Active tip deflection control for wind turbines

Liew, Jaime; Lio, Wai Hou; Urbán, Albert M.; Holierhoek, Jessica G.; Kim, Taeseong

doi:10.1016/j.renene.2019.12.036

Cited by 12 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PI controller [61][62][63][64][65][66] or PID controller [67,68] is usually used since they are simple and robust. Other methods based on adaptive concepts to compensate for the uncertainties or disturbance as in [69][70][71][72][73].…”

Section: Blade Pitch Control In Regionmentioning

confidence: 99%

“…The collective pitch control (CPC) and individual pitch control (IPC) are widely used blade pitch control schemes, where CPC technique means that the pitch commanded value is the same for all blades, and individual pitch control (IPC) has the specific goal of load reduction where the pitch angle reference values are different on each blade [63,64,66,76,78]. In the following section, current control technologies researched for FOWT to stabilize the motion while maximizing power performance are reviewed which include CPC, IPC, and hybrid IPC-CPC strategy.…”

Section: Blade Pitch Control In Regionmentioning

confidence: 99%

See 1 more Smart Citation

Recent Control Technologies for Floating Offshore Wind Energy System: A Review

Truong

Dang

et al. 2020

Int. J. of Precis. Eng. and Manuf.-Green Tech.

View full text Add to dashboard Cite

This paper presents the recent control technologies being researched for floating offshore wind energy system (FOWES). FOWES has been getting many attentions recently as an alternative energy system utilizing vast sustainable wind resource away from land with little restriction by human societies, artificial and natural obstacles. However, not only due to the harsh environmental conditions such as strong wind, wave, and current, but also due to the platform motions such as surge, sway, heave, pitch, roll, and yaw, there could occur many problems including less energy capture than expected, frequent emergency stops, turbine structural instability, and fatigues resulting in early failures, which stay the levelized cost of energy (LCOE) still high compared to conventional fixed offshore wind energy system. These risks could be lowered by operating the turbine close to the optimum point and harvesting wind energy efficiently even under strong wind conditions with the properly applied control technologies, while reducing the loads on structural components. Many researches have been actively going on not only by numerical approaches, but also by experimental tests. This study is wrapping the most recent researches on control technologies for promising floating offshore wind energy system according to different substructure designs such as a spar type, semi-submergible type, tension-leg platform (TLP) type, and barge type, and discusses about its challenges as well.

show abstract

Section: Blade Pitch Control In Regionmentioning

confidence: 99%

Section: Blade Pitch Control In Regionmentioning

confidence: 99%

Recent Control Technologies for Floating Offshore Wind Energy System: A Review

Truong

Dang

et al. 2020

Int. J. of Precis. Eng. and Manuf.-Green Tech.

View full text Add to dashboard Cite

show abstract

“…Another important problem with a potential for data driven solutions is the estimation and forecasting of the blade tip deflection for adaptive control purposes as discussed in, among others 13 . Physically measuring the blade tip deflection is possible but is a challenging task which requires advanced sensor setups.…”

Section: Introductionmentioning

confidence: 99%

Virtual sensors for wind turbines with machine learning‐based time series models

Dimitrov

Göçmen

2022

Wind Energy

View full text Add to dashboard Cite

Modern wind turbines have multiple sensors installed and provide constant data stream outputs; however, there are some important quantities where installing physical sensors is either impractical or the sensor technology is not sufficiently advanced.An example of such a problem is, for example, sensing the shape and location of wake-induced wind deficits caused by upwind turbines-a feature which would have relevant application in wind farm control; however, it is hard to detect physically due to the need of scanning the airflow in front of the turbine in multiple locations. Another control-related example is monitoring and predicting the blade tip-tower clearance. A "virtual sensor" can be created instead, by establishing a mathematical relationship between the quantity of interest and other, measurable quantities such as readings from already available sensors (e.g., SCADA, lidars, and met-masts).Machine Learning (ML) approaches are suitable for this task as ML algorithms are capable of learning and representing complex relationships. This study details the concept of ML-based virtual sensors and showcases three specific examples: blade root bending moment prediction, detection of wind turbine wake center location, and forecasting of blade tip-tower clearance. All examples utilize sequence models (Long Short-Term Memory, LSTM) and use aeroelastic load simulations to generate wind turbine response time series and test model performance. The data types used in the examples correspond to channels that would be available from high-frequency SCADA data combined with a blade and tower load measurement system. The resulting model performance demonstrates the feasibility of the ML-based virtual sensor approach.

show abstract

“…Additionally, Ke et al [ 11 ] investigated wind-induced fatigue of large turbines in periodic wind effects. Furthermore, Liew et al [ 12 ] studied individual pitch control (ICP) to prevent alternate bending by guiding blades along a preset trajectory but did not consider feasible sensor solutions for measuring tip deflection yet.…”

Section: Introductionmentioning

confidence: 99%

“…Figure12. Effect of mounting angles on resulting bending patterns: mounting angles simulated as β and γ varying from −8°to 8°in 4°steps for a rotation frequency of f 0 = 0.2 Hz.…”

mentioning

confidence: 99%

Moving Accelerometers to the Tip: Monitoring of Wind Turbine Blade Bending Using 3D Accelerometers and Model-Based Bending Shapes

Loss

Bergmann

2020

Sensors

View full text Add to dashboard Cite

Increasing the length of wind turbine blades for maximum energy capture leads to larger loads and forces acting on the blades. In particular, alternate bending due to gravity or nonuniform wind profiles leads to increased loads and imminent fatigue. Therefore, blade monitoring in operation is needed to optimise turbine settings and, consequently, to reduce alternate bending. In our approach, an acceleration model was used to analyse periodically occurring deviations from uniform bending. By using hierarchical clustering, significant bending patterns could be extracted and patterns were analysed with regard to reference data. In a simulation of alternate bending effects, various effects were successfully represented by different bending patterns. A real data experiment with accelerometers mounted at the blade tip of turbine blades demonstrated a clear relation between the rotation frequency and the resulting bending patterns. Additionally, the markedness of bending shapes could be used to assess the amount of alternate bending of the blade in both simulations and experiment.s The results demonstrate that model-based bending shapes provide a strong indication for alternate bending and, consequently, can be used to optimise turbine settings.

show abstract

Active tip deflection control for wind turbines

Cited by 12 publications

References 29 publications

Recent Control Technologies for Floating Offshore Wind Energy System: A Review

Recent Control Technologies for Floating Offshore Wind Energy System: A Review

Virtual sensors for wind turbines with machine learning‐based time series models

Moving Accelerometers to the Tip: Monitoring of Wind Turbine Blade Bending Using 3D Accelerometers and Model-Based Bending Shapes

Contact Info

Product

Resources

About