Articles | Volume 9, issue 10
https://doi.org/10.5194/wes-9-2017-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-2017-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences
Vattenfall, Amerigo-Vespucci-Platz 2, 20457, Hamburg, Germany
Christian Bak
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Related authors
Tahir H. Malik and Christian Bak
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-49, https://doi.org/10.5194/wes-2024-49, 2024
Revised manuscript under review for WES
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This research integrates custom sensors into wind turbine simulation models for improved performance monitoring utilising a developed method. Real-world data validation demonstrates that enhanced sensor accuracy increases annual energy production and extends operational lifespan. This approach addresses the need for precise performance assessments in the evolving wind energy sector, ultimately promoting sustainability and efficiency.
Tahir H. Malik and Christian Bak
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-35, https://doi.org/10.5194/wes-2024-35, 2024
Revised manuscript accepted for WES
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This study investigates how wind turbine blades damaged by erosion, along with changing wind conditions, affect power output. Even minor blade damage can lead to significant energy losses, especially in turbulent winds. Using simulations, we discovered that standard power data analysis methods, including time-averaging, can hide these losses. This research highlights the need for better blade damage detection and careful wind data analysis to optimize wind farm performance.
Tahir H. Malik and Christian Bak
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-49, https://doi.org/10.5194/wes-2024-49, 2024
Revised manuscript under review for WES
Short summary
Short summary
This research integrates custom sensors into wind turbine simulation models for improved performance monitoring utilising a developed method. Real-world data validation demonstrates that enhanced sensor accuracy increases annual energy production and extends operational lifespan. This approach addresses the need for precise performance assessments in the evolving wind energy sector, ultimately promoting sustainability and efficiency.
Tahir H. Malik and Christian Bak
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-35, https://doi.org/10.5194/wes-2024-35, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This study investigates how wind turbine blades damaged by erosion, along with changing wind conditions, affect power output. Even minor blade damage can lead to significant energy losses, especially in turbulent winds. Using simulations, we discovered that standard power data analysis methods, including time-averaging, can hide these losses. This research highlights the need for better blade damage detection and careful wind data analysis to optimize wind farm performance.
Kenneth Loenbaek, Christian Bak, Jens I. Madsen, and Michael McWilliam
Wind Energ. Sci., 6, 903–915, https://doi.org/10.5194/wes-6-903-2021, https://doi.org/10.5194/wes-6-903-2021, 2021
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We present a model for assessing the aerodynamic performance of a wind turbine rotor through a different parametrization of the classical blade element momentum model. The model establishes an analytical relationship between the loading in the flow direction and the power along the rotor span. The main benefit of the model is the ease with which it can be applied for rotor optimization and especially load constraint power optimization.
Kenneth Loenbaek, Christian Bak, and Michael McWilliam
Wind Energ. Sci., 6, 917–933, https://doi.org/10.5194/wes-6-917-2021, https://doi.org/10.5194/wes-6-917-2021, 2021
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A novel wind turbine rotor optimization methodology is presented. Using an assumption of radial independence it is possible to obtain the Pareto-optimal relationship between power and loads through the use of KKT multipliers, leaving an optimization problem that can be solved at each radial station independently. Combining it with a simple cost function it is possible to analytically solve for the optimal power per cost with given inputs for the aerodynamics and the cost function.
Kenneth Loenbaek, Christian Bak, Jens I. Madsen, and Bjarke Dam
Wind Energ. Sci., 5, 155–170, https://doi.org/10.5194/wes-5-155-2020, https://doi.org/10.5194/wes-5-155-2020, 2020
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From the basic aerodynamic theory of wind turbine rotors, it is a well-known fact that there is a relationship between the loading of the rotor and power efficiency. It shows that there is a loading that maximizes the power efficiency, and it is common to target this maximum when designing rotors. But in this paper it is found that for rotors constrained by a load, the maximum power is found by decreasing the loading and increasing the rotor radius. Max power efficiency is therefore not optimal.
Jakob Ilsted Bech, Charlotte Bay Hasager, and Christian Bak
Wind Energ. Sci., 3, 729–748, https://doi.org/10.5194/wes-3-729-2018, https://doi.org/10.5194/wes-3-729-2018, 2018
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Rain erosion on wind turbine blades is a severe challenge for wind energy today. It causes significant losses in power production, and large sums are spent on inspection and repair.
