Wind Turbine Test Rig Research Articles

A novel VAWT passive flow control numerical and experimental investigations: Guided Vane Airfoil Wind Turbine

Guided Vane Airfoil was introduced as a novel passive flow control approach to control the dynamic stall of the Darrieus vertical axis wind turbine. ANSYS fluent SW tool has been used to solve the unsteady Reynolds-averaged Navier-Stokes equations with an SST k-ω turbulent model to investigate the effect of the Guide Vane on the aerodynamics performance of NACA 0018 airfoil. Furthermore, a parametric study has been conducted to determine the optimum vane geometry to improve the airfoil aerodynamics efficiency. Based on the 2D airfoil investigation results, the best configuration of the vane and airfoil combination have been used to numerically study the dynamic stall control of one airfoil through actual vertical axis wind turbine rotation. In addition, the vane angle relative to the airfoil has been studied in the turbine CFD investigation. For the sake of validating the new concept, a wind turbine test rig has been constructed to, experimentally, investigate the performance of the Guided Vane Airfoil wind turbine. The performance of the original wind turbine has been firstly measured and then the Guided Vane is added to the turbine blades. Based on the experimental data, about 26% increase in the maximum power output has been measured from the Guided Vane airfoil wind turbine.

Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines.

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

Vibration characteristic responses due to transient mass loading on wind turbine blades

Wind turbine blades can experience mass collision events during operations, either due to bird strikes or with other flying objects. The rapid impact loadings due to these events can cause fatigue damage leading to subsequent blade loss or even wind turbine system failure. This paper addresses the effects of transient loadings resulting from a rapid mass reduction event from the rotating blade as well as an impacting mass onto the wind turbine blade, leading to transient vibration and unbalance within the turbine system. The novelty of the research is that it shows the coupling between the blade in-plane with tower transverse (Z-direction) vibrations, and blade out-of-plane with tower front (Y-direction) vibrations which have been identified through different experiments. Measurements from an experimental small-scale horizontal axis wind turbine test rig are reported, instrumented with contact and non-contact sensors and rotating and non-rotating sensors, to investigate the various transient impact loading events. Additionally, the drive shaft vibration for different conditions has been monitored by utilizing orthogonal laser measurement for detecting the main shaft displacements towards perpendicular axes. Subsequent orbit analysis is presented showing the analysis of different shaft orders during the rapid mass reduction and mass collision events. Complex orbit and filtering analysis of shaft orders during the transient mass impact loading events are used to demonstrate the sensitivity of the sensors for further fault detection.

Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection

An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data. This can become a burden for condition monitoring and fault detection systems. This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection. This paper first proposes a variable selection algorithm based on principal component analysis with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset. With the selected variables, this paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity. The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, Supervisory control and data acquisition data from an operational wind farm, and experimental data from a wind turbine test rig. Results show that the proposed methods can select a reduced set of variables with minimal information lost whilst detecting faults efficiently and effectively.

Design and Characterization of a Trailer-Based Horizontal-Axis Wind Turbine Test Rig

This paper presents a mobile testing rig developed for small wind turbine (SWT) experimental work to orchestrate, cost-effectively, turbine performance characterization in both controlled wind inflow speeds and turbulent ambient flows. It facilitates off-grid testing of up to a 1 kW wind turbine. It is a dual-purpose machine that can be towed behind a vehicle to conduct steady-state tests (track testing) or be parked to collect unsteady field data (field testing), all with the same rotor and instrumentation. Its mechanical design included computational fluid dynamics (CFD) analysis to gauge the potential impact of towing vehicle disturbance on the free stream available to the rotor. To provide a compelling platform for full rotor speed control, a reconfigurable control system coupled to an electric vehicle controller with regenerative braking technology has been modeled and implemented into its electrical design. Uncertainty analysis has also been rigorously conducted to project the error bounds pertaining to both precision and bias components of the testing results. The rig has been tested in a towed scenario and blade element momentum (BEM) simulations have been compared with the actual aggregate performance curves obtained experimentally. Future work involves testing in unsteady winds, for which the rig was ultimately designed in order to better understand unsteady rotor performance and adaptive design.

