Showing papers on "Blade pitch published in 2021"

Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

Abstract: Wind speed fluctuations and load demand variations represent the big challenges against wind energy conversion systems (WECS). Besides, the inefficient measuring devices and the environmental impacts (e.g. temperature, humidity, and noise signals) affect the system equipment, leading to increased system uncertainty issues. In addition, the time delay due to the communication channels can make a gap between the transmitted control signal and the WECS that causes instability for the WECS operation. To tackle these issues, this paper proposes an adaptive neuro-fuzzy inference system (ANFIS) as an effective control technique for blade pitch control of the WECS instead of the conventional controllers. However, the ANFIS requires a suitable dataset for training and testing to adjust its membership functions in order to provide effective performance. In this regard, this paper also suggests an effective strategy to prepare a sufficient dataset for training and testing of the ANFIS controller. Specifically, a new optimization algorithm named the mayfly optimization algorithm (MOA) is developed to find the optimal parameters of the proportional integral derivative (PID) controller to find the optimal dataset for training and testing of the ANFIS controller. To demonstrate the advantages of the proposed technique, it is compared with different three algorithms in the literature. Another contribution is that a new time-domain named figure of demerit is established to confirm the minimization of settling time and the maximum overshoot in a simultaneous manner. A lot of test scenarios are performed to confirm the effectiveness and robustness of the proposed ANFIS based technique. The robustness of the proposed method is verified based on the frequency domain conditions that are driven from Hermite–Biehler theorem. The results emphases that the proposed controller provides superior performance against the wind speed fluctuations, load demand variations, system parameters uncertainties, and the time delay of the communication channels.

Individual Blade Pitch Control of Floating Offshore Wind Turbines for Load Mitigation and Power Regulation

Abstract: This article proposes a new strategy for individual blade pitch control to regulate power production while simultaneously alleviating structural loads on spar-type floating offshore wind turbines. Individual blade pitch control types of algorithms for offshore wind turbines are sparse in the literature though there are expected benefits from experience on such types of controllers for onshore wind turbines. Wind turbine blade pitch actuators are primarily used to maintain the rated power production at the above-rated wind speeds, and therefore, control algorithms are usually developed only to regulate power production. The scope of reducing structural loads using individual pitch control has been proven to be very promising over the last decade, and numerous individual pitch control algorithms have been proposed by researchers. However, reduction in structural loads often results in a degradation in power production and regulation. Furthermore, improving power regulation often has a detrimental effect on the floating platform motion. In this article, a new control strategy is proposed to achieve the two competing objectives. The proposed controller combines a low-authority linear-quadratic (LQ) controller with an integral action to reduce the 1P (once per revolution) aerodynamic loads while regulating power production using the same pitch actuators that are traditionally used only to optimize power production. The proposed controller is compared against the baseline controller (BC) used by the state-of-the-art wind turbine simulator FAST using a high-fidelity aeroelastic offshore wind turbine model. Numerical results show that the proposed controller offers improved performance in optimizing power production and reducing wind turbine and platform loads compared with the BC over an envelope of wind-wave loading environment.

Fault-Tolerant Individual Pitch Control for Load Mitigation in Wind Turbines With Actuator Faults

Abstract: The sizes of wind turbines have been steadily increasing over the past decade, especially on offshore sites. The greater structural flexibility of such machines necessitates the development of reliable load mitigation techniques to alleviate the effects of asymmetric wind loads and fatigue. Given the importance of such techniques, this article proposes a reliable load mitigation scheme, referred to as “fault-tolerant individual pitch control,” for individually adjusting the pitch angle of wind turbine blades in the presence of blade pitch actuator faults. The proposed scheme consists of a collective pitch control augmented with an individual pitch control, and a fault detection and diagnosis system. Simulation results using an offshore wind turbine illustrate the effectiveness as well as fault-tolerance capability of the developed scheme compared to controlling the pitch angles collectively. The developed scheme not only ensures an almost equal output power with reduced loads on the turbine, but also tolerates the effects of possible faults in pitch actuators of the blades.

Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Abstract: Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

Abstract: It is a common sense expectation that the efficiency of wind turbines should decline with age, similarly to what happens with most technical systems Due to the complexity of this kind of machine and the environmental conditions to which it is subjected, it is far from obvious how to reliably estimate the impact of aging In this work, the aging of five Vestas V52 wind turbines is analyzed The test cases belong to two different sites: one is at the Dundalk Institute of Technology in Ireland, and four are sited in an industrial wind farm in a mountainous area in Italy Innovative data analysis techniques are employed: the general idea consists of considering appropriate operation curves depending on the working control region of the wind turbines When the wind turbine operates at fixed pitch and variable rotational speed, the generator speed-power curve is studied; for higher wind speed, when the rotational speed has saturated and the blade pitch is variable, the blade pitch-power curve is considered The operation curves of interest are studied through the binning method and through a support vector regression with a Gaussian kernel The wind turbine test cases are analyzed vertically (each in its own history) and horizontally, by comparing the behavior at the two sites for the given wind turbine age The main result of this study is that an evident effect of aging is the worsening of generator efficiency: progressively, less power is extracted for the given generator rotational speed Nevertheless, this effect is observed to be lower for the wind turbines in Italy (order of −15% at 12 years of age with respect to seven years of age) with respect to the Dundalk wind turbine, which shows a sharp decline at 12 years of age (−88%) One wind turbine sited in Italy underwent a generator replacement in 2018: through the use of the same kind of data analysis methods, it was possible to observe that an average performance recovery of the order of 2% occurs after the component replacement It also arises that for all the test cases, a slight aging effect is visible for higher wind speed, which can likely be interpreted as due to declining gearbox efficiency In general, it is confirmed that the aging of wind turbines is strongly dependent on the history of each machine, and it is likely confirmed that the technology development mitigates the effect of aging

Wind Turbine Operation Curves Modelling Techniques

Abstract: Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity.

A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox

Abstract: Wind turbines (WTs) are often operated in harsh and remote environments, thus making them more prone to faults and costly repairs. Additionally, the recent surge in wind farm installations have resulted in a dramatic increase in wind turbine data. Intelligent condition monitoring and fault warning systems are crucial to improving the efficiency and operation of wind farms and reducing maintenance costs. Gearbox is the major component that leads to turbine downtime. Its failures are mainly caused by the gearbox bearings. Devising condition monitoring approaches for the gearbox bearings is an effective predictive maintenance measure that can reduce downtime and cut maintenance cost. In this paper, we propose a hybrid intelligent condition monitoring and fault warning system for wind turbine’s gearbox. The proposed framework encompasses the following: a) clustering filter- (based on power, rotor speed, blade pitch angle, and wind speed signals)-using the automatic clustering model and ant bee colony optimization algorithm (ABC), b) prediction of gearbox bearing temperature and lubrication oil temperature signals- using variational mode decomposition (VMD), group method of data handling (GMDH) network, and multi-verse optimization (MVO) algorithm, and c) anomaly detection based on the Mahalanobis distances and wavelet transform denoising approach. The proposed condition monitoring system was evaluated using 10 min average SCADA datasets of two 2 MW on-shore wind turbines located in the south of Sweden. The results showed that this strategy can diagnose potential anomalies prior to failure and inhibit reporting alarms in healthy operations.

Constrained Control of Moored Ocean Current Turbines With Cyclic Blade Pitch Variations

Abstract: In this article, a new method to control an ocean current turbine (OCT) is examined. The key innovation, inspired by helicopter control, is to use cyclic blade pitch angle variations. Output variance constrained controllers are designed for OCT flight control and their performance is analyzed.

