Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Bakhshi, Roozbeh; Sandborn, Peter

doi:10.1002/we.2493

Cited by 17 publications

(8 citation statements)

References 32 publications

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“…Advanced sensors such as lidar, despite their high cost, had a great potential in maximizing wind power capture in large WTs. The applications of lidar in WTs, such as measurement of wind speed and direction, installation location, and cost calculation, were detailed 25,28 . Mikkelsen et al 26 solved the problem of the centrifugal force generated by blade rotation and electrical interference of the generator and proposed to install three lidars in the rotating spinner of 2.5 MW WT.…”

Section: Maximize Energy Capture Of a Single Wtmentioning

confidence: 99%

“…The applications of lidar in WTs, such as measurement of wind speed and direction, installation location, and cost calculation, were detailed. 25,28 Mikkelsen et al 26 solved the problem of the centrifugal force generated by blade rotation and electrical interference of the generator and proposed to install three lidars in the rotating spinner of 2.5 MW WT. The measuring reliability of spinner lidar was proved by the wind farm experiment, but the data were not utilized as the input of yaw system.…”

Section: Based On Wind Direction Estimationmentioning

confidence: 99%

“…In this context, the control method based on wind direction estimation was proposed. 24 On one side, the future wind direction information was measured by advanced equipment [25][26][27][28] or predicted by models such as time series model. [29][30][31][32][33][34][35][36][37][38][39] On the other side, the yaw system determined the optimal yaw position for the next period based on future wind direction information.…”

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confidence: 99%

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Review of control strategy of large horizontal‐axis wind turbines yaw system

et al. 2020

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In order to meet the increasing demand of wind energy utilization, wind turbines (WTs) are developing toward the trend of large size and large capacity. In such a trend, various advanced yaw control strategies have been proposed to improve large WTs' comprehensive performance, but the analysis and summary of these strategies are still lacking. Therefore, it is necessary to have a review of yaw control, which not only enables readers to understand the current status of yaw control research but also promotes the development of wind energy technology. This paper presents a review of the current situation of yaw control for WTs, focusing on the mechanical/aerodynamic parts. The mechanical part is concerned with the WT yaw system and its effect on the fatigue load of the WT, and the aerodynamic part involves the wind energy capture and wake redirection to reduce the impact on adjacent WTs. In this review, the existing yaw control methods are classified in term of three control objectives: (1) increasing the wind energy capture of a single WT, (2) reducing the fatigue load of a single WT, and (3) maximizing the total power production of the whole wind farm and optimizing the wind farm fatigue load. On this basis, the control mechanism, the control algorithm, and the results are presented and analyzed in detail. Meanwhile, the advantages and disadvantages of the existing achievements are discussed. In addition, in a conclusion of the review, the future research direction has been identified.

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Section: Maximize Energy Capture Of a Single Wtmentioning

confidence: 99%

Section: Based On Wind Direction Estimationmentioning

confidence: 99%

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confidence: 99%

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Review of control strategy of large horizontal‐axis wind turbines yaw system

et al. 2020

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“…DWLs conventionally measure high-resolution wind to the top of the wind turbine or to hub height (the centre of the wind turbine's rotor, up to 250 m). Such measurements are used to understand turbulent wind fluctuations in the surface layer that are influenced by weather, terrain, turbine wake effects or shear across the rotor-swept area (Bakhshi and Sandborn, 2020;Banta et al, 2013;Iungo and Porté-Agel, 2013;Krishnamurthy et al, 2013;Mann et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Momentum fluxes from airborne wind measurements in three cumulus cases over land

Koning

Nuijens

Mallaun

2022

Preprint

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Abstract. This study combines airborne Doppler Wind Lidar (DWL) observations with high-frequency in situ wind measurements from a gust probe, a combination that to our knowledge has not been used before. The two measurement techniques show a similar mean in the wind components throughout the flights and are then used to study momentum transport in relation to shallow cumulus over land. We present three case studies ranging from forced cumulus humilis to thicker clouds associated with stronger popcorn-like convection after a cold front passage. The wind profiles obtained with the DWL are helpful in explaining the momentum fluxes that are calculated from the 100 Hz in situ data using the eddy covariance method. Most of the momentum flux profiles revealed down-gradient momentum transport that was generally strongest within the mixed-layer and decreasing towards cloud tops. Comparing clear-sky and cloud-topped transects, the cloudy skies revealed a substantial enhancement in the mixed-layer momentum flux (more than twice as much). On one track during the third flight, after a post-cold-front passage and displaying thicker clouds, shows a momentum flux profile that did not decrease linearly with height as expected from shear-driven small-scale turbulence. The momentum in the mixed layer was very small, but a very strong flux has been observed in the cloud layer. Moreover, the updraft contribution to the flux was much larger in this case than in all other tracks that have been flown during the campaign. Last, we look into how much flux the different scales contribute to the overall transport. There we find that the largest scales (up to 7 km) usually carry most flux. However, sometimes the larger scales have opposite contribution to the flux than the scales smaller than 7 km, which can then result in a smaller or almost no net flux.

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“…The systematic yaw error can most reliably be diagnosed if additional sensory systems for measuring upwind wind flow are employed: for example, the studies in [21][22][23][24] deal with this objective through the use of Lidar anemometers. The point with this approach is its application in wind energy practice: employing a Lidar is costly and the outcome about the presence of a relevant systematic yaw error is uncertain.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact

et al. 2020

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The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4 ∘ affects the performance up to the order of the 1.5% of the Annual Energy Production.

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Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Cited by 17 publications

References 32 publications

Review of control strategy of large horizontal‐axis wind turbines yaw system

Review of control strategy of large horizontal‐axis wind turbines yaw system

Momentum fluxes from airborne wind measurements in three cumulus cases over land

Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact

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