Blade life can be extended, power production increased and maintenance costs reduced by rotor speed reduction at extreme precipitation events. Combining erosion test results, meteorological data and models of blade performance, we show that a turbine control strategy is a promising new weapon against blade erosion.
Related subject area
Thematic area: Materials and operation | Topic: Operation and maintenance, condition monitoring, reliability
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Unsupervised anomaly detection of permanent-magnet offshore wind generators through electrical and electromagnetic measurements
Operation and maintenance cost comparison between 15 MW direct-drive and medium-speed offshore wind turbines
Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent
On the Uncertainty of Digital Twin Models for Load Monitoring and Fatigue Assessment in Wind Turbine Drivetrains
Active trailing edge flap system fault detection via machine learning
Grand challenges in the digitalisation of wind energy
Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms
Assessing the rotor blade deformation and tower–blade tip clearance of a 3.4 MW wind turbine with terrestrial laser scanning
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Population Based Structural Health Monitoring: Homogeneous Offshore Wind Model Development
Wind turbine main-bearing lubrication – Part 2: Simulation-based results for a double-row spherical roller main bearing in a 1.5 MW wind turbine
Reduction of wind-turbine-generated seismic noise with structural measures
Very low frequency IEPE accelerometer calibration and application to a wind energy structure
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024, https://doi.org/10.5194/wes-9-2175-2024, 2024
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This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Ali Dibaj, Mostafa Valavi, and Amir R. Nejad
Wind Energ. Sci., 9, 2063–2086, https://doi.org/10.5194/wes-9-2063-2024, https://doi.org/10.5194/wes-9-2063-2024, 2024
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This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.
Orla Donnelly, Fraser Anderson, and James Carroll
Wind Energ. Sci., 9, 1345–1362, https://doi.org/10.5194/wes-9-1345-2024, https://doi.org/10.5194/wes-9-1345-2024, 2024
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We collate the latest reliability data in operations and maintenance (O&M) for offshore wind turbines, specifically large turbines of 15 MW. We use these data to model O&M of an offshore wind farm at three different sites. We compare two industry-dominant drivetrain configurations in terms of O&M cost for 15 MW turbines and determine if previous results for smaller turbines still hold true. Comparisons between drivetrains are topical within industry, and we produce cost comparisons for them.
Shadan Mozafari, Paul Veers, Jennifer Rinker, and Katherine Dykes
Wind Energ. Sci., 9, 799–820, https://doi.org/10.5194/wes-9-799-2024, https://doi.org/10.5194/wes-9-799-2024, 2024
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Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
Felix Christian Mehlan and Amir R. Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-28, https://doi.org/10.5194/wes-2024-28, 2024
Revised manuscript accepted for WES
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A Digital Twin is a virtual representation that mirrors the wind turbine's real behavior through simulation models and sensor measurements and can assist in making key decisions such as planning the replacement of parts. These models and measurements are, of course, not perfect and only give an incomplete picture of the real behavior. This study investigates how large the uncertainty of such models and measurements is and to what extent it affects the decision-making process.
Andrea Gamberini and Imad Abdallah
Wind Energ. Sci., 9, 181–201, https://doi.org/10.5194/wes-9-181-2024, https://doi.org/10.5194/wes-9-181-2024, 2024
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Active trailing edge flaps can potentially reduce wind turbine (WT) loads. To monitor their performance, we present two methods based on machine learning that identify flap health states, including degraded performance, in normal power production and idling condition. Both methods rely only on sensors commonly available on WTs. One approach properly detects all the flap states if a fault occurs on only one blade. The other approach can identify two specific flap states in all fault scenarios.
Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding
Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
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Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 8, 893–924, https://doi.org/10.5194/wes-8-893-2023, https://doi.org/10.5194/wes-8-893-2023, 2023
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This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction for wind turbines using SCADA data. The focus lies on techniques that respond to requirements of the industry, e.g., robustness, transparency, computational efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e., generator bearing failures, generator fan failures, and generator stator failures.
