面向效率提升的风电机组联合仿真建模优化与步长选取研究

doi:10.19595/j.cnki.1000-6753.tces.240940

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (5302 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要随着风电研究的不断深入,其仿真模型中需要考虑的因素也逐渐增加,以往相对独立的电气仿真和气动机械仿真趋于联合,风电机组的机-电联合仿真技术得到了开发与应用。由于电气模型阶数较高,所需仿真步长较小,降低了联合仿真效率。为了提升机-电联合仿真效率,需要从模型和仿真步长方面进行优化。首先,对复杂的电气模型进行降阶处理,提高电气侧仿真速度,选取GH Bladed和Matlab/Simulink搭建机-电联合仿真平台,形成机-电联合仿真优化模型;然后,提出基于残差相似度和特征选择验证分析法的综合评估方法,将仿真精度与速度评估指标加权合并,形成仿真步长选取指导方法。最后,选择功率、转速以及高低速轴转矩等参量,在风速、电气侧扰动工况下,验证机-电联合仿真优化模型仿真效率的提升效果,并依据综合评估方法为步长选取提出参考建议。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	颜湘武
	任浩洋
	蔡光
	张书瑞
	贾焦心

关键词 ：风电机组, 仿真平台, 仿真步长, 精度评估, 仿真速度

Abstract：With the development of wind power research, the factors considered in the simulation model are gradually increasing. The demand for wind turbine models that take into account both electrical and mechanical characteristics is increasing, thus promoting the development and application of co-simulation technology. However, the detailed electrical model has strict requirements on the simulation time step, which reduces the efficiency of co-simulation. Some scholars have properly simplified the electrical model to improve the simulation speed when carrying out co-simulation. But, improper selection of simulation time step has a negative impact on simulation accuracy. Therefore, this paper studies the model optimization and step size selection to solve the problem of the contradiction between simulation accuracy and speed.
First, the complex electrical model is optimized by ignoring the power electronic switching model and reducing the order of the higher-order model, so that the computational complexity is reduced and the application range of the simulation step size is increased. GH Bladed and Matlab/Simulink are selected to build the co-simulation platform. Then, a comprehensive evaluation method based on residual similarity and feature selection verification is proposed, which takes into account simulation accuracy and speed. The evaluation of simulation accuracy is divided into two aspects: global and transient difference. The comprehensive evaluation index is formed by combining the evaluation index of simulation accuracy and simulation speed in a weighted way to guide the selection of simulation time step. Finally, the co-simulation is carried out to verify the effect of the optimization model on the simulation efficiency under the conditions of wind speed disturbance, frequency disturbance and fault crossing disturbance. According to the simulation results and the comprehensive evaluation method, the reference suggestions for the selection of simulation step size are put forward.
Through simulation results and analysis, the following conclusions are drawn: (1) Through the optimized model, the co-simulation model can be run at a larger simulation time step, and the co-simulation efficiency is improved. (2) According to the proposed comprehensive evaluation method, the simulation results are evaluated from two dimensions of simulation accuracy and speed, which solves the problem of quantitative evaluation of the accuracy and speed of the model simulation results. (3) Through the co-simulation of various working conditions and the quantitative evaluation of simulation accuracy and speed, the following conclusions are drawn: Under the background of the simulation in this paper, under the condition of wind speed fluctuation, the simulation time step of co-simulation is chosen to be around 0.05 s; under the condition of frequency disturbance, the simulation time step of co-simulation is chosen to be around 0.01 s; under the condition of fault ride-through disturbance, the simulation time step of co-simulation is chosen to be around 0.005 s. (4) Based on the evaluation results of multi-condition simulation, the factors such as time scale of simulation condition and mutation characteristics of observed parameters should be fully considered in the selection of simulation time step. When the time scale is large and the observed parameters do not have mutation characteristics, the larger simulation time step can be selected. When the time scale is small and the observed parameters have mutation characteristics, the selection of simulation time step should be reduced appropriately.

Key words： Wind turbines simulation platform simulation time step accuracy evaluation simulation speed

收稿日期: 2024-05-31

PACS:

TM614

基金资助:北京市自然科学基金（3212037）、国家自然科学基金（52207102）资助项目

通讯作者: 任浩洋男,2000年生,硕士研究生,研究方向为新能源电力系统分析与控制。E-mail：renhaoyang188@163.com

作者简介: 颜湘武男,1965年生,教授,博士生导师,研究方向为新能源电力系统分析与控制、现代电力变换、新型储能与节能技术控制。E-mail：xiangwuy@ncepu.edu.cn

引用本文:

