Multi-objective and resilient control on hybrid wind farms under healthy/faulty and off-grid/grid-tied states

被引:3
作者
Faraji, Hossien [1 ]
Beigvand, Narges Yavari [2 ]
Hemmati, Reza [3 ,4 ]
机构
[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran
[2] K N Toosi Univ Technol, Dept Elect Engn, Tehran, Iran
[3] Kermanshah Univ Technol, Dept Elect Engn, Kermanshah, Iran
[4] Kermanshah Univ Technol, Dept Elect Engn, Kermanshah 6715685420, Iran
关键词
Fault ride through; Doubly fed induction generator; Resilience; Unbalanced operating condition; CONTROL SCHEME; FAULT RIDE;
D O I
10.1016/j.epsr.2022.109004
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a multi-objective control scheme on the interconnected wind farms which are formed by different wind turbines including doubly fed induction generators (DFIG) and squirrel cage induction generators (SCIG). The proposed control system aims to control voltage, frequency, and mechanical power in the wind farms under wind/load alterations, faults, and outages. The voltage stability and fault ride through capability are improved. The proposed model also deals with unbalanced loading and faulty conditions. All the aforementioned points are realized under both off-grid and grid-tied conditions. For voltage stability improvement, each wind farm is integrated with one static VAR compensator (SVC) and the grid-side is integrated with one static syn-chronous compensator (STATCOM). In the grid-tied, the resilience following events is improved by proper control of STATCOM. In the off-grid, the installed SVC at each site is responsible for increasing resilience. The proposed control systems are modeled and implemented on a typical test grid, and numerical simulations are carried out in MATLAB/SIMULINK software. It is demonstrated that the proposed multi-objective control scheme efficiently controls all individual wind farms, achieves a coordinated management between different wind farms, deals with stability/unbalanced operating condition/faults, increases resilience and improves fault ride through capability.
引用
收藏
页数:12
相关论文
共 22 条
[1]   Smart frequency control in low inertia energy systems based on frequency response techniques: A review [J].
Cheng, Yi ;
Azizipanah-Abarghooee, Rasoul ;
Azizi, Sadegh ;
Ding, Lei ;
Terzija, Vladimir .
APPLIED ENERGY, 2020, 279
[2]   Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations [J].
Dong, Hongyang ;
Zhang, Jincheng ;
Zhao, Xiaowei .
APPLIED ENERGY, 2021, 292
[3]   Primary frequency control using hierarchal fuzzy logic for a wind farm based on SCIG connected to electrical network [J].
Elyaalaoui, Kamal ;
Ouassaid, Mohammed ;
Cherkaoui, Mohamed .
SUSTAINABLE ENERGY GRIDS & NETWORKS, 2018, 16 :188-195
[4]   Enhancement of transient stability of distribution system with SCIG and DFIG based wind farms using STATCOM [J].
Gounder, Yasoda Kailasa ;
Nanjundappan, Devarajan ;
Boominathan, Veerakumar .
IET RENEWABLE POWER GENERATION, 2016, 10 (08) :1171-1180
[5]   Online Optimal Feedback Voltage Control of Wind Farms: Decentralized and Asynchronous Implementations [J].
Guo, Yifei ;
Gao, Houlei ;
Wang, Dong ;
Wu, Qiuwei .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2021, 12 (02) :1489-1492
[6]   Distributed cooperative voltage control of wind farms based on consensus protocol [J].
Guo, Yifei ;
Gao, Houlei ;
Wu, Qiuwei .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2019, 104 :593-602
[7]   Multilevel and Advanced Control Scheme for Multimicrogrid Under Healthy-Faulty and Islanded-Connected Conditions [J].
Hemmati, Reza ;
Faraji, Hossien ;
Beigvand, Narges Yavari .
IEEE SYSTEMS JOURNAL, 2022, 16 (02) :2639-2647
[8]   Multi objective control scheme on DFIG wind turbine integrated with energy storage system and FACTS devices: Steady-state and transient operation improvement [J].
Hemmati, Reza ;
Faraji, Hossien ;
Beigvand, Narges Yavari .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2022, 135
[9]   Decentralized voltage control of wind farm based on gradient projection method [J].
Jiao, Wenshu ;
Wu, Qiuwei ;
Huang, Sheng ;
Chen, Jian .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 123
[10]   Deep learning approach for wind speed forecasts at turbine locations in a wind farm [J].
Kou, Peng ;
Wang, Chen ;
Liang, Deliang ;
Cheng, Song ;
Gao, Lin .
IET RENEWABLE POWER GENERATION, 2020, 14 (13) :2416-2428