Showing papers on "Blade pitch published in 2018"

Design and Validation of Demanded Power Point Tracking Control Algorithm of Wind Turbine

Abstract: Simple demanded power point tracking (DPPT) control algorithms with a mode switch are proposed and experimentally validated in this study. One is a torque-based control and uses the generator torque with fixed blade-pitch angle. The other is blade-pitch based and uses both the generator torque and blade pitch for DPPT control. Both control algorithms receive power demand from a higherlevel controller and use their control strategies to track it. The two DPPT control algorithms were integrated with the basic torque and pitch control algorithms, and simulations using an in-house code and a high fidelity multi-body dynamic aeroelastic code were performed for steady and dynamic conditions. To verify the algorithms experimentally, wind tunnel tests with a scaled wind turbine having active pitch control capability were performed. From the simulation and the test, the proposed DPPT algorithms integrated into the conventional control algorithm were found to work well with the basic wind turbine power control algorithm. The torquebased control showed better performance in terms of power tracking, but the pitch-based control showed better performance in terms of loading.

Gaussian process operational curves for wind turbine condition monitoring

Abstract: Due to the presence of an abundant resource, wind energy is one of the most promising renewable energy resources for power generation globally, and there is constant need to reduce operation and maintenance costs to make the wind industry more profitable. Unexpected failures of turbine components make operation and maintenance (O&M) expensive, and because of transport and availability issues, the O&M cost is much higher in offshore wind farms (typically 30% of the levelized cost). To overcome this, supervisory control and data acquisition (SCADA) based predictive condition monitoring can be applied to remotely identify early failures and limit downtime, boost production and decrease the cost of energy (COE). A Gaussian Process is a nonlinear, nonparametric machine learning approach which is widely used in modelling complex nonlinear systems. In this paper, a Gaussian Process algorithm is proposed to estimate operational curves based on key turbine critical variables which can be used as a reference model in order to identify critical wind turbine failures and improve power performance. Three operational curves, namely, the power curve, rotor speed curve and blade pitch angle curve, are constructed using the Gaussian Process approach for continuous monitoring of the performance of a wind turbine. These developed GP operational curves can be useful for recognizing failures that force the turbines to underperform and result in downtime. Historical 10-min SCADA data are used for the model training and validation.

Effect of Blade Pitch Angle on the Aerodynamic Characteristics of a Straight-bladed Vertical Axis Wind Turbine Based on Experiments and Simulations

Abstract: The blade pitch angle has a significant influence on the aerodynamic characteristics of horizontal axis wind turbines. However, few research results have revealed its impact on the straight-bladed vertical axis wind turbine (Sb-VAWT). In this paper, wind tunnel experiments and CFD simulations were performed at the Sb-VAWT to investigate the effect of different blade pitch angles on the pressure distribution on the blade surface, the torque coefficient, and the power coefficient. In this study, the airfoil type was NACA0021 with two blades. The Sb-VAWT had a rotor radius of 1.0 m with a spanwise length of 1.2 m. The simulations were based on the k-ω Shear Stress Transport (SST) turbulence model and the wind tunnel experiments were carried out using a high-speed multiport pressure device. As a result, it was found that the maximum pressure difference on the blade surface was obtained at the blade pitch angle of β = 6° in the upstream region. However, the maximum pressure coefficient was shown at the blade pitch angle of β = 8° in the downstream region. The torque coefficient acting on a single blade reached its maximum value at the blade pitch angle of β = 6°. As the tip speed ratio increased, the power coefficient became higher and reached the optimum level. Subsequently, further increase of the tip speed ratio only led to a quick reversion of the power coefficient. In addition, the results from CFD simulations had also a good agreement with the results from the wind tunnel experiments. As a result, the blade pitch angle did not have a significant influence on the aerodynamic characteristics of the Sb-VAWT.