Paula Helming, Alex Intemann, Klaus-Peter Webersinke, Axel von Freyberg, Michael Sorg, and Andreas Fischer
Wind Energ. Sci., 8, 421–431, https://doi.org/10.5194/wes-8-421-2023, https://doi.org/10.5194/wes-8-421-2023, 2023
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Using renewable energy such as wind energy is vital. To optimize the energy yield from wind turbines, they have increased in size, leading to large blade deformations. This paper measures these deformations for different wind loads and the distance between the blade and the tower from 170 m away from the wind turbine. The paper proves that the blade deformation increases in the wind direction with increasing wind speed, while the distance between the blade and the tower decreases.
Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
Wind Energ. Sci., 8, 173–191, https://doi.org/10.5194/wes-8-173-2023, https://doi.org/10.5194/wes-8-173-2023, 2023
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This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
Innes Murdo Black, Moritz Werther Häckell, and Athanasios Kolios
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-93, https://doi.org/10.5194/wes-2022-93, 2022
Revised manuscript accepted for WES
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Population based structural health monitoring is a low-cost monitoring campaign. The cost reduction from this type of digital enabled asset management tool is manifested by sharing information, in this case a wind farm foundation, within the population. By sharing the information in the wind farm this reduces the amount of sensors and physical model updating, reducing the cost of the monitoring campaign.
Edward Hart, Elisha de Mello, and Rob Dwyer-Joyce
Wind Energ. Sci., 7, 1533–1550, https://doi.org/10.5194/wes-7-1533-2022, https://doi.org/10.5194/wes-7-1533-2022, 2022
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This paper is the second in a two-part study on lubrication in wind turbine main bearings. Investigations are conducted concerning lubrication in the double-row spherical roller main bearing of a 1.5 MW wind turbine. This includes effects relating to temperature, starvation, grease-thickener interactions and possible non-steady EHL effects. Results predict that the modelled main bearing would be expected to operate under mixed lubrication conditions for a non-negligible proportion of its life.
Rafael Abreu, Daniel Peter, and Christine Thomas
Wind Energ. Sci., 7, 1227–1239, https://doi.org/10.5194/wes-7-1227-2022, https://doi.org/10.5194/wes-7-1227-2022, 2022
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In order to find consensus between wind energy producers and seismologists, we study the possibility of reducing wind turbine noise recorded at seismological stations. We find that drilling half-circular holes in front of the wind turbines helps to reduce the seismic noise. We also study the influence of topographic effects on seismic noise reduction.
Clemens Jonscher, Benedikt Hofmeister, Tanja Grießmann, and Raimund Rolfes
Wind Energ. Sci., 7, 1053–1067, https://doi.org/10.5194/wes-7-1053-2022, https://doi.org/10.5194/wes-7-1053-2022, 2022
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This work presents a method to use low-noise IEPE sensors in the low-frequency range down to 0.05 Hz. In order to achieve phase and amplitude accuracy with this type of sensor in the low-frequency range, a new calibration procedure for this frequency range was developed. The calibration enables the use of the low-noise IEPE sensors for large structures, such as wind turbines. The calibrated sensors can be used for wind turbine monitoring, such as fatigue monitoring.
Cited articles
Aho, J., Buckspan, A., Laks, J., Fleming, P., Jeong, Y., Dunne, F., Churchfield, M., Pao, L., and Johnson, K.: A tutorial of wind turbine control for supporting grid frequency through active power control, in: 2012 American Control Conference (ACC), 3120–3131, IEEE, https://doi.org/10.1109/ACC.2012.6315180, 2012. a, b
Albers, A.: Relative and integral wind turbine power performance evaluation, in: Proceedings of the 2012 European Wind Energy Conference & Exhibition, 22–25, https://www.researchgate.net/publication/242780574_Relative_and_Integral_Wind_Turbine_Power_Performance_Evaluation (last access: 1 August 2023), 2012. a