Development of a low cost test rig for standalone WECS subject to electrical faults

In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5kW, 1750 RPM DC motor coupled to a 1.5kW, 1500 RPM self-excited induction generator interfaced with a WT mathematical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for wind turbine condition monitoring.

Application of wireless power transfer technologies and intermittent energy harvesting for wireless sensors in rotating machines

Battery-powered wireless sensor networks have been extensively deployed in condition monitoring and structural health monitoring systems, but the performance of wireless sensors are limited by battery capacity and difficulty of application in rotating machines. In this paper, a variety of commercial wireless charging solutions and coil-shaft configurations for magnetic coupling are compared, having in mind of the application of continuously charging wireless sensors on rotating machines. For the co-axial configuration of the transmitter coil and the receiver coil, a Qi standard compliant wireless charging kit and a custom charging circuit are successfully applied to charge wireless sensors on small rotating test rigs. In order to harvest and store intermittent energy input from the wireless power source, a prototype receiver circuit using a supercapacitor and low-dropout regulator is designed and validated. Based on the prototype circuit, the radial configuration of single transmitter coil and multiple receiver coils is demonstrated for wireless power transfer to the sensor nodes on the drivetrain of a small wind turbine test rig.

Fault Size Estimation Using Vibration Signatures in a Wind Turbine Test-rig

Fault size evaluation has become more significant in recent years to determine the fault size or the severity of fault apart from the fault detection for prediction of remaining useful life. The present investigation focuses on the fault size estimation of gear root crack in a wind turbine test rig using vibration signatures. A wind turbine test rig was developed at BITS-Pilani, Hyderabad Campus to simulate the working of a wind turbine. Time domain vibration signals are acquired using accelerometers for the healthy as well as faulty components. Discrete Wavelet Transform of vibration signature is performed and features are extracted from the statistical analysis of wavelet coefficients and the extracted features are used as inputs in an ANN (Artificial Neural Network) to effectively predict the size of gear root crack.

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

The installation of a condition monitoring system (CMS) aims to reduce the operating costs of the monitored system by applying a predictive maintenance strategy. However, a system-driven configuration of the CMS requires the knowledge of the system kinematics and could induce a lot of false alarms because of predefined thresholds. The purpose of this paper is to propose a complete data-driven method to automatically generate system health indicators without any a priori on the monitored system or the acquired signals. This method is composed of two steps. First, every acquired signal is analyzed: the spectral peaks are detected and then grouped in a more complex structure as harmonic series or modulation sidebands. Then, a time-frequency tracking operation is applied on all available signals: the spectral peaks and the spectral structures are tracked over time and grouped in trajectories, which will be used to generate the system health indicators. The proposed method is tested on real-world signals coming from a wind turbine test rig. The detection of a harmonic series and a modulation sideband reports the birth of a fault on the main bearing inner ring. The evolution of the fault severity is characterized by three automatically generated health indicators and is confirmed by experts.

Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine

This paper presents an approach for fault diagnosis of wind turbine (WT) based on multi-class fuzzy support vector machine (FSVM) classifier. In this method, empirical mode decomposition is adopted to extract fault feature vectors from vibration signals. FSVM is used for solving classification problem with outliers or noises, where kernel fuzzy c-means clustering algorithm and particle swarm optimization algorithm are applied to calculate fuzzy membership and optimize the parameters of kernel function of FSVM, respectively. In addition, to study the performance of the proposed approach, another two widely used methods, named back propagation neural network and standard support vector machine, are studied and compared. Discrete wavelet transform is also used to extract fault feature vectors. To validate the proposed approach, a direct-drive WT test rig is constructed and the experiments are carried out. The experimental results show that the proposed approach is an effective fault diagnosis method for WT, which has a better performance and can achieve higher diagnostic accuracy.

Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions

Planetary gearbox fault diagnosis under nonstationary conditions is important for many engineering applications in general and for wind turbines in particular because of their time-varying operating conditions. This paper focuses on the identification of time-varying characteristic frequencies from complex nonstationary vibration signals for fault diagnosis of wind turbines under nonstationary conditions. We propose a time–frequency analysis method based on the Vold-Kalman filter and higher order energy separation (HOES) to extract fault symptoms. The Vold-Kalman filter is improved such that it is encoders/tachometers-free. It can decompose an arbitrarily complex signal into mono-components without resorting to speed inputs, thus satisfying the mono-component requirement by the HOES algorithm. The HOES is then used to accurately estimate the instantaneous frequency because of its high adaptability to local signal changes. The derived time–frequency distribution features fine resolution without cross-term interferences and thus facilitates extracting time-varying frequency components from highly complex and nonstationary signals. The method is illustrated and validated by analyzing simulated and experimental signals of a planetary gearbox in a wind turbine test rig under nonstationary running conditions. The results have shown that the method is effective in detecting both distributed (wear on every tooth) and localized (chipping on one tooth) gear faults.

Development of a Wind Turbine Test Rig and Rotor for Trailing Edge Flap Investigation: Static Flap Angles Case

One of the strategies used to improve performance and increase the life-span of wind turbines is active flow control. It involves the modification of the aerodynamic characteristics of a wind turbine blade by means of moveable aerodynamic control surfaces. Trailing edge flaps are relatively small moveable control surfaces placed at the trailing edge of a blade's airfoil that modify the lift of a blade or airfoil section. An instrumented wind turbine test rig and rotor were specifically developed to enable a wide-range of experiments to investigate the potential of trailing edge flaps as an active control technique. A modular blade based on the S833 airfoil was designed to allow accurate instrumentation and customizable settings. The blade is 1.7 meters long, had a constant 178mm chord and a 6° pitch. The modular aerodynamic parts were 3D printed using plastic PC-ABS material. The blade design point was within the range of wind velocities in the available large test facility. The wind facility is a large open jet wind tunnel with a maximum velocity of 11m/s in the test area. The capability of the developed system was demonstrated through an initial study of the effect of stationary trailing edge flaps on blade load and performance. The investigation focused on measuring the changes in flapwise bending moment and power production for different trailing edge flap spanwise locations and deflection angles. The relationship between the load reduction and deflection angle was linear as expected from theory and the highest reduction was caused by the flap furthest from the rotor center. Overall, the experimental setup proved to be effective in measuring small changes in flapwise bending moment within the wind turbine blade and will provide insight when (active) flap control is targeted.

Fault diagnosis of wind turbine based on multi‐sensors information fusion technology

Faults occurring in wind turbine (WT) degrade the performance and efficiency of wind power generation system, which result in large operation and maintenance cost of the WT. Therefore effective fault diagnosis schemes are greatly required for WT. This study presents a multi-sensors information fusion technology for fault diagnosis of the WT, where vibration sensors are adopted. Firstly, to deal with the non-stationary nature of the vibration signals of the WT, empirical mode decomposition method is utilised to extract fault features from the signals. Secondly, different classifiers are trained separately to classify fault types based on independent feature vectors from different sensors. Finally, three fusion approaches, named ordered weighted averaging, D-S evidential reasoning and fuzzy integral, are, respectively, used for fusing the diagnosis results of individual classifiers. To validate the proposed method, a direct-drive WT test rig is constructed and the related experiments are carried out. The experimental results validate the assumption that the proposed approach is effective for fault diagnosis of the WT, which has a higher diagnostic accuracy than that of individual sensor.

