An evaluation method for wake effect of wind farm group based on CFD-WRF coupled wind resource map DOI
Junpeng Ma, Feiyan Liu,

Chenggang Xiao

et al.

Journal of Intelligent & Fuzzy Systems, Journal Year: 2023, Volume and Issue: 45(6), P. 11425 - 11437

Published: Oct. 3, 2023

The wake effect of wind farm can reduce the incoming speed at turbine located in downstream direction, resulting decrease global output. WRF model adopts a three-layer two-way nested grid division scheme to simulate upper atmospheric circulation, obtain speed, direction and other data that truly reproduce fluid characteristics regional group. boundary conditions solution CFD are set, computational dynamics region is obtained. coupled with CFD, Fitch introduced into it. By introducing drag coefficient calculation turbulent kinetic energy CFD-WRF coupling model, field simulated online. Monte Carlo sampling method used random resource then sampled calculate group output farms, evaluate impact on treatment. experimental results show this effectively analyze characteristic field, time RANS about 3 s. Due effect, overall efficiency will be significantly reduced.

Language: Английский

Wind turbine fault detection based on the transformer model using SCADA data DOI Creative Commons
Jorge Maldonado-Correa, Joel Torres-Cabrera, Sergio Martín‐Martínez

et al.

Engineering Failure Analysis, Journal Year: 2024, Volume and Issue: 162, P. 108354 - 108354

Published: April 27, 2024

The growth of installed wind power worldwide and its significant contribution to the energy market is mainly due evolution turbines (WTs) their ability withstand a wide range dynamic loads. WT failures can be costly lead extended downtime. Early detection such critical in reducing costs associated with operation maintenance (O&M) tasks unscheduled shutdowns WTs. This paper applies two Deep Learning (DL) models based on Transformer model predict IGBT module WTs at an onshore farm Ecuador. To this end, SCADA (Supervisory Control Data Acquisition) operational alarm data are used, together record (MR). These analyzed processed, applying different feature selection methods. results show that proposed perform well, high accuracy approximate prediction 4.25 months before failure occurrence. promising possibility using for early accurate identification faults components

Language: Английский

Citations

14
InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation DOI
Mingwei Zhong,

J.M. Fan,

Jianqiang Luo

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 371, P. 123745 - 123745

Published: June 20, 2024

Language: Английский

Citations

6
Integration of atmospheric stability in wind resource assessment through multi-scale coupling method DOI

Jingxin Jin,

Yilin Li, Lin Ye

et al.

Applied Energy, Journal Year: 2023, Volume and Issue: 348, P. 121402 - 121402

Published: July 3, 2023

Language: Английский

Citations

9
A Hybrid Approach to Wind Power Intensity Classification Using Decision Trees and Large Language Models DOI Creative Commons
Tahir Çetin Akıncı, H. Selçuk Noğay, Miroslav Penchev

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123388 - 123388

Published: May 1, 2025

Language: Английский

Citations

0
Identifying and understanding how critical landscapes for carbon sequestration respond to development for low carbon energy production: Insight to inform optimal land planning and management strategies DOI
Susan Waldron, Kate V. Heal, Amira Elayouty

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 385, P. 125063 - 125063

Published: May 10, 2025

Language: Английский

Citations

0
Evaluation of the topology anisotropy effect on wake development over complex terrain based on a novel method and verified by LiDAR measurements DOI

Xu Zongyuan,

Xiaoxia Gao,

Lu Hongkun

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 322, P. 119154 - 119154

Published: Oct. 25, 2024

Language: Английский

Citations

1
A Bayesian Deep Learning-Based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle DOI
Xiaoming Liu, Jun Liu, Yu Zhao

et al.

IEEE Transactions on Sustainable Energy, Journal Year: 2024, Volume and Issue: 15(4), P. 2663 - 2674

Published: July 30, 2024

Language: Английский

Citations

0
Neurocontrolled Prediction of Blade Position in Wind Generators DOI
Elvis Condor Umaginga,

Emerson Ordoñez Paccha,

William Montalvo

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 466 - 481

Published: Jan. 1, 2024

Language: Английский

Citations

0
An evaluation method for wake effect of wind farm group based on CFD-WRF coupled wind resource map DOI
Junpeng Ma, Feiyan Liu,

Chenggang Xiao

et al.

Journal of Intelligent & Fuzzy Systems, Journal Year: 2023, Volume and Issue: 45(6), P. 11425 - 11437

Published: Oct. 3, 2023

The wake effect of wind farm can reduce the incoming speed at turbine located in downstream direction, resulting decrease global output. WRF model adopts a three-layer two-way nested grid division scheme to simulate upper atmospheric circulation, obtain speed, direction and other data that truly reproduce fluid characteristics regional group. boundary conditions solution CFD are set, computational dynamics region is obtained. coupled with CFD, Fitch introduced into it. By introducing drag coefficient calculation turbulent kinetic energy CFD-WRF coupling model, field simulated online. Monte Carlo sampling method used random resource then sampled calculate group output farms, evaluate impact on treatment. experimental results show this effectively analyze characteristic field, time RANS about 3 s. Due effect, overall efficiency will be significantly reduced.

Language: Английский

Citations

0