scholarly journals Load Reduction for Wind Turbines: an Output Constrained, Subspace Predictive Repetitive Control Approach

2021 ◽  
Author(s):  
Yichao Liu ◽  
Riccardo Ferrari ◽  
Jan-Willem van Wingerden

Abstract. Individual Pitch Control (IPC) is a well-known approach to reduce blade loads on wind turbines. Although very effective, IPC usually requires high levels of actuator activities, which significantly increases the pitch Actuator Duty Cycle (ADC). This will subsequently result in an increase of the wear on the bearings of the blades and make the current IPC design not economical viable. An alternative approach to this issue is to reduce the actuator activities by incorporating the output constraints in IPC. In this paper, a fully data driven IPC approach, which is called constrained Subspace Predictive Repetitive Control (cSPRC) is introduced. The output constraints can be explicitly considered in the control problem formulation via a Model Predictive Control (MPC) approach. The cSPRC approach will actively produce the IPC action for the necessary load reduction when the blade loads violate the output constraints. In this way, actuator activities can be significantly reduced. Two kinds of scenarios are simulated to illustrate the unique applications of the proposed method: wake-rotor overlap and turbulent wind conditions. Simulation results show that the developed cSPRC is able to account for the output constraints into the control problem formulation. Since the IPC action from cSPRC is only triggered to prevent violating the output constraints, the actuator activities are significantly reduced. This will help to reduce the pitch ADC, thus leading to an economical viable load control strategy. In addition, this approach allows the wind farm operator to design conservative bounds to guarantee the safety of the wind turbine control system.

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1549 ◽  
Author(s):  
Rongyong Zhao ◽  
Daheng Dong ◽  
Cuiling Li ◽  
Steven Liu ◽  
Hao Zhang ◽  
...  

Increasing maintenance costs will hinder the expansion of the wind power industry in the coming decades. Training personnel, field maintenance, and frequent boat or helicopter visits to wind turbines (WTs) is becoming a large cost. One reason for this cost is the routine turbine inspection repair and other stochastic maintenance necessitated by increasingly unbalanced figure loads and unequal turbine fatigue distribution in large-scale offshore wind farms (OWFs). In order to solve the problems of unbalanced fatigue loads and unequal turbine fatigue distribution, thereby cutting the maintenance cost, this study analyzes the disadvantages of conventional turbine fatigue definitions. We propose an improved fatigue definition that simultaneously considers the mean wind speed, wind wake turbulence, and electric power generation. Further, based on timed automata theory, a power dispatch approach is proposed to balance the fatigue loads on turbines in a wind farm. A control topology is constructed to describe the logical states of the wind farm main controller (WFMC) in an offshore wind farm. With this novel power control approach, the WFMC can re-dispatch the reference power to the wind turbines according to their cumulative fatigue value and the real wind conditions around the individual turbines in every power dispatch time interval. A workflow is also designed for the control approach implementation. Finally, to validate this proposed approach, wind data from the Horns Rev offshore wind farm in Denmark are used for a numerical simulation. All the simulation results with 3D and 2D figures illustrate that this approach is feasible to balance the loads on an offshore wind farm. Some significant implications are that this novel approach can cut the maintenance cost and also prolong the service life of OWFs.


2020 ◽  
Vol 10 (6) ◽  
pp. 1978 ◽  
Author(s):  
Juchuan Dai ◽  
Xin Yang ◽  
Wenxian Yang ◽  
Guoqiang Gao ◽  
Mimi Li

Active wake management (AWM) via yaw control has been discussed in recent years as a potential way to improve the power production of a wind farm. In such a technique, the wind turbines will be required to work frequently at misaligned yaw angles in order to reduce the vortices in the wake area behind the turbines. However, today, it is still not very clear about how yaw operation affects the dynamics and power generation performance of the wind turbines. To further understand the effects of yaw operation, numerical research is conducted in this paper. In the study, the optimal size of the flow field used in the computational fluid dynamics (CFD) calculation was specifically discussed in order to obtain an efficient numerical model to quickly and accurately predict the dynamics and the performance of the turbines. Through this research, the correlation between the blade loads during yaw and non-yaw operations is established for aiding yaw control, and the blade loads and power generation performances of the wind turbine during yaw operation under different wind shear and blade deflection conditions are analyzed for understanding the effects of yaw operation. It is found that the optimal size of the flow field for performing efficient and accurate CFD calculations does exist. The misaligned yaw operation generally tends to decrease the loads acting on the blade. However, the aerodynamic energy captured by the turbine rotor and blade loads during yaw operation is not only dependent on the yaw angle of the rotor but is also affected by wind speed, rotor speed, the pitch angle of the blades, blade deflection, and wind shear. Particularly, it is interestingly found that wind shear can cause undesirable fluctuation of the power, which will challenge the power quality of the wind farm if no measures are taken.


2018 ◽  
Vol 8 (12) ◽  
pp. 2639 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Francesco Berno ◽  
Ludovico Terzi

Megawatt-scale wind turbine technology is nowadays mature and, therefore, several technical improvements in order to optimize the efficiency of wind power conversion have been recently spreading in the industry. Due to the nonstationary conditions to which wind turbines are subjected because of the stochastic nature of the source, the quantification of the impact of wind turbine power curve upgrades is a complex task and in general, it has been observed that the efficiency of the upgrades can vary considerably depending on the wind flow conditions at the microscale level. In this work, a test case of wind turbine control system improvement was studied numerically and through operational data. The wind turbine is multi-megawatt; it is part of a wind farm sited in a complex terrain in Italy, featuring 17 wind turbines. The analyzed control upgrade is an optimization of the revolutions per minute (rpm) management. The impact of this upgrade was quantified through a method based on operational data: It consists of the study, before and after the upgrade, of the residuals between the measured power output of the wind turbine of interest and an appropriate model of the power output itself. The input variables for the model were selected to be some operational parameters of the nearby wind turbines: They were selected from the data set at disposal with a stepwise regression algorithm. This work also includes a numerical characterization of the problem, by means of aeroelastic simulations performed with the FAST software: By mimicking the pre- and post-upgrade generator rpm–generator torque curve, it is subsequently possible to estimate how the wind turbine power curve changes. The main result of this work is that the two estimates of production improvement have the same order of magnitude (1.0% of the production below rated power). In general, this study sheds light on the perspective of employing not only operational data, but also a sort of digital replica of the wind turbine of interest, in order to reliably quantify the impact of control system upgrades.


2010 ◽  
Vol 130 (11) ◽  
pp. 1002-1009 ◽  
Author(s):  
Jorge Morel ◽  
Hassan Bevrani ◽  
Teruhiko Ishii ◽  
Takashi Hiyama

Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.


Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 693
Author(s):  
Anna Dóra Sæþórsdóttir ◽  
Margrét Wendt ◽  
Edita Tverijonaite

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.


2021 ◽  
pp. 002029402110130
Author(s):  
Xian Wang ◽  
Qian-cheng Zhao ◽  
Xue-bing Yang ◽  
Bing Zeng

The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies.


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