Article
Engineering, Electrical & Electronic
Davide Astolfi, Francesco Castellani, Andrea Lombardi, Ludovico Terzi
Summary: This study formulates a method for estimating wind turbine performance decline with age based on long term SCADA data analysis, finding that in the considered test cases, the average rate of performance decline with age is approximately -0.2% per year, compatible with recent analyses based on cumulative data. It is also concluded that gearbox aging does not contribute to the performance decline, while generator aging does.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Ravi Pandit, David Infield, Matilde Santos
Summary: Continuous assessment of wind turbine performance is crucial for maximizing power generation at a low cost. This study aims to quantify and analyze the impact of wind shear and turbulence intensity on wind turbine power curves. The results show that taking these factors into consideration can improve the accuracy and reduce the uncertainty of power curve models.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Chemistry, Multidisciplinary
Bo Jing, Zheng Qian, Hamidreza Zareipour, Yan Pei, Anqi Wang
Summary: The research introduces a novel WTPC modeling method with logistic functions based on quantile regression, which can better describe the uncertainty of wind power and exhibit superior fitting performance compared to typical models. The method combines asymmetric absolute value functions with logistic functions, and includes an adaptive outlier filtering method.
APPLIED SCIENCES-BASEL
(2021)
Article
Green & Sustainable Science & Technology
Sonam Gupta, Anup Shukla
Summary: This paper presents a dynamic modeling method for Doubly Fed Induction Generator (DFIG), considering the electromotive force model of the stator-rotor circuit. The Converter control of DFIG is designed to reduce losses and achieve maximum power control. By controlling the active and reactive components of rotor currents, the copper losses of the rotor winding are effectively reduced, resulting in an efficient system.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Review
Engineering, Marine
Shafiqur Rehman, Luai M. Alhems, Md. Mahbub Alam, Longjun Wang, Zakria Toor
Summary: This article reviews the feasibility of wind and ocean power technologies and shares the acceptance and challenges in their development. It also discusses the energy harvesting techniques for onshore and offshore wind energy, as well as various forms of ocean energy. The efficiency, availability, and costs of these technologies are compared and evaluated.
Article
Energy & Fuels
Siyi Li, Mingrui Zhang, Matthew D. Piggott
Summary: This study proposes a surrogate model for wind turbine wake modelling based on a state-of-the-art graph neural network. The model operates directly on unstructured meshes and has been validated against high-fidelity data, showing its ability to accurately predict 3D flow fields. The proposed graph neural network is flexible and general, making it applicable to various computational fluid dynamics simulations.
Article
Engineering, Electrical & Electronic
Pedro Catalan, Yanbo Wang, Joseba Arza, Zhe Chen
Summary: This article provides a comprehensive overview of high-power wind energy conversion systems (WECS) from key technique aspects, including topologies, stability, reliability, and ancillary service capability. The article also discusses the challenges and potential solutions in developing high-power wind turbines.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Geosciences, Multidisciplinary
Sheng Lin, Yuntao Wang, Wen-Zhou Zhang, Qin-Biao Ni, Fei Chai
Summary: Tropical cyclones have a significant impact on wind power input to near-inertial oscillations (NIOs), but this impact is often overlooked in global estimations. This study quantifies the wind power on NIOs induced by tropical cyclones and highlights the significance of considering their contribution in estimating global wind power on NIOs.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Computer Science, Information Systems
Carlos Ruiz, Gonzalo Abad, Markel Zubiaga, Danel Madariaga, Joseba Arza
Summary: This paper verifies the power quality and grid code compliance of offshore wind power plants in the design phase through harmonic analysis methods. It is found that for wind turbines operating with a commonly used modulation strategy, the harmonic distortion of current signals is relatively high and does not meet the requirements of the German grid code. Solutions oriented towards wind turbine manufacturers are proposed to improve the harmonic emission of offshore wind farms.
