been developed with a view to design, evaluate, and test of actual wind turbine drive trains including generators, transmissions, power-electronic converters and controllers. The simulator consists of a 10-hp induction motor (IM) which drives a generator and is driven by a 10-kW variable speed drive inverter and real-time control software. In this simulator, a microcontroller, a PC interfaced to LAB Windows I/O board, and an IGBT inverter-controlled induction motor are used instead of a real wind turbine to supply shaft torque. A control program based on C language is developed that obtains wind profiles and, by using turbine characteristics and rotation speed of IM, calculates the theoretical shaft torque of a real wind turbine. Comparing with this torque value, the shaft torque of the IM is regulated accordingly by controlling stator current demand and frequency demand of the inverter. In this way, the inverter driven induction motor acts like a real wind turbine to the energy conversion system. The drive is controlled using the measured shaft torque directly, instead of estimating it as conventional drives do. The experimental results of the proposed simulator show that this scheme is viable and accurate. This paper reports the operating principles, theoretical analyses, and test results of this wind turbine simulator.     

The prediction and diagnosis of wind turbine faults

The rapid expansion of wind farms has drawn attention to operations and maintenance issues. Condition monitoring solutions have been developed to detect and diagnose abnormalities of various wind turbine subsystems with the goal of reducing operations and maintenance costs. This paper explores fault data provided by the supervisory control and data acquisition system and offers fault prediction at three levels: (1) fault and no-fault prediction; (2) fault category (severity); and (3) the specific fault prediction. For each level, the emerging faults are predicted 5–60 min before they occur. Various data-mining algorithms have been applied to develop models predicting possible faults. Computational results validating the models are provided. The research limitations are discussed.     

Comparison of simulators for variable‐speed wind turbine transient analysis

This paper presents a comparison of three variable‐speed wind turbine simulators used for a 2 MW wind turbine short‐term transient behaviour study during a symmetrical network disturbance. The simulator with doubly fed induction generator (DFIG) analytical model, the simulator with a finite element method (FEM) DFIG model and the wind turbine simulator with an analytical model of DFIG are compared. The comparison of the simulation results shows the influence of the different modelling approaches on the short‐term transient simulation accuracy. Copyright © 2005 John Wiley & Sons, Ltd.     

Aeroservoelastic design definition of a 20 MW common research wind turbine model

Wind turbine upscaling is motivated by the fact that larger machines can achieve lower levelized cost of energy. However, there are several fundamental issues with the design of such turbines, and there is little public data available for large wind turbine studies. To address this need, we develop a 20 MW common research wind turbine design that is available to the public. Multidisciplinary design optimization is used to define the aeroservoelastic design of the rotor and tower subject to the following constraints: blade‐tower clearance, structural stresses, modal frequencies, tip‐speed and fatigue damage at several sections of the tower and blade. For the blade, the design variables include blade length, twist and chord distribution, structural thicknesses distribution and rotor speed at the rated. The tower design variables are the height, and the diameter distribution in the vertical direction. For the other components, mass models are employed to capture their dynamic interactions. The associated cost of these components is obtained by using cost models. The design objective is to minimize the levelized cost of energy. The results of this research show the feasibility of a 20 MW wind turbine and provide a model with the corresponding data for wind energy researchers to use in the investigation of different aspects of wind turbine design and upscaling. Copyright © 2016 John Wiley & Sons, Ltd.     

Flow control based 5 MW wind turbine enhanced energy production for hydrogen generation cost reduction

Improving the performance and the production of renewable energy sources, especially the wind energy, is considered an attractive approach to reduce the Cost of Energy (COE) associated to the hydrogen generation process. In this context, flow control strategies have been object of detailed investigation during last years. Originally flow control devices were designed to be implemented in the aeronautical sector. Nonetheless, the optimization of the performance of the wind turbine blades with the introduction and implementation of these devices is being widely investigated these days. An analysis of the influence of implementing Vortex Generators (VGs) and Gurney Flaps (GFs) in Wind Turbine Blades (WTBs) on the Annual Energy Production (AEP) of large Horizontal Axis Wind Turbine (HAWT) is proposed in this paper. For that purpose, the Weibull distributions of annual wind speed data series of a real wind farm have been calculated, and Blade Element Momentum (BEM) based calculations have been performed to evaluate the effect of the flow control devices on the power curve and the AEP of the NREL offshore 5 MW Baseline Wind Turbine. The obtained results show an enhancement of the AEP of the turbine of 2.43% during year 2015 and 2.68% during year 2016. As a result, increments of the generated hydrogen volume larger than 130000 Nm³ are achieved during both years with no considerable additional cost in the design of the wind turbine.     