Anderson, T. K., Nelson, M. I., Kitikoon, P., Swenson, S. L., Korslund, J. A., and Vincent, A. L.: Population dynamics of cocirculating swine influenza A viruses in the United States from 2009 to 2012, Influenza Other Resp., 7, 42–51, 2013. a
Astolfi, D., Byrne, R., and Castellani, F.: Analysis of Wind Turbine Aging through Operation Curves, Energies, 13, 5623, https://doi.org/10.3390/en13215623, 2020. a
Bak, C.: A simple model to predict the energy loss due to leading edge roughness, J. Phys. Conf. Ser., 2265, 032038, https://doi.org/10.1088/1742-6596/2265/3/032038, 2022. a
Bak, C., Forsting, A. M., and Sorensen, N. N.: The influence of leading edge roughness, rotor control and wind climate on the loss in energy production, J. Phys. Conf. Ser., 1618, 052050, https://doi.org/10.1088/1742-6596/1618/5/052050, 2020. a
Bechtold, B., Fletcher, P., seamusholden, and Gorur-Shandilya, S.: bastibe/Violinplot-Matlab: A Good Starting Point, https://api.semanticscholar.org/CorpusID:244971580 (last access: 8 August 2023), 2021. a
Bolik, S. M.: Modelling and analysis of variable speed wind turbines with induction generator during grid fault, Institut for Energiteknik, Aalborg Universitet, https://vbn.aau.dk/en/publications/modelling-and-analysis-of-variable-speed-wind-turbines-with-induc (last access: 1 August 2023), 2004. a
Butler, S., Ringwood, J., and O'Connor, F.: Exploiting SCADA system data for wind turbine performance monitoring, in: 2013 Conference on Control and Fault-Tolerant Systems (SysTol), Nice, France, 9–11 October 2013, 389–394, https://doi.org/10.1109/SysTol.2013.6693951, 2013. a, b
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates, 2 edn., https://doi.org/10.4324/9780203771587, 1988. a
Dao, C., Kazemtabrizi, B., and Crabtree, C.: Wind turbine reliability data review and impacts on levelised cost of energy, Wind Energy, 22, 1848–1871, 2019. a
Dao, P. B.: On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines, Appl. Energ., 318, 119209, https://doi.org/10.1016/j.apenergy.2022.119209, 2022. a
Ding, Y., Barber, S., and Hammer, F.: Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements, Frontiers in Energy Research, 10, 1050342, https://doi.org/10.3389/fenrg.2022.1050342, 2022. a, b
Ernst, B., Schmitt, H., and Seume, J. R.: Effect of geometric uncertainties on the aerodynamic characteristic of offshore wind turbine blades, J. Phys. Conf. Ser., 555, 012033, https://doi.org/10.1088/1742-6596/555/1/012033, 2014. a
Farkas, Z.: Considering air density in wind power production, arXiv [preprint], https://doi.org/10.48550/arXiv.1103.2198, 2011. a, b
Gaudern, N.: A practical study of the aerodynamic impact of wind turbine blade leading edge erosion, J. Phys. Conf. Ser., 524, 012031, https://doi.org/10.1088/1742-6596/524/1/012031, 2014. a
Gonzalez, E., Stephen, B., Infield, D., and Melero, J. J.: Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study, Renew. Energ., 131, 841–853, 2019. a
Hafen, R. P., Anderson, D. E., Cleveland, W. S., Maciejewski, R., Ebert, D. S., Abusalah, A., Yakout, M., Ouzzani, M., and Grannis, S. J.: Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts, BMC Med. Inform. Decis., 9, 1–11, 2009. a
Han, W., Kim, J., and Kim, B.: Effects of contamination and erosion at the leading edge of blade tip airfoils on the annual energy production of wind turbines, Renew. Energ., 115, 817–823, 2018. a
IEC: IEC 61400-12-1: 2017, Wind Energy Generation Systems – Part, 12, https://webstore.iec.ch/en/publication/26603 (last access: 1 August 2023), 2017. a
Katnam, K., Comer, A., Roy, D., Da Silva, L., and Young, T.: Composite repair in wind turbine blades: an overview, J. Adhesion, 91, 113–139, 2015. a
Kim, D.-Y., Kim, Y.-H., and Kim, B.-S.: Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear, Energy, 214, 119051, https://doi.org/10.1016/j.energy.2020.119051, 2021. a
Krog Kruse, E., Bak, C., and Olsen, A. S.: Wind tunnel experiments on a NACA 633-418 airfoil with different types of leading edge roughness, Wind Energy, 24, 1263–1274, 2021. a
Leahy, K., Gallagher, C., O’Donovan, P., and O'Sullivan, D. T.: Issues with data quality for wind turbine condition monitoring and reliability analyses, Energies, 12, 201, https://doi.org/10.3390/en12020201, 2019. a, b
