Showing papers on "Turbine published in 2020"

Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design

Abstract: The Wind and Wind Turbines.- Modelling of Variable-speed Variable-pitch Wind Energy Conversion Systems.- Control Objectives and Strategies.- Control of Variable-speed Fixed-pitch Wind Turbines Using Gain Scheduling Techniques.- Control of Variable-speed Variable-pitch Wind Turbines Using Gain Scheduling Techniques.

Implementation of the blade element momentum model on a polar grid and its aeroelastic load impact

Abstract: . We show that the upscaling of wind turbines from rotor diameters of 15–20 m to presently large rotors of 150–200 m has changed the requirements for the aerodynamic blade element momentum (BEM) models in the aeroelastic codes. This is because the typical scales in the inflow turbulence are now comparable with the rotor diameter of the large turbines. Therefore, the spectrum of the incoming turbulence relative to the rotating blade has increased energy content on 1P , 2P , …, nP , and the annular mean induction approach in a classical BEM implementation might no longer be a good approximation for large rotors. We present a complete BEM implementation on a polar grid that models the induction response to the considerable 1P , 2P , …, nP inflow variations, including models for yawed inflow, dynamic inflow and radial induction. At each time step, in an aeroelastic simulation, the induction derived from a local BEM approach is updated at all the stationary grid points covering the swept area so the model can be characterized as an engineering actuator disk (AD) solution. The induction at each grid point varies slowly in time due to the dynamic inflow filter but the rotating blade now samples the induction field; as a result, the induction seen from the blade is highly unsteady and has a spectrum with distinct 1P , 2P , …, nP peaks. The load impact mechanism from this unsteady induction is analyzed and it is found that the load impact strongly depends on the turbine design and operating conditions. For operation at low to medium thrust coefficients (conventional turbines at above rated wind speed or low induction turbines in the whole operating range), it is found that the grid BEM gives typically 8 %–10 % lower 1 Hz blade root flapwise fatigue loads than the classical annular mean BEM approach. At high thrust coefficients that can occur at low wind speeds, the grid BEM can give slightly increased fatigue loads. In the paper, the implementation of the grid-based BEM is described in detail, and finally several validation cases are presented. Comparisons with blade loads from full rotor CFD, wind tunnel experiments and a field experiment show that the model can predict the aerodynamic forces in half-wake, yawed flow, dynamic inflow and turbulent inflow conditions.

Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis

Abstract: Wind turbine blade bearings are often operated in harsh circumstances, which may easily be damaged causing the turbine to lose control and to further result in the reduction of energy production. However, for condition monitoring and fault diagnosis (CMFD) of wind turbine blade bearings, one of the main difficulties is that the rotation speeds of blade bearings are very slow (less than 5 r/min). Over the past few years, acoustic emission (AE) analysis has been used to carry out bearing CMFD. This article presents the results that reflect the potential of the AE analysis for diagnosing a slow-speed wind turbine blade bearing. To undertake this experiment, a 15-year-old naturally damaged industrial and slow-speed blade bearing is used for this study. However, due to very slow rotation speed conditions, the fault signals are very weak and masked by heavy noise disturbances. To denoise the raw AE signals, we propose a novel cepstrum editing method, discrete/random separation-based cepstrum editing liftering (DRS-CEL), to extract weak fault features from raw AE signals, where DRS is used to edit the cepstrum. Thereafter, the morphological envelope analysis is employed to further filter the residual noise leaked from DRS-CEL and demodulate the denoised signal, so the specific bearing fault type can be inferred in the frequency domain. The diagnostic framework combining DRS-CEL and morphological analysis is validated by comparing several methods and related studies, which offers a promising solution for wind-farm applications.

Wind Turbine Fault Diagnosis and Predictive Maintenance Through Statistical Process Control and Machine Learning

Abstract: This study applies statistical process control and machine learning techniques to diagnose wind turbine faults and predict maintenance needs by analyzing 2.8 million sensor data collected from 31 wind turbines from 2015 to 2017 in Taiwan. Unlike previous studies that only relied on historical wind turbine data, this study analyzed the sensor data with practitioners’ insight by incorporating maintenance check list items into the data mining processes. We used Pareto analyses, scatter plots, and the cause and effect diagram to cluster and classify the failure types of wind turbines. In addition, control charts were used to establish a monitoring mechanism to track whether operation data are deviated from the controls (i.e., standard deviations) as a mean to detect wind turbine abnormalities. While statistical process control was applied to fault diagnosis, machine learning algorithms were used to predict maintenance needs of wind turbines. First, the density-based spatial clustering of applications with noise algorithm was used to classify abnormal-state wind turbine data from normal-state data. Then, random forest and decision tree algorithms were employed to construct the predictive models for wind turbine anomalies and tested with K-fold cross-validation. The results indicate a high level of accuracy: 92.68% for the decision tree model, and 91.98% for the random forest model. The study demonstrates that, by data mining and modeling, the failures of wind turbines can be detected, and the maintenance needs of parts can be predicted. Model results may provide technicians early warnings, improve equipment efficient, and decrease system downtime of wind turbine operation.

Pitch angle control of a wind turbine operating above the rated wind speed: A sliding mode control approach

Abstract: The paper focuses on variable-rotor-speed/variable-blade-pitch wind turbines operating in the region of high wind speeds, where control is aimed at limiting the turbine energy capture to the rated power value. A robust sliding mode approach is proposed, using the blade pitch as control input, in order to regulate the rotor speed to a fixed rated value, in the presence of uncertainties characterizing the wind turbine model. Closed loop convergence of the overall control system is proved. The proposed control solution has been validated on a 5 − M W three-blade wind turbine using the National Renewable Energy Laboratory (NREL) wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. A comparison with the standard FAST baseline controller (NWTC 2012 and Jonkman et al. 2009) has been also included.

Load mitigation of a class of 5-MW wind turbine with RBF neural network based fractional-order PID controller.

Abstract: In variable-pitch wind turbines, pitch angle control is implemented to regulate the rotor speed and power production. However, mechanical loads of the wind turbines are affected by the pitch angle adjustment. To improve the performance and at the same time alleviate the mechanical loads, a gain-scheduling fractional-order PID (FOPID), where a trained RBF neural network chooses its parameters is proposed. The database, which the RBF neural network is trained based on, is created via optimization of a FOPID in several wind speeds with chaotic differential evolution (CDE) algorithm. The simulation results are compared to an RBF based PID controller that is designed via the same method, a conventional gain-scheduling baseline PI controller developed by NREL, an optimal RBF based PI controller, and a FOPI controller. The simulations indicate that the RBF based FOPID improves the control performance of the benchmark wind turbine in comparison to the other controllers, while the applied loads to the structure are mitigated. To validate the performance and robustness, all controllers are implemented on FAST wind turbine simulator. The superiority of the proposed FOPID controller is depicted in comparison to the other controllers.