Performance enhancement and load reduction of a 5 MW wind turbine blade

A wind turbine rotor blade, based on the U.S. National Renewable Energy Laboratory (NREL) 5 MW reference turbine, is optimized for minimum cost of energy through simultaneous consideration of aerodynamics and bend-twist coupling. Eighty-three total design variables are considered, encompassing airfoil shapes, chord and twist distributions, and the degree of bend-twist coupling in the blade. A recently developed method requiring significantly less computation than finite element analysis is used for planning and predicting the bend-twist coupling behavior of the rotor. Airfoil performance is computed using XFOIL, while the wind turbine loads and performance are computed using the NREL FAST code. The objective function is annual cost of energy (COE), where reductions in flapwise bending loads and blade surface area are assumed to decrease rotor cost through reduced material requirements. The developed optimization process projects decreased blade loads while maintaining wind turbine performance.     

Advanced control algorithms for reduction of wind turbine structural loads

To enable further growth of wind turbine dimensions and rated power, it is essential to decrease structural loads that wind turbines experience. Therefore a great portion of research is focused on control algorithms for reduction of wind turbine structural loads, but typically wind turbine rotor is considered to be perfectly symmetrical, and therefore such control algorithms cannot reduce structural loads caused by rotor asymmetries. Furthermore, typical approach in the literature is to use blade load measurements, especially when higher harmonics of structural loads are being reduced. In this paper, improvements to standard approach for reduction of structural loads are proposed. First, control algorithm capable of reducing structural loads caused by rotor asymmetries is developed, and then appropriate load transformations are introduced that enable presented control algorithms to use load measurements from various wind turbine components. Simulation results show that proposed control algorithm is capable of reducing structural loads caused by rotor asymmetries.     

Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine

Fault diagnosis for wind turbine transmission systems is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine transmission systems. In this paper, a novel fault diagnosis method based on manifold learning and Shannon wavelet support vector machine is proposed for wind turbine transmission systems. Firstly, mixed-domain features are extracted to construct a high-dimensional feature set characterizing the properties of non-stationary vibration signals from wind turbine transmission systems. Moreover, an effective manifold learning algorithm with non-linear dimensionality reduction capability, orthogonal neighborhood preserving embedding (ONPE), is applied to compress the high-dimensional feature set into low-dimensional eigenvectors. Finally, the low-dimensional eigenvectors are inputted into a Shannon wavelet support vector machine (SWSVM) to recognize faults. The performance of the proposed method was proved by successful fault diagnosis application in a wind turbine's gearbox. The application results indicated that the proposed method improved the accuracy of fault diagnosis.     

某风电场风力发电机组故障诊断

为了准确判断风电机组的运行状态及故障,提出了基于常规分析—振动幅值分析—波形频谱分析的故障诊断流程,阐述了针对风电机组的幅值分析方法和波形频谱分析方法,并通过对某机组异响的根源探究实例,准确地诊断出机组异响来源于齿轮箱太阳轮,可为风电机组故障诊断技术提供依据。     

Load reduction on a clipper liberty wind turbine with linear parameter‐varying individual blade pitch control

The increasing size of modern wind turbines also increases the structural loads caused by effects such as turbulence or asymmetries in the inflowing wind field. Consequently, the use of advanced control algorithms for active load reduction has become a relevant part of current wind turbine control systems. In this paper, an individual blade pitch control law is designed using multivariable linear parameter‐varying control techniques. It reduces the structural loads both on the rotating and non‐rotating parts of the turbine. Classical individual blade pitch control strategies rely on single‐control loops with low bandwidth. The proposed approach makes it possible to use a higher bandwidth since it accounts for coupling at higher frequencies. A controller is designed for the utility‐scale 2.5 MW Liberty research turbine operated by the University of Minnesota. Stability and performance are verified using the high‐fidelity nonlinear simulation and baseline controllers that were directly obtained from the manufacturer. Copyright © 2017 John Wiley & Sons, Ltd.     

