Investigation on the Effect of the Baseline Control System on Dynamic and Fatigue Characteristics of Modern Wind Turbines

Yuan, Chenyang; Li, Jing; Xie, Yuanfu; Bai, Weifeng; Wang, Jianyou

doi:10.3390/app12062968

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Similar work was conducted by Chenyang Yuan et al [19], who also developed a comparative study of the effect on fatigue life between a fixed-pitch and a blade-pitch control scheme, where the latter was based on a PID control system. The authors referred to the blade-pitch control as a baseline (classic) control system (BCS).…”

Section: Prior Workmentioning

confidence: 85%

Self-Optimizing Control System to Maximize Power Extraction and Minimize Loads on the Blades of a Wind Turbine

Rivas,

Malo,

Minchala

et al. 2023

Machines

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This research proposes a methodology for designing and testing a self-optimizing control (SOC) algorithm applied to a wind energy conversion system (WECS). The SOC maximizes WECS power output and reduces the mechanical stress of the wind turbine (WT) blades by optimizing a multiobjective cost function. The cost function computation uses a combined blade element momentum (BEM) and thin-wall beam (TWB) model for calculating wind the turbine power output and blades’ stress. The SOC deployment implies a low computational cost due to an optimization space reduction via a matrix projection applied to a measurement vector, based on a prior offline calculation of a projection matrix, H. Furthermore, the SOC optimizes the operation of the WECS in the presence of uncertainty associated with the wind speed variation by controlling a linear combination of measured variables to a set point. A MATLAB simulation of a wind turbine model allows us to compare the WECS operating with the SOC, a baseline classic control system (BCS), and a nonlinear model predictive controller (NMPC). The SOC algorithm is evaluated in terms of power output, blades’ stress, and computational cost against the BCS and NMPC. The power output and blades’ stress performance of the SOC algorithm are compared with that of the BCS and NMPC, showing a significant improvement in both cases. The simulation results demonstrate that the proposed SOC can effectively optimize a WECS operation in real time with minimal computational costs.

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Section: Prior Workmentioning

confidence: 85%

Self-Optimizing Control System to Maximize Power Extraction and Minimize Loads on the Blades of a Wind Turbine

Rivas,

Malo,

Minchala

et al. 2023

Machines

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“…Large wind turbines place higher demands on the safe and reliable operation of their components [3]. As size and power ratings increase, the analysis of support structures and blades subjected to wind loads become increasingly important [4].…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model

Zou

Wang

et al. 2022

Applied Sciences

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The research on damage detection in wind turbine blades plays an important role in reducing the risk of shut down in wind turbines. Rapid and accurate damage identification by using efficient detection models is the focus of the current research on damage detection in wind turbine blades. To solve the problems of the complex structure of the model and high time consumption in deep learning models, an improved broad learning system (BLS) model using the algorithm of chunking based on non-local means (NLMs) was proposed, which was called the CBNLM-BLS. The chunked, in-parallel accelerated integral image approach was used to optimize the NLM to speed up the BLS. Experiment results showed that the proposed model achieved a classification accuracy of 99.716%, taking 28.662 s to detect damage in the wind turbine blades. Compared with deep neural network models, such as ResNet, AlexNet and VGG-19, the proposed CBNLM-BLS had higher classification accuracy, shorter training time and less complex model construction and parameters. Compared with traditional BLSs, the CBNLM-BLS had less time complexity. It is of great significance to identify damage in wind turbine blades more efficiently.

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“…Many investigations dealing with the study of the control method on the dynamic characteristics as well as performance of small and large scale HAWT were performed recently as reported for instance in [15][16][17][18][19]. Another parameter which affects the dynamic response of the wind turbine is the solidity which has recently been attracting a certain attention in the literature.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation of the Effect of Blades Number on the Dynamic Response of a Small Horizontal-Axis Wind Turbine

et al. 2022

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The new energy scenario is rising a strong interest towards the distributed production from renewable sources; among them, wind seems to be one of the most interesting and at the same time critical due to the challenges of the conversion technology. Operation of small wind energy conversion systems is generally very complex due the extremely variable regime of operation and the high turbulent wind, so that a deep knowledge of the dynamic response of such devices is crucial not only for optimising their performances but also to make them comfortable to be used in residential areas. For these reasons, the present work is focused on analysing the effect in changing the number of blades on a small horizontal axis wind turbine through an experimental campaign in the wind tunnel. The turbine dynamics has been characterised running some “wind ramp” tests and analysing the rotor capability in following the wind as well the vibrations transmitted by the turbine during the operation. Results demonstrate that a higher number of blades, despite a small decrease of performance, can make the machine more efficient in operating at low wind regimes. At the same time, a higher number of blades can make the rotor efficient even at lower speed of rotation, thus limiting the risks of having high magnitude vibrations.

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Investigation on the Effect of the Baseline Control System on Dynamic and Fatigue Characteristics of Modern Wind Turbines

Cited by 5 publications

References 42 publications

Self-Optimizing Control System to Maximize Power Extraction and Minimize Loads on the Blades of a Wind Turbine

Self-Optimizing Control System to Maximize Power Extraction and Minimize Loads on the Blades of a Wind Turbine

Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model

Experimental and Numerical Investigation of the Effect of Blades Number on the Dynamic Response of a Small Horizontal-Axis Wind Turbine

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