Article
Green & Sustainable Science & Technology
H. Alphan
Summary: With the increasing demand for renewable energy sources globally, there has been a dramatic increase in wind power supply, but concerns about the visibility impacts of wind turbines are also growing. Spatial modelling of potential wind turbine visibility is crucial for wind power siting decisions, especially when balancing energy production and protecting visual landscape amenities.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Thermodynamics
Tenghui Li, Xiaolei Liu, Zi Lin, Rory Morrison
Summary: This study proposes a novel WTPC model integrating iForest, NSFM RBFNN, and a metaheuristic algorithm. Through evaluation on four real-world datasets, NSFM RBFNN was found to outperform competitive neural network-based methods in accuracy and robustness, making it a promising tool for practical applications.
Article
Engineering, Electrical & Electronic
Jonata C. de Albuquerque, Ronaldo R. B. de Aquino, Otoni Nobrega Neto, Milde M. S. Lira, Aida A. Ferreira, Manoel Afonso de Carvalho
Summary: A new method using artificial intelligence tools to develop non-parametric power curve models is proposed, with results showing that the new pre-trained FIS models have better precision in power curve approximation compared to ANN and FIS models.
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
(2021)
Article
Computer Science, Information Systems
Davide Astolfi
Summary: Wind turbines exhibit complex behavior due to varying environmental conditions, and this study focuses on analyzing meaningful operation curves to predict power output accurately. By utilizing multivariate Support Vector Regression with additional input variables, the models show improved performance in predicting power output with reduced error metrics. The approach presented in this study offers a superior capability to interpret wind turbine performance and reduces dependence on nacelle anemometer data, leading to more accurate power predictions.
Article
Computer Science, Artificial Intelligence
Celal Cakiroglu, Sercan Demir, Mehmet Hakan Ozdemir, Batin Latif Aylak, Gencay Sariisik, Laith Abualigah
Summary: This study estimates the power produced in a wind turbine using six different regression algorithms based on machine learning. The XGBoost algorithm performs the best according to the R2 performance metric, while the LightGBM model is the most efficient in terms of computational speed. Wind speed is shown to have the most significant impact on the model predictions according to the SHAP algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Energy & Fuels
Mahdi Fasihi, Robert Weiss, Jouni Savolainen, Christian Breyer
Summary: This study investigates the global potential of green ammonia production using a combination of hybrid PV-wind power plants and conversion technologies. The results suggest that solar PV technology will dominate electricity generation by 2030, and green ammonia could potentially substitute fossil-based ammonia globally by 2040.
Article
Meteorology & Atmospheric Sciences
Florian Le Guillou, Noe Lahaye, Clement Ubelmann, Sammy Metref, Emmanuel Cosme, Aurelien Ponte, Julien Le Sommer, Eric Blayo, Arthur Vidard
Summary: The paper introduces an alternating minimization algorithm to separate and map the balanced motions and internal tides signals of ocean water bodies, showing promising results in Observation System Simulation Experiments. The algorithm successfully reconstructs a significant portion of the variance of BMs and ITs, demonstrating potential for disentangling these signals from wide-swath altimetry data.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2021)
Article
Meteorology & Atmospheric Sciences
Charles X. Light, Brian K. Arbic, Paige E. Martin, Laurent Brodeau, J. Thomas Farrar, Stephen M. Griffies, Ben P. Kirtman, Lucas C. Laurindo, Dimitris Menemenlis, Andrea Molod, Arin D. Nelson, Ebenezer Nyadjro, Amanda K. O'Rourke, Jay F. Shriver, Leo Siqueira, R. Justin Small, Ehud Strobach
Summary: This paper investigates high-frequency variability of precipitation using various models and methods, and finds that high-resolution models yield results closer to observations. Increasing model grid spacing generally increases high-frequency precipitation variance in climate modeling.