Experimental Testing of a Preview-Enabled Model Predictive Controller for Blade Pitch Control of Wind Turbines

Abstract: Model predictive control (MPC) is a control method that involves determining the input to a dynamical system as the solution to an optimization problem that is solved online. In the wind turbine research literature, MPC has received considerable attention for its ability to handle both actuator constraints and preview disturbance information about the oncoming wind, which can be provided by a lidar scanner. However, while many studies simulate the wind turbine response under MPC, very few physical tests have been carried out, likely due in part to the difficulties associated with solving the MPC problem in real time. In this work, we implement MPC on an experimental, scaled wind turbine operating in a wind tunnel testbed, using an active grid to create reproducible wind sequences and a hot-wire anemometer to generate upstream wind measurements. To our knowledge, this work presents the first physical test of MPC for blade pitch control of a scaled wind turbine. We compare two MPC strategies: one including preview disturbance information and one without. Our results provide further evidence that feedforward control can improve wind turbine performance in transition and above-rated conditions without increasing actuation requirements, which we hope will encourage industry experimentation and uptake of feedforward control methods. We also provide a high-level analysis and interpretation of the computational performance of the chosen approach. This work builds upon the results of an earlier study, which considered unconstrained optimal blade pitch control.

Adaptive Robust Fault-Tolerant Control Design for Wind Turbines Subject to Pitch Actuator Faults

Abstract: This paper proposes an adaptive fault tolerant control (FTC) design for a variable speed wind turbine (WT) operating in the high wind speeds region. It aims at mitigating pitch actuator faults and regulating the generator power to its rated value, thereby reducing the mechanical stress in the high wind speeds region. The proposed FTC design implements a sliding mode control (SMC) approach with an adaptation law that estimates the upper bounds of the uncertainties. System stability and uniform boundedness of the outputs was proven using the Lyapunov stability theory. The proposed approach was validated on a 5 MW three-blade wind turbine modeled using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures and Turbulence (FAST) wind turbine simulator. The controller’s performance was assessed in the presence of several pitch actuator faults and turbulent wind conditions. Its performance was also compared to that of a standard SMC approach. Mitigation of blade pitch actuator faults, generation of uniform power, smoother pitching actions and reduced chattering compared to standard SMC approach are among the main features of the proposed design.

High-fidelity CFD validation and assessment of ducted propellers for aircraft propulsion

Abstract: This paper presents validation and assessment of ducted propellers for aircraft propulsion. Numerical methods and simulation strategies are put forward, including steady/unsteady high-fidelity computational fluid dynamics (CFD) simulations and simpler momentum-based methods. The validation and comparisons of the methods are made using a ducted propeller proposed by NASA. Simulations are also performed and analyzed at extended advance ratios, blade pitch setting, and cross-wind angles. Comparisons are also made with open propeller counterparts. The ducted propeller shows superior performance over its unducted counterpart in hover and at low advance ratios. The major thrust gain is identified from the combination of duct leading-edge suction and the higher pressure at the diffuser exit. The propeller is off-loaded due to the higher inflow velocities. The ducted propeller is also shown to have less intrusive wake features at low axial speeds. However, as the advance ratio increases, the duct thrust contribution becomes negative and the ducted propeller becomes deficient, due to growing high-pressure areas at the leading edge. At cross-wind, high-fidelity CFD simulations offer accurate aerodynamic loads predictions despite the complex flow features. The duct surface separation is found to be delayed due to the propeller suction, while the propeller is shown shielded by the duct, thereby suffering less from the unbalanced inflow velocities. Decomposition of induced velocities by each part is carried out and presented. A large, nonlinear extra induction component, due to mutual interactions of the duct and the propeller, is observed and found favorable for the performance augmentation.

Effect of blade pitch angle on the aerodynamic characteristics of a twisted blade horizontal axis wind turbine based on numerical simulations

Abstract: The present work includes a study of the impact of varying pitch angles and angular velocity on the performance parameters of a horizontal axis wind turbine using computational fluid dynamics. Simulations have been made using commercial Ansys 15 software. Seven pitch angles are chosen for study, i.e., 0° , 5 ° , 10° , 15° , 20° , 25° , and 28°, and two angular velocity values of 1.57 rad/sec and 2.22 rad/sec are used for simulation. The turbulence model used is shear stress transport (SST) K-ω. A detailed study of the influence of pitch angle on the aerodynamic characteristics of the wind turbine is highlighted. Performance parameters like torque and power have been found to exhibit random variability with a change in wind velocity and pitch angle. The verification of computational fluid dynamics (CFD) with the standard empirical formula is highlighted. The best pitch angle is noted for the best power coefficient.