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The Science of Making Torque from Wind (TORQUE 2018)

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Room: BL.27 Building
Topic: CM. Poster Session
Form of presentation: Postermanolas
Duration: 75 minutes

Authors:
Tao Han, Martin Evans, Shuchun Zhao

Abstract:
Modern wind turbines have access to highly reliable measurements of important control input signals, such as generator speed and nacelle acceleration. They also have high fidelity numerical models such as in Bladed, which can be used to estimate structural loads under normal and extreme operating conditions. However, if we want to know the structural loads that occurred in a time period on the real turbine, presently this requires instrumentation with strain gauges. These sensors can be unreliable and expensive to install, calibrate and maintain. DNV GL has introduced the digital twin solution for wind asset management, where numerical models of the wind turbine are combined with real data to offer a smart monitoring solution of the asset performance in terms of energy and loading. In this framework, a load estimator is developed to use information with measurements from reliable sensors that are already in use within the turbine controller. The estimated load signal can be used directly for estimation of turbine fatigue accumulation. Furthermore, it can be fed back to the controller to design a load based controller to allow wind turbine to better react to the change of external conditions.

Authors:
Korbinian Schechner, Christoph M. Hackl

Abstract:
In this paper we discuss the detection of rotational periodic torque deviations in variable speed wind turbine systems. These deviations can be caused by faults in the system. The turbine torque is estimated with an observer and an estimate of the aerodynamical torque is calculated. These torques are analysed using a phase-locked loop to detect deviations. The torque observer is based on the model of the turbine drive train. It is modelled as a two-mass-system with a flexible shaft. The design of the observer and the phase-locked loop are shown and their stability is discussed. Simulations show, that the presented concept is capable of detecting the amplitude of the deviations at different periodicities, online and at variable speed.

Authors:
Sjoerd Boersma, Vahab Rostampour, Bart Doekemeijer, Will van Geest, Jan-Willem van Wingerden

Abstract:
The objective of active power control in wind farms is providing grid facilities.Understanding it is important for a smooth wind energy penetration in the energy market. Inthis work, a centralized model predictive controller is applied in the LES code PALM, providingpower reference tracking under constraints on the control signals and turbines fatigue. It willalso be shown that the available power will increase when controlling with applied optimal yawsettings and consequently, the controller can provide better power reference tracking.

Authors:
Edward Hart, William E Leithead, Julian Feuchtwang

Abstract:
It is shown that data which is available to the controller can be used to determine estimates of Cpmax and the drivetrain torque-losses of a wind turbine as part of a regression problem. Gaussian process machine learning is applied in this case along with least squares regression on data generated by aeroelastic simulation of a 5MW wind turbine model. The characteristics of this data are shown to cause problems for least squares regression, with generated predictions which are scattered and not useful. The Gaussian process results however are seen to be tightly clustered and sensitive enough to allow for changes in the estimated quantities to be detected. The results indicate that these formulations could have useful applications in wind turbine monitoring and O&M strategies.

Authors:
Florian Haizmann, David Schlipf, Po Wen Cheng

Abstract:
Lidar-assisted control of wind turbines has been an active field of research during the last years and has recently become more attention from industry, as well. The potential of lidar-assisted feed-forward controllers is shown in various simulation studies and proven in first field-testings. This work wants to further push forward the application of lidar-assisted controlby introducing a method to bring the wind turbine to a different operating point, when a lidar detects an approaching extreme gust. The method reduces the power output of the wind turbine while keeping the rotor rotation constant. This is achieved by a multi-variable feed-forwardingof the generator torque and the pitch angle at the same time. In combination with a classical lidar-assisted feed-forward controller, this leads to futher reduced structural loads under the impact of an extreme gust.

