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Analysis of Current Status and Prospects of Parachute-Based Airborne Wind Energy Technology
LUO Bixiong, REN Zongdong, LIU Haiyang, LI Xiaoyu
Electric Power Construction ›› 2025, Vol. 46 ›› Issue (8) : 45-53.
PDF(3772 KB)
PDF(3772 KB)
Analysis of Current Status and Prospects of Parachute-Based Airborne Wind Energy Technology
[Objective] Airborne wind energy(AWE)technology utilizes faster and more stable wind speeds at higher altitudes and offers higher energy density and power generation efficiency than traditional wind power generation. This study explored the current status and prospects of AWE technology,with a particular focus on parachute-based ground-generated high-altitude wind power technology. [Methods] This article outlines the technological routes of AWE systems(AWESs)using two main approaches(ground-gen and air-gen)and discusses their respective technical challenges and the current status of development. Special attention is paid to the parachute-based ground-gen AWES,with a detailed introduction to its working principle,system composition,and engineering case analysis. Parachute-based technology effectively captures and converts wind energy through the coordinated operation of aerial,traction,and ground components. By analyzing the specific implementation of the Jixi high-altitude wind power project in China,this article demonstrates the practical application and effectiveness of parachute-based ground-gen AWE technology. [Results] The project successfully achieved high-altitude wind power generation,which could output kilowatt-level power at low altitudes and megawatt levels over 5 km,thus verifying the feasibility and advantages of the technology. [Conclusions] The Jixi Project proved the feasibility of this technology,which features scalability,high safety,and high resource utilization efficiency. It also achieves a high wind energy conversion efficiency and can capture wind resources at altitudes over 1 km by increasing the length of the tether and adjusting the launch angle. In the “Three North” regions with abundant wind resources,this technology can achieve MW-level power generation at an altitude of 1000 m and further upgrade the power generation capacity by increasing the number of doing-work parachutes,holding significant implications for renewable energy development.
renewable energy / airborne wind energy(AWE)technology / parachute-based AWES
| [1] |
孟凡斌, 南钰, 武亚非, 等. 基于谱归一化生成对抗网络与谱聚类的典型风力发电场景生成[J]. 浙江电力, 2024, 43(12): 86-94.
|
| [2] |
邵宜祥, 刘剑, 胡丽萍, 等. 一种改进组合神经网络的超短期风速预测方法研究[J]. 发电技术, 2024, 45(2): 323-330.
超短期风速预测是保障风电机组桨距角前馈控制实施效果的关键,对提高风电机组环境适应性具有重要影响。为了提高预测精度,提出了一种改进组合神经网络的超短期风速预测方法。该方法选择适合时间序列预测且具有较强非线性学习能力的BP神经网络和长短期记忆(long short-term memory,LSTM)神经网络进行加权组合,以消除单个神经网络可能存在的较大误差;同时,为了提高组合效果,采用差分进化算法对组合权重进行优化。将该方法应用于某风场超短期风速预测中,通过与单神经网络预测、等权重组合神经网络预测的结果对比,验证了所提方法在提高预测精度上的有效性。
Ultra-short-term wind speed prediction is the key to ensure the implementation effect of wind turbine pitch angle feedforward control, and has an important impact on improving the environmental adaptability of wind turbines. In order to improve the prediction accuracy, an ultra-short-term wind speed prediction method based on an improved combined neural networks was proposed. In this method, BP neural network and long short-term memory (LSTM) neural network, which are suitable for time series prediction and have strong nonlinear learning ability, are selected for weighted combination to eliminate the large error that may exist in a single neural network. At the same time, to improve the combination effect, the differential evolution (DE) algorithm was used to optimize the combination weight. The method was applied to the ultra-short-term wind speed prediction of a wind farm. Compared with the results of single neural network prediction and equal weight combined neural networks prediction, the effectiveness of the proposed method in improving the prediction accuracy was verified. |
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
袁天梦, 宁亮, 巩彦江, 等. 考虑能量耗散的分布式风力发电并网电网容量扩充研究[J]. 电网与清洁能源, 2024, 40(12): 135-140.
|
| [7] |
程启明, 孙英豪, 程尹曼, 等. 电网故障时基于MMC-PET接口风力发电系统的建模与控制[J]. 电力工程技术, 2024, 43(6): 64-77.
|
| [8] |
|
| [9] |
韩华春, 宁联辉, 李辰辰, 等. 海上风电M3C换流器虚拟同步发电机控制[J]. 电力工程技术, 2024, 43(6): 78-87, 132.
|
| [10] |
夏冰清, 傅栩杰, 杨文斌, 等. 直驱式永磁同步风力发电系统的组合控制策略[J]. 浙江电力, 2024, 43(11): 57-64.
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
We compare the available wind resources for conventional wind turbines and for airborne wind energy systems. Accessing higher altitudes and continuously adjusting the harvesting operation to the wind resource substantially increases the potential energy yield. The study is based on the ERA5 reanalysis data which covers a period of 7 years with hourly estimates at a surface resolution of 31 x 31 km and a vertical resolution of 137 barometric altitude levels. We present detailed wind statistics for a location in the English Channel and then expand the analysis to a surface grid of Western and Central Europe with a resolution of 110 x 110 km. Over the land mass and coastal areas of Europe we find that compared to a fixed harvesting height at the approximate hub height of wind turbines, the wind power density which is available for 95% of the time increases by a factor of two. (C) 2019 The Authors. Published by Elsevier Ltd.
|
| [28] |
|
| [29] |
邵垒, 毛虹霖, 邢胜, 等. 高空风力发电发展现状及关键技术研究综述[J]. 新能源进展, 2020, 8(6): 477-485.
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
ECORYS. Study on challenges in the commercialisation of airborne wind energy systems[M/OL]. Publications Office of the European Union, 2018. [2024-11-01]. https://op.europa.eu/en/publication-detail/-/publication/a874f843-c137-11e8-9893-01aa75ed71a1/language-en.
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
/
| 〈 |
|
〉 |