Application of artificial neural networks for prediction of voltage instability

被引:0
|
作者
Vallabhan, SC [1 ]
Jeyasurya, B [1 ]
机构
[1] MEM UNIV NEWFOUNDLAND, FAC ENGN & APPL SCI, St John, NF A1B 3X5, CANADA
来源
ELECTRIC MACHINES AND POWER SYSTEMS | 1997年 / 25卷 / 02期
基金
加拿大自然科学与工程研究理事会;
关键词
D O I
10.1080/07313569708955734
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 investigates the application of artificial neural networks for prediction of voltage instability. A voltage stability index based on an energy margin method is used for designing: the artificial neural network. Contingencies are also taken into account while computing the voltage stability index and for training the neural network. The effects of learning rate and training tolerance on the neural network performance are studied. Tests results based on the IEEE 24 bus Reliability test system are presented.
引用
收藏
页码:215 / 226
页数:12
相关论文
共 50 条
  • [21] Application of artificial neural networks to the dynamic analysis of the voltage stability problem
    Schmidt, HP
    IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 1997, 144 (04) : 371 - 376
  • [22] Voltage and Overpotential Prediction of Vanadium Redox Flow Batteries with Artificial Neural Networks
    Martinez-Lopez, Joseba
    Portal-Porras, Koldo
    Fernandez-Gamiz, Unai
    Sanchez-Diez, Eduardo
    Olarte, Javier
    Jonsson, Isak
    BATTERIES-BASEL, 2024, 10 (01):
  • [23] On the Prediction Instability of Graph Neural Networks
    Klabunde, Max
    Lemmerich, Florian
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT III, 2023, 13715 : 187 - 202
  • [24] Application of an Artificial Neural Networks Model for Prediction of Instability Phenomena in Low Current Vacuum Arc by Cathode Spot Model
    Thungsuk, Nuttee
    Phumemek, Phanudet
    Chaithanakulwat, Arckarakit
    Savangboon, Teerawut
    Tanram, Thaweesak
    Tati, Sunun
    Mungkung, Narong
    Arunrungrusmi, Somchai
    Tanitteerapan, Tanes
    Tunlasakun, Khanchai
    Yuji, Toshifumi
    IEEE TRANSACTIONS ON PLASMA SCIENCE, 2024, 52 (04) : 1207 - 1217
  • [25] Application of artificial neural networks for classification and prediction of air quality classes
    Skrzypski, J.
    Kaminski, K.
    Jach-Szakiel, E.
    Kaminski, W.
    MANAGEMENT OF NATURAL RESOURCES, SUSTAINABLE DEVELOPMENT AND ECOLOGICAL HAZARDS II, 2010, 127 : 219 - 228
  • [26] The Application of Artificial Neural Networks for the Prediction of Oil Production Flow Rate
    Mirzaei-Paiaman, A.
    Salavati, S.
    ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2012, 34 (19) : 1834 - 1843
  • [27] Application of artificial neural networks to the prediction of minor axis steel connections
    Anderson, D
    Hines, EL
    Arthur, SJ
    Eiap, EL
    COMPUTERS & STRUCTURES, 1997, 63 (04) : 685 - 692
  • [28] The Application of Artificial Neural Networks for the Prediction of Water Quality of Polluted Aquifer
    F. Gümrah
    B. Öz
    B. Güler
    S. Evin
    Water, Air, and Soil Pollution, 2000, 119 : 275 - 294
  • [29] Application of Artificial Neural Networks on growth prediction of Staphylococcus aureus in milk
    Orawan, C.
    Panwadee, S.
    Bandit, S.
    INTERNATIONAL FOOD RESEARCH JOURNAL, 2016, 23 (01): : 415 - 418
  • [30] Application Of Artificial Neural Networks For Path Loss Prediction In Railway Environments
    Wu, Di
    Zhu, Gang
    Ai, Bo
    2010 5TH INTERNATIONAL ICST CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA (CHINACOM), 2010,