Authors:
Arndt Hoffmann

Abstract:
Control- and operation system of wind turbines must primarily ensure the fully automatic operation of wind turbines in a constantly changing environment. Economic efficiency charges the control system to ensure that the highest possible efficiency is achieved and the mechanical loads caused by disturbances are minimized. The ability of a Kalman filter (Kf) to estimate non-measurable states from a set of measurements using a model of the plant suggests the idea of extending the model of the plant by a model of the disturbance. The states of the disturbance can thus be reconstructed and an easy-to-determine quasi feedforward controller can be used to reject them. This method is called disturbance accommodating control. In this paper Drydens turbulence model, which shapes a white noise signal via a form filter, and an inverse notch filter to model the rotational sampling effect are used for each blade, in contrary to the hitherto used deterministic disturbance models. With this innovative approach the requirement of the Kf derivation is meet and quantitative measures for the Kf tuning matrix, the process noise covariance matrix, are available. The simplified tuning process and the high potential for load reduction are demonstrated for the NREL 5 MW Wind turbine.

Authors:
Fanzhong Meng, Tobias Meyer, Philipp Thomas, Jan Wenske

Abstract:
In this paper a model based approach for condition monitoring and diagnosis of faults in the wind turbine rotor blades is described. The investigation is focused on creating a fault-free reference system, the design and optimization of the controller/observer based on the Disturbance Accommodating Control theory and the multi-objective genetic optimization algorithm in order to further enhance the condition monitoring and faults diagnosis. Test cases are presented by injecting a parameter change, e.g. pitch angle, as an error to the non-linear wind turbine simulation model in order to demonstrate that the proposed method is able to detect the fault on the blades.

Authors:
Jagath Sri Lal Senanayaka, Khang Van Huynh, Kjell G. Robbersmyr

Abstract:
Permanent magnetic machines have gained popularity in wind turbines due to their merits of high efficiency, power density, and reliability.  The wind turbines normally work in a wide range of operations and harsh environments, so unexpected faults may occur and result in less productivity. The common faults in permanent magnet machines are bearing and stator winding faults, which are mainly detected in steady-state operating conditions under constant loads and speeds. However, variable loads and speeds are typical operations in wind turbines and powertrain applications. Therefore, it is important to detect bearing and stator winding faults in variable speed and load conditions. This paper proposes an algorithm to diagnose multiple faults in variable speed and load conditions. The algorithm is based on tracking the frequency orders associated with faults from the normalised order spectrum. The normalised order spectrum is generated by resampling the measured vibration signal via estimated motor speeds. The fault features are then generated from the tracking orders in addition to estimated torque and speed features. Finally, support vector machine algorithm is used to classify the faults. The proposed method is validated using experimental data, and the validated results confirm its usefulness for practical applications.

Authors:
Jagath Sri Lal Senanayaka, Khang Van Huynh, Kjell G. Robbersmyr

Abstract:
Permanent magnet synchronous motors become popular in wind turbines and industrial applications. In critical applications, it is necessary to use robust condition monitoring and fault diagnosis algorithms. The Data-driven approach with machine learning methods is widely used in industrial and research communities as this method does not require a mathematical model of the system, which is difficult to obtain in practical cases. Most of the successful machine learning methods are based on supervised learning approach and labelled training data is required. The supervised learning approach cannot use the unlabelled data, while only a few labelled data is in place in the industry. To overcome the shortage of labelled data, this work uses a deep autoencoder based unsupervised learning method to identify the features of the fault classification algorithm in a self-supervised way. The proposed algorithm uses the benefits of available unlabelled data, but it needs only a few labelled data. The fault classification algorithm is based on artificial neural networks and Bayes classifier. The robustness of the algorithm is improved by fusing the current and vibration information. Experimental results are used to validate the robustness of proposed algorithms under noise conditions, and the results show that the algorithm could classify faults robustly.

Authors:
Vimanyu Kumar, Feike Savenije, Jan-Willem van Wingerden

Abstract:
Increasing energy demands have focused attention on Vertical Axis Wind Turbines (VAWTs). However, accessing high range of power from VAWTs at offshore locations is damage-prone due to increased loads on the turbine blades. Here, we produced an optimal pitching action to reduce the periodic disturbances on the turbine blades of VAWTs without affecting the total power production in a rotation, thereby ensuring the reliability and safe operation of VAWTs. A control technique called Subspace Predictive Repetitive Control (SPRC) is used for the recursive identification to estimate the parameters of VAWT model and further provide an optimal control law accordingly. Basis functions have been used to reduce the dimensionality of the system, following which the system identification has been performed in the lifted domain. These results show a real potential of the data-driven SPRC approach in reducing the turbine loads on VAWTs. The novel mechanisms described above also offer valuable insights into enhancing the functions of VAWTs in a more reliable and damage-free manner.