Loeven, A. and Bijl, H.: Airfoil analysis with uncertain geometry using the probabilistic collocation method, in: 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 16th AIAA/ASME/AHS Adaptive Structures Conference, 10th AIAA Non-Deterministic Approaches Conference, 9th AIAA Gossamer Spacecraft Forum, 4th AIAA Multidisciplinary Design Optimization Specialists Conference, 2070, https://doi.org/10.2514/6.2008-2070, 2008. a, b
Maniaci, D. C., White, E. B., Wilcox, B., Langel, C. M., van Dam, C., and Paquette, J. A.: Experimental measurement and CFD model development of thick wind turbine airfoils with leading edge erosion, J. Phys. Conf. Ser., 753, 022013, https://doi.org/10.1088/1742-6596/753/2/022013, 2016. a
Mishnaevsky Jr., L., Hasager, C. B., Bak, C., Tilg, A.-M., Bech, J. I., Rad, S. D., and Fæster, S.: Leading edge erosion of wind turbine blades: Understanding, prevention and protection, Renew. Energ., 169, 953–969, 2021. a
Murphy, P., Lundquist, J. K., and Fleming, P.: How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine, Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, 2020. a
OpenAI: ChatGPT: Optimizing Language Models for Dialogue, https://openai.com/chatgpt/ (last access: 1 May 2024), 2023. a
Pindado, S., Barrero-Gil, A., and Sanz, A.: Cup anemometers’ loss of performance due to ageing processes, and its effect on annual energy production (AEP) estimates, Energies, 5, 1664–1685, 2012. a
Prema, V. and Rao, K. U.: Time series decomposition model for accurate wind speed forecast, Renewables: Wind, Water, and Solar, 2, 1–11, 2015. a
Sanchez-Vazquez, M. J., Nielen, M., Gunn, G. J., and Lewis, F. I.: Using seasonal-trend decomposition based on loess (STL) to explore temporal patterns of pneumonic lesions in finishing pigs slaughtered in England, 2005–2011, Prev. Vet. Med., 104, 65–73, 2012. a
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality (complete samples), Biometrika, 52, 591–611, 1965. a
St. Martin, C. M., Lundquist, J. K., Clifton, A., Poulos, G. S., and Schreck, S. J.: Wind turbine power production and annual energy production depend on atmospheric stability and turbulence, Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, 2016. a
Student: The probable error of a mean, Biometrika, 6, 1–25, 1908. a
Tautz-Weinert, J. and Watson, S. J.: Using SCADA data for wind turbine condition monitoring – a review, IET Renew. Power Gen., 11, 382–394, 2017. a
The MathWorks, Inc.: MATLAB: trenddecomp function, mATLAB version 2023b https://uk.mathworks.com/help/matlab/ref/double.trenddecomp.html (last access: September 2023), 2023. a
van Dijk, M. T., van Wingerden, J.-W., Ashuri, T., Li, Y., and Rotea, M. A.: Yaw-misalignment and its impact on wind turbine loads and wind farm power output, J. Phys. Conf. Ser., 753, 062013, https://doi.org/10.1088/1742-6596/753/6/062013, 2016. a
Verbesselt, J., Hyndman, R., Newnham, G., and Culvenor, D.: Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ., 114, 106–115, 2010. a
Wan, S., Cheng, L., and Sheng, X.: Effects of yaw error on wind turbine running characteristics based on the equivalent wind speed model, Energies, 8, 6286–6301, 2015. a
Wharton, S. and Lundquist, J. K.: Atmospheric stability affects wind turbine power collection, Environ. Res. Lett., 7, 014005, https://doi.org/10.1088/1748-9326/7/1/014005, 2012. a
Wilcoxon, F.: Individual comparisons by ranking methods, Biometrics Bull., 1, 80–83, 1945. a
Xu, L., Ou, Y., Cai, J., Wang, J., Fu, Y., and Bian, X.: Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition, Renew. Energ., 216, 119097, https://doi.org/10.1016/j.renene.2023.119097, 2023. a
Short summary
We explore the effect of blade modifications on offshore wind turbines' performance through a detailed analysis of 12 turbines over 12 years. Introducing the turbine performance integral method, which utilises time-series decomposition that combines various data sources, we uncover how blade wear, repairs and software updates impact efficiency. The findings offer valuable insights into improving wind turbine operations, contributing to the enhancement of renewable energy technologies.
We explore the effect of blade modifications on offshore wind turbines' performance through a...
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