Research on prediction of tower mechanical response in wind field based on multi-layer perceptron

被引:0
|
作者
Mo, Wenxiong [1 ]
Fan, Weinan [1 ]
Liu, Junxiang [1 ]
Luan, Le [1 ]
Xu, Zhong [1 ]
Zhou, Kai [1 ]
机构
[1] Guangzhou Power Supply Bur, Guangdong Power Grid, Guangzhou 510000, Peoples R China
关键词
Machine learning; Tower damage; Multilayer perceptron;
D O I
10.1109/ICBASE53849.2021.00059
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
There are often strong winds and typhoons in coastal areas, which brings great risks to the safe operation of transmission lines. The study of a fast calculation method for the response of transmission lines in wind farm is of great significance to ensure the safe operation of power grid. Most of the existing research methods are simulation modeling and statistical calculation, but there are still deficiencies in computing speed and universality. In this paper, the response of transmission tower in wind field is studied. Using the principle of machine learning, the tower response model under different weather and tower operation conditions is built, and the MLP multi-layer perceptron model is used to predict the tower stress value in wind field; Smote linear interpolation is used to increase the correctness of the number of unbalanced sample data. Finally, the prediction accuracy of minority samples is improved by modifying the loss function. The prediction accuracy of minority samples is significantly improved, and the accuracy is 96.5%.
引用
收藏
页码:285 / 289
页数:5
相关论文
共 50 条
  • [11] An Extension of Multi-Layer Perceptron Based on Layer-Topology
    Zuters, Janis
    PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 7, 2005, 7 : 178 - 181
  • [12] Prediction of hospital re-admission using firefly based multi-layer perceptron
    Battula B.P.
    Balaganesh D.
    Ingenierie des Systemes d'Information, 2020, 25 (04): : 527 - 533
  • [13] MULTI-LAYER PERCEPTRON BASED TRANSFER PASSENGER FLOW PREDICTION IN ISTANBUL TRANSPORTATION SYSTEM
    Utku A.
    Kaya S.K.
    Decision Making: Applications in Management and Engineering, 2022, 5 (01): : 208 - 224
  • [14] Multi-layer Perceptron Based Video Surveillance System
    Harihar, Vijai Kumar
    Sukumaran, Sandeep
    Sirajuddin, Samar
    Sali, Aswathy
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC), 2017, : 490 - 495
  • [15] Seismic data denoising based on multi-layer perceptron
    Wang Q.
    Tang J.
    Zhang L.
    Liu X.
    Xu Z.
    Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting, 2020, 55 (02): : 272 - 281
  • [16] Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network
    Sun, W. Z.
    Jiang, M. Y.
    Ren, L.
    Dang, J.
    You, T.
    Yin, F-F
    PHYSICS IN MEDICINE AND BIOLOGY, 2017, 62 (17): : 6822 - 6835
  • [17] A Multi-layer Perceptron-based Approach for Prediction of the Crude Oil Pyrolysis Process
    Rasouli, A. R.
    Dabiri, A.
    Nezamabadi-pour, H.
    ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2015, 37 (13) : 1464 - 1472
  • [18] Multi-layer perceptron based modelling of nonlinear systems
    Lightbody, G
    Irwin, GW
    FUZZY SETS AND SYSTEMS, 1996, 79 (01) : 93 - 112
  • [19] Prediction of credit delinquents using locally transductive multi-layer perceptron
    Heo, Hyunjin
    Park, Hyejin
    Kim, Namhyoung
    Lee, Jaewook
    NEUROCOMPUTING, 2009, 73 (1-3) : 169 - 175
  • [20] Probabilistic fatigue life prediction using multi-layer perceptron with maximum
    Zhu, Yifeng
    Hu, Zican
    Luo, Jiaxiang
    Song, Peilong
    INTERNATIONAL JOURNAL OF FATIGUE, 2024, 187