Vibration‐based wind turbine planetary gearbox fault diagnosis using spectral averaging

Planetary gearboxes (PGBs) are widely used in the drivetrain of wind turbines. Any PGB failure could lead to a significant breakdown or major loss of a wind turbine. Therefore, PGB fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and lifespan of wind turbines. The wind energy industry currently utilizes vibratory analysis as a standard method for PGB condition monitoring and fault diagnosis. Among them, the vibration separation is considered as one of the well‐established vibratory analysis techniques. However, the drawbacks of the vibration separation technique as reported in the literature include the following: potential sun gear fault diagnosis limitation, multiple sensors and large data requirement, and vulnerability to external noise. This paper presents a new method using a single vibration sensor for PGB fault diagnosis using spectral averaging. It combines the techniques of enveloping, Welch's spectral averaging, and data mining‐based fault classifiers. Using the presented approach, vibration fault features for wind turbine PGB are extracted as condition indicators for fault diagnosis and condition indicators are used as inputs to fault classifiers for PGB fault diagnosis. The method is validated on a set of seeded localized faults on all gears: sun gear, planetary gear, and ring gear. The results have shown a promising PGB fault diagnosis performance with the presented method. Copyright © 2015 John Wiley & Sons, Ltd.     

Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals

The implementation of condition monitoring and fault diagnosis system (CMFDS) on wind turbine is significant to lower the unscheduled breakdown. Generator is one of the most important components in wind turbine, and generator bearing fault identification always draws lots of attention. However, non-stationary vibration signal of weak fault and compound fault with a large amount of background noise makes this task challenging in many cases. So, effective signal processing method is essential in the accurate diagnosis step of CMFDS. As a novel signal processing method, empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis, which is seen as a powerful tool for mechanical fault diagnosis. Moreover, in order to avoid the inaccurate identification the internal modes caused by the heavy noise, wavelet spatial neighboring coefficient denoising with data-driven threshold is applied to increase Signal to Noise Ratio (SNR) before EWT. The effectiveness of the proposed technique on weak fault and compound fault diagnosis is first validated by two experimental cases. Finally, the proposed method has been applied to identify fault feature of generator bearing on wind turbine in wind farm successfully.     

Analysis of technology improvement opportunities for a 1.5 MW wind turbine using a hybrid stochastic approach in life cycle assessment

This paper presents an analysis of potential technological advancements for a 1.5 MW wind turbine using a hybrid stochastic method to improve uncertainty estimates of embodied energy and embodied carbon. The analysis is specifically aimed at these two quantities due to the fact that LCA based design decision making is of utmost importance at the concept design stage. In the presented case studies, better results for the baseline turbine were observed compared to turbines with the proposed technological advancements. Embodied carbon and embodied energy results for the baseline turbine show that there is about 85% probability that the turbine manufacturers may have lost the chance to reduce carbon emissions, and 50% probability that they may have lost the chance to reduce the primary energy consumed during its manufacture. The paper also highlights that the adopted methodology can be used to support design decision making and hence is more feasible for LCA studies.     

Simplified automatic fault detection in wind turbine induction generators

This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach.     

Data‐driven multiscale sparse representation for bearing fault diagnosis in wind turbine

With the increase of the wind turbine capacity, failures occur on the drivetrain of wind turbines frequently. Since faults of bearings in the wind turbine can lead to long downtime and even casualties, fault diagnosis of the drivetrain is very important to reduce the maintenance cost of the wind turbine and improve economic efficiency. However, the traditional diagnosis methods have difficulty in extracting the impulsive components from the vibration signal of the wind turbine because of heavy background noise and harmonic interference. In this paper, we propose a novel method based on data‐driven multiscale dictionary construction. Firstly, we achieve the useful atom through training the K‐means singular value decomposition (K‐SVD) model with a standard signal. Secondly, we deform the chosen atom into different shapes and construct the final dictionary. Thirdly, the constructed dictionary is used to sparsely represent the vibration signal, and orthogonal matching pursuit (OMP) is performed to extract the impulsive component. The proposed method is robust to harmonic interference and heavy background noise. Moreover, the effectiveness of the proposed method is validated by numerical simulation and two experimental cases including the bearing fault of the wind turbine generator in the field test. The overall results indicate that compared with traditional methods, the proposed method is able to extract the fault characteristics from the measured signals more efficiently.     

Estimation of wind energy production in various sites in Australia for different wind turbine classes: A comparative technical and economic assessment

This study examines the effect of different wind turbine classes on the electricity production of wind farms in three areas of Australia, which present low, low to medium, and medium to high wind potential: Gingin, Armidale, and Gold Coast Seaway. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from six different manufacturers have been used. For each manufacturer, at lest two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC I, IEC II and/or IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (higher IEC class) in all three locations, in terms of energy production. This improvement is higher for the locations with lower and medium wind potential (Gingin and Armidale), and varies from 5% to 55%. Moreover, this study investigates the economical feasibility of a 30 MW wind farm, for all combinations of site locations and wind turbine models.