Online Assessment of Voltage Stability Region using an Artificial Neural Network

被引:2
|
作者
Samy, A. Karuppa [1 ]
Venkadesan, A. [1 ]
机构
[1] Natl Inst Technol Puducherry, Dept EEE, Karaikal, India
关键词
Voltage Stability; VCPI; Multi-Layer Feed Forward; Mean square error; MARGIN;
D O I
10.1109/ICEECCOT52851.2021.9708047
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
As the voltage instability disturbs the adequate operation of power system, incessant observing of the system status is needed Artificial Neural networks can be used for voltage stability monitoring due to the non-linear nature of the voltage stability prediction problem. In this work, a Multi-layered Feed Forward Neural Network (MLFFNN) with less amount of neurons is used It estimates the voltage stability index of all the buses by using VCPI (Voltage Collapse point indicator) under different loading conditions. Test results indicate that the proposed Multi-Layer Feed Forward neural network-based approach gives exact assessment of Voltage Stability Index values for different loading conditions. Since the method is much quicker it can evidence that, it is easily adopted for on-line applications as compared to conventional power flow methods. The standard IEEE 14 bus test system is tested with the proposed method.
引用
收藏
页码:757 / 761
页数:5
相关论文
共 50 条
  • [41] Voltage Stability Index for Online Voltage Stability Assessment
    Maharjan, Rabindra
    Kamalasadan, Sukumar
    [J]. 2015 NORTH AMERICAN POWER SYMPOSIUM (NAPS), 2015,
  • [42] ICA based Artificial Neural Network model for Voltage Stability Monitoring
    Sajan, K. S.
    Kumar, Vishal
    Tyagi, Barjeev
    [J]. TENCON 2015 - 2015 IEEE REGION 10 CONFERENCE, 2015,
  • [43] Artificial Neural Network based Voltage Stability Analysis in Power System
    Subramani, C.
    Jimoh, A. A.
    Kiran, Harish S.
    Dash, Subhransu Sekhar
    [J]. PROCEEDINGS OF IEEE INTERNATIONAL CONFERENCE ON CIRCUIT, POWER AND COMPUTING TECHNOLOGIES (ICCPCT 2016), 2016,
  • [44] Monitoring and assessment of voltage stability margins using artificial neural networks with a reduced input set
    Popovic, D
    Kukolj, D
    Kulic, F
    [J]. IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 1998, 145 (04) : 355 - 362
  • [45] Probabilistic slope stability assessment of laterite borrow pit using artificial neural network
    Idris, Musa Adebayo
    [J]. INTERNATIONAL JOURNAL OF GEOTECHNICAL ENGINEERING, 2022, 16 (09) : 1152 - 1164
  • [46] Voltage stability estimation and prediction using neural network
    Belhadj, CA
    Al-Duwaish, H
    Shwehdi, MH
    Farag, AS
    [J]. POWERCON '98: 1998 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY - PROCEEDINGS, VOLS 1 AND 2, 1998, : 1464 - 1467
  • [47] A robust artificial neural network technique for dynamic stability assessment
    Dave, MP
    Chauhan, S
    [J]. ELECTRIC MACHINES AND POWER SYSTEMS, 1996, 24 (07): : 733 - 744
  • [48] Artificial Neural Network-Based Voltage Stability Online Monitoring Approach for Distributed Generation Integrated Distribution System
    Sundarajoo, Sharman
    Soomro, Dur Muhammad
    [J]. Distributed Generation and Alternative Energy Journal, 2023, 38 (06): : 1839 - 1862
  • [49] Online Estimation and Control of Voltage Flicker Using Neural Network
    Srivastava, S.
    Preeti, K. S.
    Sharma, Divy Jyoti
    Gupta, M.
    [J]. TENCON 2009 - 2009 IEEE REGION 10 CONFERENCE, VOLS 1-4, 2009, : 1580 - +
  • [50] Transient stability assessment using artificial neural networks
    Krishna, S
    Padiyar, KR
    [J]. PROCEEDINGS OF IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY 2000, VOLS 1 AND 2, 2000, : 627 - 632