颜湘武, 任浩洋, 蔡光, 张书瑞, 贾焦心. 面向效率提升的风电机组联合仿真建模优化与步长选取研究[J]. 电工技术学报, 2025, 40(13): 4189-4199. Yan Xiangwu, Ren Haoyang, Cai Guang, Zhang Shurui, Jia Jiaoxin. Research on Model Optimization and Time Step Selection of Wind Turbine Co-Simulation for Improving Efficiency. Transactions of China Electrotechnical Society, 2025, 40(13): 4189-4199.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.240940 https://dgjsxb.ces-transaction.com/CN/Y2025/V40/I13/4189

[1] 张祥宇, 胡剑峰, 付媛, 等. 风储联合系统的虚拟惯量需求与协同支撑[J]. 电工技术学报, 2024, 39(3): 672-685.
Zhang Xiangyu, Hu Jianfeng, Fu Yuan, et al.Virtual inertia demand and collaborative support of wind power and energy storage system[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 672-685.
[2] 杨德健, 王鑫, 严干贵, 等. 计及调频死区的柔性风储联合频率控制策略[J]. 电工技术学报, 2023, 38(17): 4646-4656.
Yang Dejian, Wang Xin, Yan Gangui, et al.Flexible frequency regulation scheme of DFIG embed battery energy storage system considering deadbands[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4646-4656.
[3] 胡正阳, 高丙团, 张磊, 等. 风电机组双向支撑能力分析与自适应惯量控制策略[J]. 电工技术学报, 2023, 38(19): 5224-5240.
Hu Zhengyang, Gao Bingtuan, Zhang Lei, et al.Bidirectional support capability analysis and adaptive inertial control strategy of wind turbine[J]. Transactions of China Electrotechnical Society, 2023, 38(19): 5224-5240.
[4] 张祥宇, 邵孜建, 付媛. 风储并网发电系统的虚拟多段协同调速与频率安全支撑技术[J/OL]. 电工技术学报, 1-17[2024-12-26]. https://doi.org/10.19595/ j.cnki.1000-6753.tces.241464.
Zhang Xiangyu, Shao Zijiann, Fu Yuan. Virtual multi-stage coordinated speed regulation and frequency safety support technology of wind-storage grid-connected power generation system[J/OL]. Transactions of China Electrotechnical Society, 1-17[2024-12-26]. https://doi.org/10.19595/j.cnki.1000-6753.tces.241464.
[5] 张德勇, 朱国荣, 李悦功, 等. 抑制直驱风机故障恢复后暂态过电压的协调控制策略[J/OL]. 电源学报, 2025: 1-15. (2025-03-05). https://kns.cnki.net/kcms/ detail/12.1420.TM.20250304.1811.004.html.
Zhang Deyong, Zhu Guorong, Li Yuegong, et al. Coordinated control strategy for restraining transient overvoltage after fault recovery of direct-driven fan[J/OL]. Journal of Power Supply, 2025: 1-15. (2025-03-05). https://kns.cnki.net/kcms/detail/12.1420. TM.20250304.1811.004.html.
[6] Jonkman J M, Buhl M L.FAST user’s guide[R]. National Renewable Energy Laboratory(NREL), Golden, Co, tech. rep., 2005.
[7] Bossanyi E A.GH bladed theory manual[R]. Bristol: Gaerrad Hassan and Partners Limited, 2015.
[8] Imran R M, Hussain D M A, Soltani M, et al. Optimal tuning of multivariable disturbance-observer-based control for flicker mitigation using individual pitch control of wind turbine[J]. IET Renewable Power Generation, 2017, 11(8): 1121-1128.
[9] Mohammadi E, Rasoulinezhad R, Moschopoulos G.Using a supercapacitor to mitigate battery microcycles due to wind shear and tower shadow effects in wind-diesel microgrids[J]. IEEE Transactions on Smart Grid, 2020, 11(5): 3677-3689.
[10] 苗风麟, 施洪生, 张小青. 风力发电机组多领域耦合模型及动态响应分析[J]. 中国电机工程学报, 2015, 35(7): 1704-1712.
Miao Fenglin, Shi Hongsheng, Zhang Xiaoqing.Modelling of wind turbines coupled in multi-domain and dynamic response analysis[J]. Proceedings of the CSEE, 2015, 35(7): 1704-1712.
[11] 司金冬, 柴兆森, 李辉, 等. 基于电气阻尼-刚度控制的双馈风电机组轴系扭振抑制策略[J]. 电力自动化设备, 2022, 42(1): 140-147.
Si Jindong, Chai Zhaosen, Li Hui, et al.Torsional vibration suppression strategy for doubly-fed wind turbine shafting based on electrical damping and stiffness control[J]. Electric Power Automation Equipment, 2022, 42(1): 140-147.
[12] 杨超, 李东翰, 雷显帅, 等. 虚拟惯量控制对直驱风电机组载荷影响的分析及评估[J]. 电力系统自动化, 2024, 48(7): 258-266.
