Distributed control of offshore wind turbine group with input delay

被引:0
|
作者
Tang Z. [1 ]
Wang B. [1 ]
Liu W.-Y. [1 ]
Cao Z.-J. [2 ]
机构
[1] College of Energy and Electrical Engineering, Hohai University, Nanjing
[2] Nanjing Howard Technology Science and Technology Company, Nanjing
来源
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 12期
基金
中国国家自然科学基金;
关键词
Casimir function; Distributed control; Doubly fed wind turbine group; Hamilton; Offshore wind farms; Time delay;
D O I
10.7641/CTA.2020.00101
中图分类号
学科分类号
摘要
With the large-scale development of offshore wind power and the increasing scale of power grid interconnection, time delay mainly occurs in the signal measurement and transmission of wide area measurement system of wind farm, which leads to the performance degradation or even instability of the wind turbine system, and seriously affects the normal operation of the wind farm. Therefore, based on Hamilton energy theory, the distributed time delay control of doubly fed wind turbine group is studied in this paper. Firstly, the doubly fed wind power system is implemented to PCH-D model. Then, Casimir function is introduced into the PCH-D model of the single wind power system to make the wind power system stable under time delay. Then, the wind turbine group is networked and the distributed time delay control strategy is proposed to maintain the wind turbine group system stable in the time delay range of 30_300 ms. Finally, the simulation results show that the control strategy proposed in this paper, can effectively solve the input time delay problem, and reduce the system error caused by time delay, also improve the stability and control accuracy of the wind turbine group. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码:2581 / 2590
页数:9
相关论文
共 25 条
  • [1] OWENS B N., The Wind Power Story: A Century of Innovation that Reshaped the Global Energy Landscape, (2019)
  • [2] WU Q W, SUN Y Z., Modeling and Modern Control of Wind Power, (2018)
  • [3] APOSTOLAKI-IOSIFIDOU E, MCCORMACK R, KEMPTON W, Et al., Transmission design and analysis for large-scale offshore wind energy development, IEEE Power and Energy Technology Systems Journal, 6, 1, pp. 2332-7707, (2019)
  • [4] YI X, SCUTARIU M, SMITH K., Optimisation of offshore wind farm inter-array collection system, IET Renewable Power Generation, 13, 11, pp. 1990-1999, (2019)
  • [5] HE Jinghan, WANG Zhenji, LUO Guomin, Et al., Hierarchical distributed control of voltage and active power for VSC-MTDC, Power System Technology, 42, 12, pp. 3951-3959, (2018)
  • [6] KONG X B, LIU X J, MAL, Et al., Hierarchical distributed model predictive control of standalone wind/solar/battery power system, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49, 8, pp. 1570-1581, (2019)
  • [7] XUE N, CHAKRABORTTY A., Control inversion: A clusteringbased method for distributed wide-area control of power systems, IEEE Transactions on Control of Network Systems, 6, 3, pp. 937-949, (2019)
  • [8] WANG Bing, DOU Yu, WANG Honghua, Distributed cooperative control research of doubly fed wind turbine groups in offshore windfarms, Proceedings of the CSEE, 36, 19, pp. 5279-5287, (2016)
  • [9] PHADKE A G, BI T S., Phasor measurement units, WAMS, and their applications in protection and control of power systems, Journal of Modern Power Systems and Clean Energy, 6, 4, pp. 619-629, (2018)
  • [10] LIN Z Z, WEN F S, DING Y, Et al., WAMS-based coherency detection for situational awareness in power systems with renewables, IEEE Transactions on Power Systems, 33, 5, pp. 5410-5426, (2018)