Surrogate-Assisted Cooperation Control of Network-Connected Doubly Fed Induction Generator Wind Farm With Maximized Reactive Power Capacity

Dong, Zhen; Li, Zhongguo; Xu, Yiqiao; Guo, Xiaoyu; Ding, Zhengtao

doi:10.1109/tii.2021.3084895

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…For example, [47] designed an RL-based adaptive wind farm control method to enhance the stability of power systems with wind energy penetration. Dong et al [48] proposed a cooperative control method to maximize the reactive power capacity of network-connected wind farms.…”

Section: Essential Objectives In Wind Farm Controlmentioning

confidence: 99%

Wind farm control technologies: from classical control to reinforcement learning

2022

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Wind power plays a vital role in the global effort towards net zero. The recent figure shows that 93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year increase. Control system is the core in wind farm operations and has essential influences on the farm’s power capture efficiency, economic profitability, and operation & maintenance cost. However, wind farms’ inherent system complexities and the aerodynamic interactions among wind turbines bring significant barriers to control systems design. The wind industry has recognized that new technologies are needed to handle wind farm control tasks, especially for large-scale offshore wind farms. This paper provides a comprehensive review of the development and most recent advances of wind farm control technologies. This covers the introduction of fundamental aspects in wind farm control in terms of system modelling, main challenges, and control objectives. Existing wind farm control methods for different purposes, including layout optimization, power generation maximization, fatigue loads minimization, and power reference tracking, are investigated. Moreover, a detailed discussion regarding the differences and connections among model-based, model-free and data-driven wind farm approaches is presented. In addition, highlights are made on the state-of-the-art wind farm control technologies based on reinforcement learning - a booming machine learning technique that has drawn worldwide attention. Future challenges and research avenues in wind farm control are also analysed.

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Section: Essential Objectives In Wind Farm Controlmentioning

confidence: 99%

Wind farm control technologies: from classical control to reinforcement learning

2022

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“…CFRP Subsurface Defects in Lock-In Thermography; TII April 2022 2571-2581 Dong, Z., Li, Z., Xu, Y., Guo, X., and Ding, Z., Surrogate-Assisted Cooperation Control of Network-Connected Doubly Fed Induction Generator Wind Farm With Maximized Reactive Power Capacity;TII Jan. 2022 197-206 Dong, Z., see Mao, S., TII July 2022 4737-4748 Dong, Z., see Yang, L., 5231-5241 Dong, Z.Y., see Ruan, J., TII Nov. 2022 7814-7823 Donisi, A., see Benedetto, L.D., TII Aug. 2022 5330-5337 Dou, C., see Zhang, Z., TII March 20221746-1757 Dou, C., see Zhang, Z., TII June 2022 3987-3999 Dou, W., see Jia, Y., TII May 2022 3306-3315 Du, C., see Li, J., TII June 2022 4107-4117 Du, H., see Wu, D., TII Nov. 2022 7435-7443 Du, H., see Huo, J., TII Dec. 2022 8654-8663 Du, J., see Liu, X., TII Feb. 2022 986-995 Du, J., see Yan, M., TII May 2022 3349-3357 Du, S., see Li, J., TII March 20222021-2031 Du, W., see Wu, Q., 2213-2222 Du, W., see He, W., TII June 2022 3595-3608 Du, W., see Mao, S., TII July 2022 4737-4748 Du, W., see Zhou, J., TII Oct. 2022 7252-7261 Du, X., see Zhao, Y., TII May 2022 3203-3212 Du, X., see Hossain, M.M., TII June 2022 3863-3872 Du, X., see Wang, Y., TII Dec. 2022 9027-9036 Du, Y., see Sun, Y., TII June 2022 4078-4087 Du, Y., see Liu, D., TII Nov. 2022 8119-8128 Duan, B., see Zhang, C., TII Feb. 2022 932-942 Duan, G., see Kong, X., TII Sept. 2022 6308-6316 Duan, G., see Yang, B., TII Oct. 2022 6743-6755 Duan, N., Huang, C., Sun, C., and Min, L., Smart Meters Enabling Voltage Monitoring and Control: The Last-Mile Voltage Stability Issue; TII Jan. 2022 677-687 Duan, P., see Li, J., TII Aug. 2022 5318-5329 Duan, Q., see Wang, Y., TII Dec. 2022 9027-9036 Duan, X., see Xiao, H., TII Aug. 2022 5148-5160 Duan, Y., see Zhang, Y., TII Aug. 2022 5504-5514 Dumitrescu, A., see Sanduleac, M.,…”

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confidence: 99%

2022 Index IEEE Transactions on Industrial Informatics Vol. 18

2022

IEEE Trans. Ind. Inf.

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This index covers all technical items-papers, correspondence, reviews, etc.-that appeared in this periodical during 2022, and items from previous years that were commented upon or corrected in 2022. Departments and other items may also be covered if they have been judged to have archival value.The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.

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An Adaptive Droop Coefficient Based Voltage Control Approach for Wind Power Plants through Enhanced Reactive Power Support

Shabbir

Chakrabarti

2022

2022 IEEE/IAS 58th Industrial and Commercial Power Systems Technical Conference (I&CPS)

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Surrogate-Assisted Cooperation Control of Network-Connected Doubly Fed Induction Generator Wind Farm With Maximized Reactive Power Capacity

Cited by 4 publications

References 29 publications

Wind farm control technologies: from classical control to reinforcement learning

Wind farm control technologies: from classical control to reinforcement learning

2022 Index IEEE Transactions on Industrial Informatics Vol. 18

An Adaptive Droop Coefficient Based Voltage Control Approach for Wind Power Plants through Enhanced Reactive Power Support

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