Yang Chao, Li Donghan, Lei Xianshuai, et al.Analysis and evaluation of impact of virtual inertia control on load of direct-drive wind turbine[J]. Automation of Electric Power Systems, 2024, 48(7): 258-266.
[13] 杨超, 李东翰, 胡姚刚, 等. 附加阻尼控制下风电机组机网耦合载荷建模及分析[J]. 太阳能学报, 2024, 45(2): 102-108.
Yang Chao, Li Donghan, Hu Yaogang, et al.Modeling and analysis on machine-grid coupling loads of WTGS with additional damping control[J]. Acta Energiae Solaris Sinica, 2024, 45(2): 102-108.
[14] 刘兴华, 敬维, 林威. GH Bladed和Matlab的交互软件设计及风力发电机的独立变桨控制器仿真研究[J]. 中国电机工程学报, 2013, 33(22): 83-88, 14.
Liu Xinghua, Jing Wei, Lin Wei.Interactive software platform design based on GH bladed and Matlab with simulation study of individual pitch controller of wind turbine[J]. Proceedings of the CSEE, 2013, 33(22): 83-88, 14.
[15] 胡文平, 周文, 王磊, 等. 基于联合仿真的风电机组低电压穿越传动链扭振抑制研究[J]. 电力系统保护与控制, 2016, 44(24): 196-201.
Hu Wenping, Zhou Wen, Wang Lei, et al.Study on suppression strategy for wind turbine drive train torsional vibration under grid fault based on co-simulation[J]. Power System Protection and Control, 2016, 44(24): 196-201.
[16] 应有, 孙勇, 杨靖, 等. 大型双馈风电机组电网故障穿越过程载荷特性分析[J]. 电力系统自动化, 2020, 44(12): 131-138.
Ying You, Sun Yong, Yang Jing, et al.Load characteristic analysis of grid fault ride-through process for DFIG based large wind turbine[J]. Automation of Electric Power Systems, 2020, 44(12): 131-138.
[17] 李少林, 秦世耀, 王瑞明, 等. 一种双馈风电机组一次调频协调控制策略研究[J]. 太阳能学报, 2020, 41(2): 101-109.
Li Shaolin, Qin Shiyao, Wang Ruiming, et al.A collaborative control of primary frequency regulation for DFIG-WT[J]. Acta Energiae Solaris Sinica, 2020, 41(2): 101-109.
[18] 秦世耀, 代林旺, 王瑞明, 等. 考虑风电机组功率跌落和机械载荷优化的虚拟惯量控制方法[J]. 电网技术, 2021, 45(5): 1665-1672.
Qin Shiyao, Dai Linwang, Wang Ruiming, et al.Virtual inertia control method considering wind turbine power drop and mechanical load optimization[J]. Power System Technology, 2021, 45(5): 1665-1672.
[19] 洪国庆, 吴国旸, 金宇清, 等. 电力系统风力发电建模与仿真研究综述[J]. 电力系统自动化, 2024, 48(17): 22-36.
Hong Guoqing, Wu Guoyang, Jin Yuqing, et al.Review on research of modeling and simulation for wind power generation in power system[J]. Automation of Electric Power Systems, 2024, 48(17): 22-36.
[20] Tang W, Hu Jiabing, Chang Yuanzhu, et al.Modeling of DFIG-based wind turbine for power system transient response analysis in rotor speed control timescale[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6795-6805.
[21] 黄森, 姚骏, 钟勤敏, 等. 含跟网和构网型新能源发电单元的混联电力系统暂态同步稳定分析[J]. 中国电机工程学报, 2024, 44(21): 8378-8392.
Huang Sen, Yao Jun, Zhong Qinmin, et al.Transient synchronization stability analysis of hybrid power system with grid-following and grid-forming renewable energy generation units[J]. Proceedings of the CSEE, 2024, 44(21): 8378-8392.
[22] 李梦杰, 谢震, 高翔, 等. 弱电网下双馈风电机组混合功率同步控制策略及稳定性分析[J]. 中国电机工程学报, 2023, 43(21): 8388-8400.
Li Mengjie, Xie Zhen, Gao Xiang, et al.Hybrid power synchronization control strategy of DFIG-based wind turbines and its stability analysis under weak grid[J]. Proceedings of the CSEE, 2023, 43(21): 8388-8400.
[23] 颜湘武, 蔡光, 李锐博, 等. 计及功角偏差和阻尼效应的构网型双馈风机暂态稳定性分析[J]. 中国电机工程学报, 2025, 45(7): 2616-2633.
Yan Xiangwu, Cai Guang, Li Ruibo, et al.Transient stability analysis of grid forming-doubly fed induction generator with power angle deviation and damping effect[J]. Proceedings of the CSEE, 2025, 45(7): 2616-2633.
[24] Duffy A P, Martin A J M, Orlandi A, et al. Feature selective validation (FSV) for validation of comput-ational electromagnetics (CEM). Part I-the FSV method[J]. IEEE Transactions on Electro-magnetic Compatibility, 2006, 48(3): 449-459.