电源学报

适用于深远海漂浮式海上风机变换器的智能功率控制

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夏懿 , 宋汶秦 , 杨茜

电源学报 | 新能源发电与储能 2026,24(1): 157-167

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电源学报 |新能源发电与储能 2026 , 24 (1) : 157 -167

适用于深远海漂浮式海上风机变换器的智能功率控制

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夏懿, 宋汶秦, 杨茜

作者信息

国网甘肃省电力公司，兰州　730050

通讯作者:

杨茜（1994—　），女，通信作者，硕士，工程师。研究方向：水电工程。E-mail：yangq94@163.com。

作者简介:

夏懿（1974—　），男，硕士，高级工程师。研究方向：电力系统及其自动化。E-mail：xyz20202021@126.com。

宋汶秦（1983—　），男，硕士，高级工程师。研究方向：水电工程。E-mail：613314714@qq.com。

Intelligent Power Control for Converter in Deep-sea Floating Offshore Wind Turbine

Yi XIA, Wenqin SONG, Qian YANG

Affiliations

State Grid Gansu Electric Power Company, Lanzhou 730050, China

出版时间: 2026-01-30 doi: 10.13234/j.issn.2095-2805.2026.1.157

文章导航

摘要

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随着我国海上风电开发向深远海推进，受限于深远海区域缺少浮标、海岸附近风速低且不稳定，以及漂浮装置在极端环境条件（海浪和洋流等）中的复杂扰动等多重因素的影响，当前面临风速测量数据精度低引起漂浮式海上风机控制效果较差等问题。基于此，提出了1种基于神经网络有效风速估计器的深远海漂浮式海上风力发电机变流器智能功率控制方法。该控制系统由比例-积分-微分PID（proportional-integral-derivative）控制器、查找表、神经网络估计器和虚拟传感器组成。其中，神经网络估计器经过在线训练可适应环境的变化，以估计漂浮式海上风力发电机的有效风速并预测其未来值，并将当前和未来的风速数据组合起来提供给控制器；虚拟传感器还可根据与风机发电相关的其他可用因素（如风机的俯仰角和角速度）提供有效风速的测量。最后，通过仿真分析验证了所提控制方法的有效性。与传统PID相比，所提方法可将正弦风测量精确度提升约16%，平均改善率约为8%。

关键词

漂浮式海上风力发电机 / 变桨距控制 / 神经网络估计器 / 虚拟传感器 / 风能

Abstract

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With the development of offshore wind power in China advancing to the deep sea, there are no buoys in the deep sea area, the wind speed near the coast is not large and unstable, and the flotation device is affected by complex factors including extreme environmental conditions (e.g., waves and ocean currents), so there exist problems such as the poor control effect of floating offshore wind turbines due to the low accuracy of wind speed measurement data. Under this background, an intelligent power control method for the converter in a deep-sea floating offshore wind turbine is proposed, which is based on a neural network effective wind speed estimator. The control system is composed of a proportional-integral-derivative (PID) controller, lookup table, a neural network estimator and a virtual sensor, in which the neural network estimator is trained online to adapt to the transformation of the environment, so as to estimate the effective wind speed of the floating offshore wind turbine and predict its future value. In addition, the current and future wind speed data are combined and further provided to the controller. The virtual sensor can also provide measurement of the effective wind speed based on other available factors (e.g., the pitch angle and angular velocity of a fan) related to the power generation of the fan. Finally, the effectiveness of the proposed control method is verified by simulation analysis. Compared with the traditional PID, this method can improve the accuracy of sinusoidal wind measurement by about 16%, and the average improvement rate is about 8%.

Key words

Floating offshore wind turbine / variable pitch control / neural network estimator / virtual sensor / wind energy

引用本文

夏懿, 宋汶秦, 杨茜. 适用于深远海漂浮式海上风机变换器的智能功率控制. 电源学报, 2026 , 24 (1) : 157 -167 . DOI: 10.13234/j.issn.2095-2805.2026.1.157

Yi XIA, Wenqin SONG, Qian YANG. Intelligent Power Control for Converter in Deep-sea Floating Offshore Wind Turbine[J]. Journal of Power Supply, 2026 , 24 (1) : 157 -167 . DOI: 10.13234/j.issn.2095-2805.2026.1.157

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2026年第24卷第1期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.1.157

接收时间：2023-03-02
首发时间：2026-10-26
出版时间：2026-01-30

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收稿日期：2023-03-02
修回日期：2023-07-06
录用日期：2023-07-17

基金

作者信息

国网甘肃省电力公司，兰州　730050

通讯作者:

杨茜（1994—　），女，通信作者，硕士，工程师。研究方向：水电工程。E-mail：yangq94@163.com。

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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