Blackstart of Power Grids with Inverter-Based Resources

被引:0
|
作者
Jain, Himanshu [1 ]
Seo, Gab-Su [1 ]
Lockhart, Eric [1 ]
Gevorgian, Vahan [1 ]
Kroposki, Benjamin [1 ]
机构
[1] Natl Renewable Energy Lab, Golden, CO 80401 USA
来源
2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM) | 2020年
关键词
Black start with inverters; collective black start; inverter-driven black start; inrush current; soft start;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper presents the findings of our investigation into inverter-based resource- (IBR-) driven blackstart of electric grids. Four potential black-start configurations with different setups are presented. To evaluate the technical feasibility of IBR-driven black start in the four configurations, a behavioral model of inverters that mimics current-limited inverter operation is developed using variable resistors in the MATLAB Simulink/Simscape environment. The inverter model is connected to an induction motor through transformers and a transmission line to simulate its startup. Simulation results show that even with the limited current supply capability of inverters because of their physical constraints, IBRs can black-start a motor under certain conditions. Results also show that by using soft-start techniques, such as ramped supply voltage, inrush currents can be reduced, thereby expanding the conditions under which IBRs can provide black-start support. Simulation results with different scenarios lead to discussions and key takeaways that could be valuable for further IBR-driven blackstart research.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] Improving the Differential Protection of Power Transformers Based on Clarke Transform and Fuzzy Systems
    Hosseinimoghadam, Seyed Mohammad Sadegh
    Dashtdar, Masoud
    Dashtdar, Majid
    JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS, 2022, 33 (02) : 610 - 624
  • [32] Power transformers internal fault diagnosis based on deep convolutional neural networks
    Afrasiabi, Mousa
    Afrasiabi, Shahabodin
    Parang, Benyamin
    Mohammadi, Mohammad
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2019, 37 (01) : 1165 - 1179
  • [33] Research on novel power transformer protection based on self-correction function
    Yan, Xu
    Jing, Ma
    Zengping, Wang
    TENCON 2005 - 2005 IEEE REGION 10 CONFERENCE, VOLS 1-5, 2006, : 743 - +
  • [34] A new differential protection algorithm for power reactors based on the second central moment
    Vazquez, Ernesto
    Andrade, Manuel A.
    Esponda, Hector
    Avila, Jesus
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 118
  • [35] Designing a composite deep learning based differential protection scheme of power transformers
    Afrasiabi, Shahabodin
    Afrasiabi, Mousa
    Parang, Benyamin
    Mohammadi, Mohammad
    APPLIED SOFT COMPUTING, 2020, 87
  • [36] Low-Frequency Demagnetization for Power Transformers Based on Modified J-A Model
    Li, Jiangtao
    Li, Zheng
    Li, Tao
    Gu, Yue
    Jiang, Weihua
    Zheng, Minjun
    TENCON 2015 - 2015 IEEE REGION 10 CONFERENCE, 2015,
  • [37] Sensitive relay for power auto-transformer protection based on fifth harmonic criteria
    Htita, I. M.
    Mousa, Sabry
    Hasan, S.
    2017 NINETEENTH INTERNATIONAL MIDDLE-EAST POWER SYSTEMS CONFERENCE (MEPCON), 2017, : 114 - 120
  • [38] Decision Tree based discrimination between inrush currents and internal faults in power transformer
    Samantaray, S. R.
    Dash, P. K.
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2011, 33 (04) : 1043 - 1048
  • [39] A Setting-Free Differential Protection for Power Transformers Based on Second Central Moment
    Esponda, Hector
    Vazquez, Ernesto
    Andrade, Manuel A.
    Johnson, Brian K.
    IEEE TRANSACTIONS ON POWER DELIVERY, 2019, 34 (02) : 750 - 759
  • [40] A method to identify inrush currents in power transformers protection based on the differential current gradient
    Negreiros Alencar, Raidson Jenner
    Bezerra, Ubiratan Holanda
    Damasceno Ferreira, Andre Mauricio
    ELECTRIC POWER SYSTEMS RESEARCH, 2014, 111 : 78 - 84