On wind turbine down-regulation control strategies and rotor speed set-point

Abstract: The use of down-regulation or curtailment control strategies for wind turbines offers means of supporting the stability of the power grid and also improving the efficiency of a wind farm. Typically, wind turbine derating is performed by modifying the power set-point and subsequently, the turbine control input, namely generator torque and blade pitch, are acted on to such changes in the power reference. Nonetheless, in addition to changes in the power reference, derating can be also performed by modifying the rotor speed set-point. Thus, in this work, we investigate the performance of derating strategies with different rotor speed set-point, and in particular, their effect on the turbine structural fatigue and thrust coefficient were evaluated. The numerical results obtained from the high-fidelity turbine simulations showed that compared to the typical derating strategy, the derated turbines might perform better with lower rotor speed set-point but it is crucial to ensure such a set-point does not drive the turbine into stalled operations.

Optimal dynamic induction control of a pair of inline wind turbines

Abstract: We study dynamic induction control for mitigating the wake losses of a pair of inline wind turbines. In order to explore control strategies that account for unsteady interactions with the flow, we employ optimal control and adjoint-based optimization in combination with large-eddy simulations. The turbines are represented with an actuator line model. We consider a simple uniform inflow case with two NREL 5 MW turbines spaced 5 diameters apart and find that optimal control leads to 25% gains compared to standard Maximum-Power-Point Tracking (MPPT). It is further found that only the control dynamics of the first turbine are changed, improving wake mixing, while the second turbine controller remains close to the MPPT control. We further synthesize the optimal generator torque and blade pitch controls of the first turbine into a signal that can be periodically used as an open-loop controller, with a Strouhal number of 0.38, while realizing the same gains as the original optimal control signal. Further analysis of the improved wake mixing resulting from the open-loop signal reveals periodic shedding of a three-vortex ring system, which interacts and merges downstream of the first turbine, increasing entrainment of high-speed momentum into the wake. The sensitivity of the open-loop signal to inlet turbulence levels and turbine spacing is also investigated. At low to medium turbulence levels, the control remains effective, while at higher levels, the coherence of the vortex rings degrades too fast for them to remain effective.

APIAN-INF: an aerodynamic and aeroacoustic investigation of pylon-interaction effects for pusher propellers

Abstract: Advanced propellers promise significant fuel-burn savings compared to turbofans When installed on the fuselage in a pusher configuration, the propeller interacts with the wake of the supporting pylon This paper presents an experimental analysis of the aerodynamic and aeroacoustic characteristics of this pylon–propeller interaction An isolated propeller was operated in undisturbed flow and in the wake of an upstream pylon at the large low-speed facility of the German–Dutch wind tunnels (DNW-LLF) Measurements of the pylon-wake characteristics showed that the wake width and velocity deficit decreased with increasing thrust due to the suction of the propeller The installation of the pylon led to a tonal noise penalty of up to 24 dB, resulting from the periodic blade-loading fluctuations caused by the wake encounter The noise penalty peaked in the upstream direction and became increasingly prominent with decreasing propeller thrust setting, due to the associated reduction of the steady blade loads The integral propeller performance was not significantly altered by the pylon-wake encounter process However, at sideslip angles of ±6°, the effective advance ratio of the propeller was modified by the circumferential velocity components induced by the pylon tip vortex The propeller performance improved when the direction of rotation of the propeller was opposite to that of the pylon tip vortex Under this condition, a reduction was measured in the noise emissions due to a favorable superposition of the angular-inflow and pylon-wake effects

Full-Scale Wind Turbine Vibration Signature Analysis

Abstract: A sample of healthy wind turbines from the same wind farm with identical sizes and designs was investigated to determine the average vibrational signatures of the drive train components during normal operation. The units were variable-speed machines with three blades. The rotor was supported by two bearings, and the drive train connected to an intermediate three-stage planetary/helical gearbox. The nominal 2 MW output power was regulated using blade pitch adjustment. Vibrations were measured in exactly the same positions using the same type of sensors over a six-month period covering the entire range of operating conditions. The data set was preliminary validated to remove outliers based on the theoretical power curves. The most relevant frequency peaks in the rotor, gearbox, and generator vibrations were detected and identified based on averaged power spectra. The amplitudes of the peaks induced by a common source of excitation were compared in different measurement positions. A wind speed dependency of broadband vibration amplitudes was also observed. Finally, a fault detection case is presented showing the change of vibration signature induced by a damage in the gearbox.