Authors:
Luis Felipe Recalde, William Leithead

Abstract:
A novel strategy to reduce unwanted swings and motions in floating wind turbines is presented. At above rated wind speeds, the platform, where the wind turbine is mounted, introduces a negative damping effect in the turbine tower which causes the generator speed control loop to become unstable. The proposed strategy assures stability of the control loop by an additive adjustment of the measured generator speed using tower fictitious forces. The developed strategy is independent of the platform and wave dynamics.

Authors:
Adrian Gambier, Anshu Behera

Abstract:
In the present contribution, the modelling of the aerodynamic coefficients of wind turbines are obtained by using an artificial neural network (ANN) and it is analysed from the control applica­tion point of view. The obtained results show that the artificial neural network approach is appropriate to fit the data of the aerodynamic coefficients with high accuracy. The advantage of the approach is that the ANN provides a precise analytical equation that can be embedded in the general dynamic model, which is used for control system design purposes.

Authors:
Yong Jiang

Abstract:
Based on actual damage of couplings and gearbox elastic supporting caused by drive train oscillation which occurred in a low wind speed mountainous wind farm of China, this paper proposes a novel drive train damping method. Instead of common lead component, delay component is used for phase correction. Features of both methods are analysed and compared. Compared to common damper, the proposed one is only effective at center frequency of damping, and will not magnify other frequencies, especially high frequency. Furthermore, for the case of oscillation at more than one frequency, the proposed damper is able to accurately damping simultaneously and respectively, resulting in optimization of drive train operation. With the proposed method , field test has been done at Shanghai electric 2MW-105 wind turbine, and test result shows that the proposed method is effective to solve present problem of drive train oscillation, and can be extended to large-rotor turbines located in slow-speed and mountainous areas of China.

Authors:
Sara Siniscalchi Minna

Abstract:
In order to take part in primary frequency support, wind farms may work in de-loading operation. Different approaches are possible for a wind farm controller to distribute the power set-points for each turbine in order to track the power demanded by the electrical grid. In this work, a classical heuristic distribution strategy is compared with a new model predictive control strategy. The latter ensures the tracking of the power demanded by the Trasmission System Operator while maximizing the active power available for frequency support.

Authors:
Clemens Hübler, Wout Weijtjens, Raimund Rolfes, Christof Devriendt

Abstract:
While substructures of offshore wind turbines become older and begin to reach their design lifetimes, the relevance of measurement based lifetime extension increases. In order to make well-founded decisions on possible lifetime extensions, damage extrapolation based on measurements are needed. However, although for all substructures, fatigue damage calculations were conducted during the design process, there is no conclusive consensus on how to extrapolate 10 min damages to lifetime damages. Furthermore, extrapolating damages is an uncertain process and its actual reliability is unknown. Therefore, the current work uses data of offshore strain measurements to assess different approaches of extrapolating damages, and to investigate the reliability of damage extrapolations. We show that for the present data the most reliable lifetime estimations are possible, if the damage data is split up into wind speed bins. For each wind speed bin, the occurrence probability should be based on data rather than on design documents, and use of mean damages in each is expedient. Furthermore, our results suggest that lifetime estimations based on about 9 months of measurement data are sufficiently accurate.

Authors:
Surya Teja Kandukuri, Andreas Klausen, Khang Van Huynh, Kjell Gunnar Robbersmyr

Abstract:
Failures in wind turbine pitch systems can cause significant outages in offshore wind turbines due to the finite weather windows for maintenance. In a complex system such as the pitch system, a fault in the gearbox can contaminate the motor current signals and result in a misdiagnosis. This paper investigates a sensor fusion technique to reliably detect faults in multistage planetary gearboxes in pitch drives. A support vector machine classifier is used for fault detection and identification with a combination of motor currents and gearbox vibration signals. The approach is validated with two commonly occurring faults, namely, the high-speed shaft bearing and planet gear faults, which are artificially seeded in a scaled pitch drive.

