Author(s)

JiaXuan Li 

Affiliation(s)

Engineering Management Department, Huadian Liaoning Energy Development Co., Ltd., Shenyang 110102, Liaoning, China.

Corresponding Author

JiaXuan Li 

ABSTRACT

This paper addresses the issues of energy loss and equipment fatigue caused by wake effects in offshore wind farms by proposing a hybrid prediction model that integrates physical mechanisms with data-driven approaches, along with a layout optimization framework based on a multi-objective evolutionary algorithm. First, through improved RANS numerical simulations, scaled wind tunnel experiments, and high-resolution LES local simulations, the study systematically reveals the coupling effects of different planar layouts, wind speed conditions, and turbine height differences on wake velocity attenuation, turbulence regeneration, and vortex structure evolution. Second, a hybrid prediction model integrating analytical model priors with corrections from XGBoost and residual network compensation is constructed, enabling high-precision, low-latency online prediction of wake decay rates and turbulence intensity under unknown layouts and extreme wind conditions. Finally, by incorporating annualized power generation, fatigue load fluctuations of critical components, and operational costs into a unified multi-objective function, Pareto front search is employed to generate multiple sets of optimal layout schemes. The robustness of these schemes is validated through sensitivity and uncertainty analyses. Case studies demonstrate that optimized wind farms can increase annual power generation by 3%–7%, reduce load fluctuations by 10%–15%, and lower operational costs by 5%–9%, while simultaneously decreasing maintenance vessel dispatch frequency, reducing marine noise pollution, and significantly enhancing carbon reduction benefits. The research outcomes not only enrich wake dynamics and turbulence regeneration theories but also provide practical, visualized decision-support tools for the design, construction, and operation of offshore wind farms, holding significant importance for advancing the intelligent and low-cost development of the wind power industry.

KEYWORDS

Offshore wind farm; Wake effect; Hybrid prediction model; Multi-objective layout optimization; Turbulence regeneration; Pareto front; XGBoost; LES simulation

CITE THIS PAPER

JiaXuan Li. Analysis of wake effects and layout optimization methods for offshore wind farms. World Journal of Engineering Research. 2025, 3(3): 51-64. DOI: https://doi.org/10.61784/wjer3042.

REFERENCES

[1] Zhou Yizhou, Mohammad S, Wei Zhinong, et al. Distributionally Robust Unit Commitment in Coordinated Electricity and District Heating Networks. IEEE Transactions on Power Systems, 2019, 35(3): 2155–2166.

[2] Yang Lun, Xu Yinliang, Zhou Jianguo, et al. Distributionally Robust Frequency Constrained Scheduling for an Integrated Electricity-Gas System. IEEE Transactions on Smart Grid, 2022, 13(4): 2730–2743.

[3] Li Zhichuan, Hu Peng, Ma Jiaxing. Analysis and Outlook on the Current Development Status of Offshore Wind Power in China. China Offshore Oil and Gas, 2022, 34(5): 229–236.

[4] Yang Lun, Xu Yinliang, Zhou Jianguo, et al. Distributionally Robust Frequency Constrained Scheduling for an Integrated Electricity-Gas System. IEEE Transactions on Smart Grid, 2022, 13(4): 2730–2743.

[5] Mu Yanfei, Wang Qiang, Luo Kun. Assessment of Wake Impact on Offshore Wind Farms Based on Mesoscale WRF Model. Proceedings of the CSEE, 2022, 42(S1): 193–203.

[6] Song Ziqiu, Feng Hanyu, Yu Zhaoguo, et al. Research on Coordinated Control Method for Semi-submersible Floating Wind Turbines Based on Model Predictive Control. Proceedings of the CSEE, 2022, 42(12): 4330–4338.

[7] Zhu Ruonan. Optimization Control Strategy for Wind Farm Wake Considering Floater Foundation Displacement. Shenyang: Shenyang University of Technology, 2023.

[8] Li Yan, Wu Di, Hong Chang, et al. Layout Optimization Strategies for Large-Scale Offshore Wind Farm Turbines. Acta Energiae Solaris Sinica, 2020, 41(2): 67–73.

[9] Zhang Zhi, Zhou Ming, Zhu Lingzhi, et al. Robust Optimal Operation Method of Wind Farms’ Synergetic Participation in Generation-Reserve in Power Systems. Power System Technology, 2022, 46(4): 1316–1325.

[10] Li Xuelin, Yuan Ling. Development Status and Recommendations for Offshore Wind-to-Hydrogen Technology. Power Generation Technology, 2022, 43(2): 198–206.

[11] Franco BA, Baptista P, Neto RC, et al. Assessment of Offloading Pathways for Wind-Powered Offshore Hydrogen Production: Energy and Economic Analysis. Applied Energy, 2021, 286: 116553.

[12] Luo Kui, Guo Jianbo, Ma Shicong, et al. Review of Key Technologies of Reliability Analysis and Improvement for Offshore Wind Power Grid Integration. Power System Technology, 2022, 46(10): 3691–3702.

[13] Liu Yige, Zhao Zhenzhou, Ma Yuanzhuo, et al. Active Wake Control Strategy of Tandem Wind Turbines Based on Whale Optimization Algorithm. Proceedings of the CSEE, 2024, 44(9): 3702–3709.

[14] Shi M, Shahidehpour M, Zhou Q, et al. Optimal Consensus-Based Event-Triggered Control for Resilience Enhancement in DC Microgrid. IEEE Transactions on Power Systems, 2021, 36(3): 1807–1818.

[15] Bidadfar A, Saborío-Romano O, Sakamuri JN, et al. On Feasibility of Autonomous Frequency-Support Provision from Offshore HVDC Grids. IEEE Transactions on Power Delivery, 2020, 35(6): 2711–2721.

[16] Fang Fang, Zhang Xiaoning, Yao Guishan, et al. Assessment of Wake Disturbance Impact between Offshore and Onshore Wind Farms Based on Mesoscale Meteorological Model Analysis. Proceedings of the CSEE, 2022, 42(13): 4848–4859.

[17] Liu Bowen. Numerical Simulation of Wind Turbine Wake Based on Reynolds-Averaged Approach. Shenyang: Shenyang University of Technology, 2018.

[18] Huang Lingling, Cao Jialin, Zhang Kaihua, et al. Research and Prospects of Operation and Maintenance Status of Offshore Wind Turbines. Proceedings of the CSEE, 2016, 36(3): 729–738.

[19] Li Jiarong, Lin Jin, Xing Xuetao, et al. Selection and Optimal Planning of Power-to-Hydrogen (P2H) Module Combination in Active Distribution Network Based on Unified Operation Model. Proceedings of the CSEE, 2021, 41(12): 4021–4033.

[20] Chen K X, Lin J, Qiu Y W, et al. Model Predictive Control for Wind Farm Power Tracking with Deep Learning-Based Reduced Order Modeling. IEEE Transactions on Industrial Informatics, 2022, 18(11): 7484–7493.