Power Generation Control of a Monopile Hydrostatic Wind Turbine Using an $\mathcal{H}_{\infty}$ Loop-Shaping Torque Controller and an LPV Pitch Controller

Abstract: We transform the National Renewable Energy Laboratory (NREL) 5-MW geared equipped monopile wind turbine model into a hydrostatic wind turbine (HWT) by replacing its drivetrain with a hydrostatic transmission (HST) drivetrain. Then, we design an $\mathcal {H}_{\infty }$ loop-shaping torque controller (to regulate the motor displacement) and a linear parameter varying (LPV) blade pitch controller for the HWT. To enhance the performances of the pitch control system during the transition region around the rated wind speed, we add an antiwindup (AW) compensator to the LPV controller, which would otherwise have had undesirable system responses due to pitch saturation. The LPV AW pitch controller uses the steady rotor effective wind speed as the scheduling parameter, which is estimated by the light detection and ranging preview. The simulations based on the transformed NREL 5-MW HWT model show that our torque controller achieves a very good tracking behavior, while our pitch controller (no matter with or without AW) gets much improved overall performances over a gain-scheduled PI pitch controller.

Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations

Abstract: The axial induction exerted by utility-scale wind turbines for different operative and atmospheric conditions is estimated by coupling ground-based LiDAR measurements and RANS simulations. The LiDAR data are thoroughly post-processed in order to average the wake velocity fields by using as common reference frame their respective wake directions and the turbine hub location. The various LiDAR scans are clustered according to their incoming wind speed at hub height and atmospheric stability regime, namely Bulk Richardson number. Time-averaged velocity fields are then calculated as ensemble averages of the scans belonging to the same cluster. The LiDAR measurements are coupled with RANS simulations in order to estimate the rotor axial induction for each cluster of the LiDAR data. First, a control volume analysis of the streamwise momentum is applied to the time-averaged LiDAR velocity fields to obtain an initial estimate of the axial induction over the rotor disk. The calculated thrust force is imposed as forcing of an axisymmetric RANS simulation in order to estimate pressure, radial velocity and momentum fluxes. The latter are combined with the LiDAR streamwise velocity field in order to refine the estimate of the rotor axial induction through the control volume approach. This process is repeated iteratively until achieving convergence on the rotor axial induction while minimizing difference between LiDAR and RANS streamwise velocity fields. This procedure allows to single out the reduction in thrust load while the blade pitch angle is increased transitioning from region 2 to 3 of the power curve. Furthermore, an enhanced thrust force is observed for a fixed incoming wind speed and transitioning from stable to convective stability regimes. The presented technique is proposed as a data-driven alternative to the blade element momentum theory typically used with current actuator disk models in order to quantify rotor aerodynamic thrust for different operative and atmospheric conditions.

Blade-Pitch Control for Wind Turbine Load Reductions

Abstract: Large wind turbines are subjected to the harmful loads that arise from the spatially uneven and temporally unsteady oncoming wind. Such loads are the known sources of fatigue damage that reduce the turbine operational lifetime, ultimately increasing the cost of wind energy to the end users. In recent years, a substantial amount of studies has focused on blade pitch control and the use of real-time wind measurements, with the aim of attenuating the structural loads on the turbine blades and rotor. However, many of the research challenges still remain unsolved. For example, there exist many classes of blade individual pitch control (IPC) techniques but the link between these different but competing IPC strategies was not well investigated. In addition, another example is that many studies employed model predictive control (MPC) for its capability to handle the constraints of the blade pitch actuators and the measurement of the approaching wind, but often, wind turbine control design specifications are provided in frequency-domain that is not well taken into account by the standard MPC. To address the missing links in various classes of the IPCs, this thesis aims to investigate and understand the similarities and differences between each of their performance. The results suggest that the choice of IPC designs rests largely with preferences and implementation simplicity. Based on these insights, a particular class of the IPCs lends itself readily for extracting tower motion from measurements of the blade loads. Thus, this thesis further proposes a tower load reduction control strategy based solely upon the blade load sensors. To tackle the problem of MPC on wind turbines, this thesis presents an MPC layer design upon a pre-determined robust output-feedback controller. The MPC layer handles purely the feed-forward and constraint knowledge, whilst retaining the nominal robustness and frequency-domain properties of the pre-determined closed-loop. Thus, from an industrial perspective, the separate nature of the proposed control structure offers many immediate benefits. Firstly, the MPC control can be implemented without replacing the existing feedback controller. Furthermore, it provides a clear framework to quantify the benefits in the use of advance real-time measurements over the nominal output-feedback strategy.

Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

Abstract: Individual pitch control (IPC) provides an important means of attenuating harmful fatigue and extreme loads upon the load bearing structures of a wind turbine. Conventional IPC architectures determine the additional pitch demand signals required for load mitigation in response to measurements of the flap-wise blade-root bending moments. However, the performance of such architectures is fundamentally limited by bandwidth constraints imposed by the blade dynamics. Seeking to overcome this problem, we present a simple solution based upon a local blade inflow measurement on each blade. Importantly, this extra measurement enables the implementation of an additional cascaded feedback controller that overcomes the existing IPC performance limitation and hence yields significantly improved load reductions. Numerical demonstration upon a high-fidelity and nonlinear wind turbine model reveals (i) 60% reduction in the amplitude of the dominant 1P fatigue loads, and (ii) 59% reduction in the amplitude of extreme wind shear induced blade loads, compared to a conventional IPC controller with the same robust stability margin. This paper therefore represents a significant alternative to wind turbine IPC load mitigation as compared to LiDAR-based feedforward control approaches.

Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring

Abstract: Several studies have used the power curve as a critical indicator to assess the performance of wind turbines. However, the wind turbine internal operation is affected by various parameters, particularly by blade pitch angle. Continuous monitoring of blade pitch angle can be useful for power performance assessment of wind turbines. The blade pitch curve describes the nonlinear relationship between pitch angle and hub height wind speed which to date has been little explored for wind turbine condition monitoring. Gaussian Process models are nonlinear and nonparametric technique, based on Bayesian probability theory. Such models have the potential give results quickly and efficiently. In this paper, we propose a Gaussian Process model to predict blade pitch curve of a wind turbine for condition monitoring purposes. The obtained Gaussian Process based blade pitch curve is then compared with a conventional approach based on a binned blade pitch curve for identifying operational anomalies purposes. Finally, the weaknesses and strengths of these methods are summarised. SCADA data from healthy wind turbines are used to train and evaluate the performance of these techniques.

Cyclic Blade Pitch Control Without a Swashplate for Small Helicopters

Abstract: This paper presents the design, dynamical model, and experimental investigation of an articulated rotor that affords cyclic pitch authority in small unmanned air vehicle rotorcraft without requirin...

Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator

Abstract: Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG) systems aim to provide clean and reliable electrical power. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN) is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT) generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances.

Bidirectional Long Short-Term Memory Networks for Rapid Fault Detection in Marine Hydrokinetic Turbines

Abstract: Fault detection remains a key problem for reducing the operation and maintenance costs of marine hydrokinetic (MHK) turbines. With this in mind, a deep bidirectional long short-term memory (Bi-LSTM) network for rapid detection of MHK turbine faults is presented. The effectiveness of the proposed scheme is validated using simulated time-series sensor data gathered from a novel Fatigue, Aerodynamics, Structures, and Turbulence (FAST)-based MHK turbine simulation platform. Four-factor analysis of variance is performed along with post-hoc testing at the 95% confidence level to establish the model's capabilities. Operating conditions, input length, training pitch, and generalization pitch are used as factors in this experiment. Models are trained on data consisting of 500 baseline and 500 faulty examples of a single blade pitch imbalance. To evaluate generalization performance, models are applied, without fine-tuning, on the remaining levels of fault. Over 70% mean generalization accuracy in all cases, with an optimal accuracy over 95% given 1 second lengths of data, is observed. No significant difference is found due to varying operating conditions, indicating a robust model. Training on lower levels of fault demonstrated greater generalization capability. To the best of our knowledge, this is the first time Bi-LSTM has been applied as a building block, and that a deep learning-based method has been applied to only time-series sensor measurements for fault detection in MHK turbines.