Application of SPM to Detect the Wind Turbine Bearing Fault

With the widespread application of wind turbines, the Maintenance issue gets growing concern. Bearings play a critical role in industrial applications. It is necessary to effectively monitor their health status. The shock pulse method (SPM) can detect the incipient fault of bearings and prevent the fault consequence effectively. However, many researchers used laboratory data to validate the SPM. This paper mainly concentrates on the SPM application on bearing fault detection of wind turbines. Shock pulse signals are derived from the gearbox of industrial wind turbine test rig by SPM instrument. According to frequency spectrum analysis, the bearing fault has been accurately detected and located. The analysis results demonstrate that the SPM technology is potentially effective for detecting the bearing faults of industrial wind turbines.

Global geometric similarity scheme for feature selection in fault diagnosis

This work presents a global geometric similarity scheme (GGSS) for feature selection in fault diagnosis, which is composed of global geometric model and similarity metric. The global geometric model is formed to construct connections between disjoint clusters in fault diagnosis. The similarity metric of the global geometric model is applied to filter feature subsets. To evaluate the performance of GGSS, fault data from wind turbine test rig is collected, and condition classification is carried out with classifiers established by Support Vector Machine (SVM) and General Regression Neural Network (GRNN). The classification results are compared with feature ranking methods and feature wrapper approaches. GGSS achieves higher classification accuracy than the feature ranking methods, and better time efficiency than the feature wrapper approaches. The hybrid scheme, GGSS with wrapper, obtains optimal classification accuracy and time efficiency. The proposed scheme can be applied in feature selection to get better accuracy and efficiency in condition classification of fault diagnosis.

Experimental Tests of Wind Turbine Main Shaft Motion on a Laboratory Test Rig


 
 
 This paper introduces research and discusses findings dealing with failure modes of gearboxes in wind turbines. These gearboxes fail in general after five years which is far below the expected design life of twenty years of a wind turbine. The research is taking a more holistic approach towards finding typical behaviour of the main shaft taking the transient nature of the wind into consideration. In this research, a small scale wind turbine test rig has been designed and manufactured with displacement sensors installed to observe the displacement of the main shaft at specific points, namely the main bearing locations of the forward framework of a wind turbine nacelle, where the main shaft is installed. The experimental data measured from the test rig is being analysed with common beam bending, statistical and fatigue theories to draw conclusions for long term loading in service. Aspects of the turbulent nature of the wind driving the wind turbine have been taken into consideration as being part of the aerodynamic loading onto the rotor and eventually the gearbox, transmitted through the main shaft. The purpose of the test rig at this stage is to obtain a quantitative insight into the motion of the main shaft. The deliberately chosen softer aluminum material and the more slender geometry for the components should provide exaggerated displacements which help to make motion and deformations more obvious. At this point, no resemblance to real size wind turbines has been established.
 
 

Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation

Planetary gearboxes play an important role in wind turbine drive trains. Fault diagnosis of planetary gearboxes is a key topic for maintenance of wind turbines. Considering the spectral complexity of planetary gearbox vibration signals as well as their amplitude modulation and frequency modulation (AMFM) nature, we propose a simple yet effective method to diagnose planetary gearbox faults based on amplitude and frequency demodulations. We use the energy separation algorithm to estimate the amplitude envelope and instantaneous frequency of modulated signals for further demodulation analysis, by exploiting the adaptability of Teager energy operator to instantaneous changes in signals and the fine time resolution. However, the energy separation algorithm requires signals to be mono-components. To satisfy this requirement, we decompose signals into intrinsic mode functions (IMFs) using the ensemble empirical mode decomposition (EEMD) method as it can decompose any signal into mono-components. We further propose a criterion to guide the selection of the most relevant IMF for demodulation analysis according to the wavelet-like filter nature of EEMD and the AMFM characteristics of the planetary gearbox vibration signals. By matching the dominant peaks in the Fourier spectra of the obtained amplitude envelope and instantaneous frequency with the theoretical characteristic frequency of each gear, we can diagnose planetary gearbox faults. The principle and effectiveness of the proposed method are illustrated by simulation and the experimental analysis of signals from a planetary gearbox of a wind turbine test rig. With the proposed method, both the wear and chipping faults can be detected and located for a sun gear of the planetary gearbox test rig.