A new fragled series resonance-bridge fault current limiter with fuzzy logic controller to enhance fault ride through capabilities in DFIG-wind farms

被引:1
|
作者
Verma, Preeti [1 ]
Kaimal, Seethalekshmi [1 ]
Dwivedi, Bharti [1 ]
机构
[1] Department of Electrical Engineering, Institute of Engineering and Technology, Lucknow,226015, India
关键词
Fault current limiters - Fuzzy logic - Resonance - MATLAB - Asynchronous generators - Reactive power - Computer circuits - Electric utilities;
D O I
暂无
中图分类号
学科分类号
摘要
In the wind energy market, the doubly-fed induction generator (DFIG) is the most popular generator over the other generators. However, the noteworthy concern for DFIG-based wind farms (WFs) is to retain transient stability at fault condition and according to grid code requirements, generators should connect to the grid. Therefore, the need of some solution against the severity is essential. In this paper, a series resonance-bridge type fault current limiter (SR-BFCL) with accurate nonlinear fuzzy logic control is proposed to achieve the fault ride through (FRT) capabilities. The proposed FCL provides low-voltage ride through (LVRT) and reactive power compensation. A comparative analysis has been performed with inductive-Type bridge fault current limiter (I-BFCL). Simulation results are performed in MATLAB/Simulink environment under both symmetrical and unsymmetrical faults. A quantitative analysis is reported in terms of the performance indexes that indicate the proposed FCL gives a better performance in comparison to the I-BFCL. © 2021 Inderscience Enterprises Ltd.. All rights reserved.
引用
收藏
页码:117 / 139
相关论文
共 48 条
  • [41] Coordinate Operation of Fuzzy Logic Voltage Regulator and Bi-2212 SFCL for Enhancing Fault Ride Through Capability of DFIG Wind Turbines
    Romphochai, Sillawat
    Pichetjamroen, Achara
    Teerakawanich, Nithiphat
    Hongesombut, Komsan
    2017 INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON), 2017,
  • [42] Low voltage ride-through enhancement of DFIG-based wind turbine using DC link switchable resistive type fault current limiter
    Naderi, Seyed Behzad
    Negnevitsky, Michael
    Jalilian, Amin
    Hagh, Mehrdad Tarafdar
    Muttaqi, Kashem M.
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2017, 86 : 104 - 119
  • [43] Low-Voltage Ride Through Enhancement of Both DFIG and SEIG-Based Wind Power Plants by RC-Type Solid-State Fault Current Limiter
    Ghorbani, Mohsen
    Mozafari, Babak
    Firouzi, Mehdi
    Golshan, Farzad
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2022, 49 (6-7) : 729 - 742
  • [44] Performance improvement of DFIG-based wind farms using NARMA-L2 controlled bridge-type flux coupling non-superconducting fault current limiter
    Islam, Md. Rashidul
    Hasan, Jakir
    Hasan, Md. Mahmudul
    Huda, Md. Najmul
    Hossain Sadi, Mohammad Ashraf
    AbuHussein, Ahmed
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2020, 14 (26) : 6580 - 6593
  • [45] Design of Robust Fuzzy Logic Controller Based on Gradient Descent Algorithm with Parallel-Resonance Type Fault Current Limiter for Grid-Tied PV System
    Ul Islam, Saif
    Zeb, Kamran
    Kim, Soobae
    SUSTAINABILITY, 2022, 14 (19)
  • [46] Augmentation of fault ride-through capability of PMSG in a wind power plant using resistive SFCL and a new reactive current injection controller
    Kheibargir, Davood
    Zeinali, Rahim
    Aliabadi, Seyed Mohsen
    34TH INTERNATIONAL POWER SYSTEM CONFERENCE (PSC2019), 2019, : 623 - 631
  • [47] Design of Capacitive Bridge Fault Current Limiter for Low-Voltage Ride-Through Capacity Enrichment of Doubly Fed Induction Generator-Based Wind Farm
    Padmaja, A.
    Shanmukh, Allusivala
    Mendu, Siva Subrahmanyam
    Devarapalli, Ramesh
    Serrano Gonzalez, Javier
    Garcia Marquez, Fausto Pedro
    SUSTAINABILITY, 2021, 13 (12)
  • [48] Whale optimization algorithm-based Sugeno fuzzy logic controller for fault ride-through improvement of grid-connected variable speed wind generators
    Qais, Mohammed H.
    Hasanien, Hany M.
    Alghuwainem, Saad
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 87 (87)