Article
Environmental Sciences
William Llovel, Nicolas Kolodziejczyk, Sally Close, Thierry Penduff, Jean-Marc Molines, Laurent Terray
Summary: The global ocean is warming and has absorbed 90% of the Earth Energy Imbalance, resulting in global mean sea level rise. Both ocean heat content and sea level trends show large regional deviations, with uncertainties caused by uneven in-situ observations. Recent research has highlighted the contribution of chaotic ocean variability to regional sea level and ocean heat content trends, suggesting the need to account for this intrinsic variability when assessing decadal-scale budgets.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Meteorology & Atmospheric Sciences
Takaya Uchida, Quentin Jamet, William K. Dewar, Julien Le Sommer, Thierry Penduff, Dhruv Balwada
Summary: The thickness-weighted average (TWA) framework provides a theoretical formulation of the eddy feedback onto the residual-mean flow by treating the residual-mean flow as the prognostic variable. The ensemble dimension in an ensemble of North Atlantic simulations allows for a more accurate understanding of means and eddies. The Eliassen-Palm flux tensor captures the eddy-mean flow feedback, with eddy momentum fluxes dominating in the separated Gulf Stream.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Oceanography
Andrew McC. Hogg, Thierry Penduff, Sally E. Close, William K. Dewar, Navid C. Constantinou, Josue Martinez-Moreno
Summary: Circulation and eddies in the Southern Ocean are unique and significantly influenced by wind stress and buoyancy fluxes. The strength of the eddy field is controlled by external forcing and intrinsic variability.
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
(2022)
Article
Multidisciplinary Sciences
G. Boutin, T. Williams, C. Horvat, L. Brodeau
Summary: This study evaluates the extent of the marginal ice zone (MIZ) using a model and compares it with satellite data. By defining metrics for MIZ and considering the sparse coverage of observations, the model produces MIZ extents comparable to observations in winter but underestimates them in autumn.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2022)
Article
Meteorology & Atmospheric Sciences
Quentin Jamet, Stephanie Leroux, William K. Dewar, Thierry Penduff, Julien Le Sommer, Jean-Marc Molines, Jonathan Gula
Summary: This study focuses on non-local energy transfers between eddies and mean flow, utilizing ensemble statistics to define the mean and turbulent flow. The analysis highlights the significant role of cross energy term in explaining non-local dynamics, providing constraints on horizontal organization of eddy-mean flow KE transfers.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat
Summary: The use of machine learning in building subgrid parametrizations for climate models is gaining attention. This paper compares different learning strategies to obtain parameterizations and shows that end-to-end learning strategies based on a posteriori criteria outperform other empirical and data-driven schemes in terms of performance, stability, and adaptability. The results support the relevance of differentiable programming paradigms for future climate models.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Mengnan Zhao, Rui M. M. Ponte, Thierry Penduff
Summary: Recent research has shown that intrinsic processes such as mesoscale turbulence play a crucial role in causing variations in ocean bottom pressure (p(b)), similar to atmospheric variability. These processes can generate random variability on scales larger than the mesoscale. Model analyses have revealed a global-scale intrinsic p(b) variability mode at monthly time scales, which operates through a different mechanism. This mode exhibits larger amplitudes around Drake Passage and opposite polarity between the Southern Ocean and Atlantic/Arctic oceans, consistent with observed p(b) variability.
Article
Geosciences, Multidisciplinary
Anne Marie Treguier, Clement de Boyer Montegut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, Steve Yeager
Summary: This study evaluates the mixed-layer depth (MLD) in different ocean models and highlights the importance of accurately representing the MLD in climate studies. The results show that higher resolution models improve the representation of the MLD, especially in certain formation regions. However, biases still exist, particularly in the Southern Ocean. The study also emphasizes the need for careful selection of reference levels and spatio-temporal sampling in MLD computation for future model intercomparison projects.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Stephanie Leroux, Jean-Michel Brankart, Aurelie Albert, Laurent Brodeau, Jean-Marc Molines, Quentin Jamet, Julien Le Sommer, Thierry Penduff, Pierre Brasseur
Summary: The predictability properties of ocean dynamics were studied using a regional ocean model, and a series of ensemble prediction experiments were conducted. The ensemble variance was shown to upscale from small scales to larger structures, and the ensemble simulations provided a statistical description of the relationship between initial accuracy and forecast accuracy.
Article
Geosciences, Multidisciplinary
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurelie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clement Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, Rene Schubert, Baylor Fox-Kemper, William K. Dewar, Alan Wallcraft
Summary: With the increase in computational power, higher-resolution ocean models have been developed, but the larger data size poses challenges for data transfer and analysis. A cloud-based analysis framework is proposed to address these challenges, allowing for more efficient and collaborative analysis of model outputs.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