Authors:
Fabrice Guillemin, Hoai-Nam Nguyen, Guillaume Sabiron, Domenico Di Domenico, Matthieu Boquet

Abstract:
In recent years, Light Detection and Ranging (LiDAR) has emerged as a feasible, reliable and accurate remote sensing technology for wind speed measurements. Although much effort has been invested in developing such instruments, the temporal and spatial resolution of the measurements still need to be improved. With the objective to retrieve more accurate information from the sensor raw data, this paper aims to present a method to estimate, in three dimensions and in real time, the wind speed and the wind direction of the incoming wind field. An innovative reconstruction is proposed based on recursive weighted least squares method. The approach is validated for pulsed nacelle LiDAR systems with simulated and real measurement data, obtained during the ongoing experimental campaign of the SMARTEOLE collaborative project.

Authors:
Elena Gonzalez, Jannis Tautz-Weinert, Simon Watson, Julio Melero

Abstract:
Operational data from wind farms is crucial for condition monitoring and performance assessment. Since wind turbines operate in a very stochastic environment, their performance and health state strongly depend on the environmental and operating conditions. Understanding these could help in achieving more reliable WT condition monitoring solutions. Based on the Physics of Failure approach, we analyse in this paper the evolution of statistical performance parameters in four onshore wind farms to investigate environmental and operational conditions that result in underperformance and drive damage. The defined set of performance parameters intends to provide a complete definition of the system’s behaviour. Initial attempts with the proposed technique show a connection of some gearbox and blade failures with high turbulence intensity and particularly high turbulence intensity in standstill conditions. Turbines developing a blade failure also show a high underperformance ratio and a low power factor, indicating a potential abnormal behaviour.

Authors:
David Astrain, Irene Eguinoa, Torben Knudsen

Abstract:
Derating of individual turbines is one of the options to implement wind farm performance optimization, although there are different ways to proceed with such derating at the turbine control level. The present paper develops an option based on the minimal thrust coefficient in accordance with the Cp and Ct contour levels. This strategy is compared for the region below rated wind speed with two other strategies where either the pitch or the tip speed ratio are maintained at their maximum Cp values from normal operation. The study concludes that maintaining the pitch at the optimal value from normal operation produces poorer performance from the thrust and the loads perspective. Practical implementation issues have also been detected.

Authors:
Wai Hou Lio, Mahmood Mirzaei, Gunner C. Larsen

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.

Authors:
Stefano Cacciola, Carlo Emanuele Dionigi Riboldi, Alessandro Croce

Abstract:
A typical concern in rotating systems is related to rotor imbalances, which result typically from pitch misalignment and unbalanced mass distribution. A novel control for simultaneously targeting mass and pitch imbalances on the rotor is presented. Additionally, a novel detection strategy is developed in order to detect the imbalance source out of the behavior of the control action. More in depth, since the control will generate an artificial aerodynamic imbalance which compensates the pre-existent aerodynamic and inertial ones, one can find and interpret the fingerprint of the imbalance source in the behavior of the balancing controller. A clear advantage of this approach is that the imbalance detection is performed while the control keeps the machine working within its operating limits reducing the down-time.

Authors:
Dimitris I. Manolas, Giannis P. Serafeim, Panagiotis K. Chaviaropoulos, Vasilis A. Riziotis, Spyros G. Voutsinas

Abstract:
In the present paper, the ability and efficiency of passive and active control methods for wind turbine load alleviation is assessed. In present work, passive methods refer to blades designed with material or geometry bend-twist coupling (BTC), realized by introducing an offset angle on the plies of the uni-directional material over the spar caps of the blade or by sweeping the blade elastic axis with respect to the pitch axis. Active control methods consider individual pitch control (IPC) or concurrent use of individual pitch and flap control (IPC+IFC) for load reduction. In addition combinations of the abovementioned methods are examined, leading to swept blades with material BTC and to BTC blades with active IPC + IFC. The adopted control methods are assessed through time domain aero-elastic simulations, carried out for the 10MW DTU Reference wind turbine indicating a reduction of the blade root flapwise DEL by 7% with BTC, by 25% with IPC, by 28 with IPC+IFC and by 31% with BTC+IPC+IFC. The concurrent use of IPC + IFC provides significant saving in the pitch duty cycle as opposed to the isolated IPC.

