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

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一種基于長短期記憶網(wǎng)絡(luò)的線路覆冰預(yù)測模型研究

來源:電工電氣發(fā)布時間:2020-03-27 13:27 瀏覽次數(shù):1087
一種基于長短期記憶網(wǎng)絡(luò)的線路覆冰預(yù)測模型研究
 
陳雨鴿1,高偉1,林鴻偉2,阮肇華2,鄭為湊2,林福2,陳錦植2
(1 福州大學(xué) 電氣工程與自動化學(xué)院,福建 福州 350108;
2 國網(wǎng)福建省電力有限公司寧德供電公司,福建 寧德 352100)
 
    摘 要:輸電線路覆冰災(zāi)害易引發(fā)危害電網(wǎng)安全運(yùn)行的事故,對輸電線路覆冰情況進(jìn)行短期預(yù)測十分必要。提出了一種基于結(jié)合氣象因素和導(dǎo)線覆冰量的時間序列模型預(yù)測法,建立一個由5 個氣象要素和一個導(dǎo)線覆冰量數(shù)據(jù)組成的數(shù)據(jù)集,采用長短期記憶網(wǎng)絡(luò)算法訓(xùn)練預(yù)測模型,利用線路實(shí)際運(yùn)行數(shù)據(jù)對模型進(jìn)行優(yōu)化和評估。實(shí)驗(yàn)結(jié)果表明,所提方法可準(zhǔn)確、有效地實(shí)現(xiàn)線路覆冰發(fā)展情況的預(yù)測,預(yù)測誤差僅4.2%。
    關(guān)鍵詞:輸電線路;覆冰預(yù)測;氣象信息;長短期記憶網(wǎng)絡(luò)
    中圖分類號:TM74     文獻(xiàn)標(biāo)識碼:A     文章編號:1007-3175(2020)03-0005-07
 
Study of an Icing Prediction Model for Transmission Line Based on Long and Short-Term Memory Network
 
CHEN Yu-ge1, GAO Wei1, LIN Hong-wei2, RUAN Zhao-hua2, ZHENG Wei-cou2, LIN Fu2, CHEN Jin-zhi2
(1 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China;
2 Ningde Power Supply Company of State Grid Fujian Electric Power Co., Ltd, Ningde 352100, China)
 
    Abstract: Ice disasters on power transmission lines can easily lead to accidents, endanger the safe operation of the power grid and bring economic losses. Therefore, it is important to make short-term predictions of icing conditions on transmission lines. A time series model prediction method combining meteorological factors and the amount of icing on the wire is proposed. First, a data set consisting of five meteorological factors and the amount of ice covered by the traverse is established. Secondly, the long and short-term memory network algorithm is used to train the prediction model. Finally, the model is optimized and evaluated using the actual operating data of the line. The experimental results show that the proposed method can accurately and effectively predict the development of icing on the line, and the prediction error is only 4.2%.
    Key words: transmission line; icing prediction; meteorological information; long and short-term memory network
 
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