Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

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基于PSO-RBF的輸電線路覆冰預(yù)測研究

來源:電工電氣發(fā)布時間:2018-06-15 14:15 瀏覽次數(shù):677
基于PSO-RBF的輸電線路覆冰預(yù)測研究
 
焦晗,黃陳蓉,李焱飛
(南京工程學(xué)院 電力工程學(xué)院,江蘇 南京 211167)
 
    摘 要:覆冰后的架空輸電線路在風(fēng)載荷的作用下,容易產(chǎn)生導(dǎo)線舞動現(xiàn)象,嚴(yán)重危害輸電線路安全。提出一種基于PSO-RBF的神經(jīng)網(wǎng)絡(luò)模型對輸電線路的覆冰情況進(jìn)行預(yù)測,對微氣象參數(shù)影響因子進(jìn)行排序,選取合適的微氣象因素作為模型的輸入,降低建模輸入的維度,并通過粒子群算法對RBF神經(jīng)網(wǎng)絡(luò)參數(shù)進(jìn)行優(yōu)化,與單一的RBF神經(jīng)網(wǎng)絡(luò)相比提高了預(yù)測精度,能及時了解導(dǎo)線覆冰的趨勢并給出預(yù)警,有效防止嚴(yán)重覆冰事故的發(fā)生。
    關(guān)鍵詞:架空輸電線路;覆冰預(yù)測;微氣象;神經(jīng)網(wǎng)絡(luò)
    中圖分類號:TM726     文獻(xiàn)標(biāo)識碼:A     文章編號:1007-3175(2018)06-0033-04
 
Prediction Research on Transmission Line Icing Based on Particle Swarm Optimization-Radial Basis Function
 
JIAO Han, HUANG Chen-rong, LI Yan-fei
(School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)
 
    Abstract: The icing overhead transmission lines under the action of wind load are easy to generate the galloping phenomenon, which is seriously harmful to the safe operation of transmission lines. This paper proposed a kind of neural network model based on particle swarm optimization-radial basis function (PSO-RBF) to carry out prediction for the icing condition of transmission lines, to sort the micro meteorological parameters, to select the suitable micro meteorological parameters as the input of model and to reduce the dimensions of modeling input. The algorithm of PSO was used to optimize the RBF neural network and compared with the single RBF neural network, its prediction accuracy was improved, which makes the icing trend of transmission lines known in time with warning to effectively prevent serious icing accidents.
    Key words: overhead transmission line; icing prediction; microclimate; neural network
 
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