Authors:
Marcelo Nesci Soares, Yves Mollet, Michel Kinnaert, Jan Helsen, Johan Gyselinck

Abstract:
This paper proposes a robust fault detection and isolation (FDI) technique for the power electronic converter (PEC) of doubly-fed induction generator (DFIG) wind turbines, and in particular for open-switch faults herein. It combines fault indicators based on current signal processing and the Clarke transformation of the converter currents and a statistical change detection algorithm, namely a cumulative sum (CUSUM) algorithm that detects significant changes in the variance of the stator reactive power. This allows for a reduction of the false alarm rate compared to an approach relying exclusively on the current analysis. The performance of the FDI technique is verified by both simulation and experimental studies.

Authors:
Cédric Peeters

Abstract:
Condition monitoring increasingly combines knowledge from fields like signal processing, machine learning, and mechanics. Such a diverse approach becomes necessary when dealing with the vast amount of data that is generated by the multitude of sensors that are typically placed on a wind turbine gearbox. Ideally, this approach needs to be automated and scalable. This paper focuses on assessing the performance of such an automated processing framework for gearbox fault detection using vibration measurements. A year of measurements is simulated by stochastic variation of operating conditions and system behavior. A bearing fault is progressively introduced as to track detection capabilities of the framework in such stochastic circumstances. The used signal model is based on previously obtained experience with experimental data sets originating from wind turbine gearboxes. The framework consists of multiple pre-processing steps where each step tries to deal with compensating for the external or unwanted influences such as speed variation or noise. Features are calculated on the pre-processed signals as to see whether the processing scheme can provide any benefit compared to basic traditional statistical indicators. It is shown that the multi-step approach is beneficial and robust for the advanced feature calculation and the early fault detection.

Authors:
Dimitris I. Manolas, Nikos A. Spyropoulos, Giannis P. Serafeim, Vasilis A. Riziotis, Panagiotis K. Chaviaropoulos, Spyros G. Voutsinas

Abstract:
In the present paper, an individual flap control algorithm based on wind speed measurements obtained using a spinner anemometer is presented. The controller uses flap actuators with the aim to remove any deterministic source of load variation on blades, associated with the characteristics of the inflow. Such load variations are concentrated on multiples of the rotational frequency (p multiples) and they are mainly due to wind yaw, inclination and ABL shear. The aim of the controller is to assist operation of the conventional feedback individual pitch controller (IPC) and thereby reduce its control duty cycle. The instantaneous yaw and tilt angles and the wind speed provided by spinner anemometer measurements are low-pass filtered and then translated into 1P and 2P periodic variations. Look-up tables for the amplitude and phase of the flap angle variations (as functions of the wind velocity, yaw and tilt angle and shear exponent) are created through an automated tuning process which is based on deterministic runs using a standard individual flap controller. The performance of the proposed controller is assessed through aero-elastic simulations for the 10MW DTU Reference Wind Turbine for both fatigue and ultimate load reduction and is compared against the baseline design and the IPC.

Authors:
Amin Mahdizadeh

Abstract:
The upstream wind is known as the main source of disturbance to the wind turbine. Hence, having the wind information before it hits the turbine, allows the wind turbine controller to take necessary actions to a proper rejection of the disturbance. Several advanced control methods are designed to exploit the LIDAR data to enhance the wind turbine control. So far, nonlinear model predictive control (NMPC), as a numerical optimization approach, has had the most success in utilizing the LIDAR measured data in favor of improving the control performance. However, due to the immense computational power required, its real-time application has been challenging. In this paper, a design of the NMPC controller is discussed and compared with the recently introduced exact output regulation (EOR) controller which not only utilizes the LIDAR measurements effectively, but is applicable in real-time. The performance of these controllers in rejecting the wind disturbances is compared against the classic baseline feedback controller. Simulation results verify that both controllers show significant improvements in mitigating the fatigue loads on the turbine structures while power production maintained at maximum. It is also shown that the EOR can closely compete with the performance of the NMPC whilst the simulation running times are considerably lower using the EOR scheme that is of high importance in real-time applications.

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