基于GA-SVM的高压输电线路弧垂预测模型
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摘要
导线弧垂是反映输电线路运行状态的重要参数之一,为了预知和预警高压输电线路弧垂的变化,提出了一种基于遗传算法(genetic algorithm, GA)特征自适应赋权的支持向量机(support vector machine, SVM),预测输电线路弧垂的方法(GA-SVM).该方法主要分为两个阶段,首先使用GA对实验数据自适应赋权,以突出重要属性,抑制冗余或次要属性,然后使用SVM预测输电线路弧垂.实验结果表明,该方法在预测输电线路弧垂方面是可行有效的,并且优于贝叶斯(Bayes)算法、K-最近邻算法(KNN)、决策树算法和BPNN神经网络算法.
The wire sag was one of the important parameters of transmission lines. In order to predict the line sag variation, a support vector machine model based on genetic algorithm(GA-SVM) was proposed. The method was divided into two stages. Firstly, GA algorithm was applied to adaptively weight the features to highlight the important attributes and suppress the redundant or secondary attributes. Then, SVM algorithm was used to predict the line sag. The empirical analysis showed that the proposed method was feasible and effective. It was superior to Bayes algorithm, K-Nearest Neighbor algorithm(KNN), Decision Tree algorithm, and BPNN algorithm.
The wire sag was one of the important parameters of transmission lines. In order to predict the line sag variation, a support vector machine model based on genetic algorithm(GA-SVM) was proposed. The method was divided into two stages. Firstly, GA algorithm was applied to adaptively weight the features to highlight the important attributes and suppress the redundant or secondary attributes. Then, SVM algorithm was used to predict the line sag. The empirical analysis showed that the proposed method was feasible and effective. It was superior to Bayes algorithm, K-Nearest Neighbor algorithm(KNN), Decision Tree algorithm, and BPNN algorithm.
引文
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[2] 王孔森,孙旭日,盛戈皞,等. 架空输电线路导线弧垂在线监测误差分析及比较方法[J].高压电网,2014,50(4):27-34.
[3] 王礼田,邵凤莹,萧宝瑾. 基于双目视觉稀疏点云重建的输电线路弧垂测量方法[J].太原理工大学学报, 2016,47(6):747-751.
[4] 董晓虎,易东. 基于北斗卫星差分定位技术的输电线路弧垂监测[J].电子设计工程,2015,23(19):41-42.
[5] 陈思明,闫斌,周小佳,等. 基于倾角的输电线路弧垂三维模型算法研究[J].计算机应用与软件, 2014,31(3):82-84.
[6] 姚陈果,张磊,李成祥,等. 基于力学分析和弧垂测量的导线覆冰厚度测量方法[J].高电压技术, 2013,39(5):1204-1209.
[7] 张江华. 高压输电线路弧垂在线监测研究[D].武汉:华中科技大学,2012.
[8] SUGANYADEVI M V, BABULAL C K. Support vector regression model for the prediction of loadability margin of a power system[J]. Applied soft computing, 2014,24:304-315.
[9] WANG J Z, HOU R, WANG C,et al. Improved v-support vector regression model based on variable selection and brain storm optimization for stock price forecasting[J]. Applied soft computing, 2016,49:164-178.
[10] 靳江伟, 董春芳, 冯国红. 基于灰色关联支持向量机的混凝土抗压强度预测[J].郑州大学学报(理学版), 2015,47(3):59-63.
[11] 尹立. 基于支持向量机的某区域电网电力需求的预测研究[D].北京:北京交通大学, 2014.
[12] WAUTERS M, VANHOUCKE M. Support vector machine regression for project control forecasting [J]. Automation in constuction, 2014,47:92-106.
[13] MALHARA S, VITTAL V. Mechanical state estimation of overhead transmission lines using tilt sensors[J]. IEEE transactions on power systems, 2010,25(3):1282-1290.
[14] DIALAMEH M, JAHROMI M Z. A general feature-weighting function for classification problems[J].Expert systems with applications, 2017,72:177-188.
[15] 姬波. 信息瓶颈方法的特征权重研究[D].郑州:郑州大学, 2013.
[16] PHAN A V, NGUYEN M L, BUI L T. Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems[J]. Applied intelligence, 2017, 46(2):455-469.
[17] PENG L Z, ZHANG H L, ZHANG H B,et al. A fast feature weighting algorithm of data gravitation classification[J]. Information sciences, 2017, 375:54-78.
[18] 田晋跃, 王晨阳, 李得志. 基于遗传算法的某工程车辆起步特性研究[J].郑州大学学报(理学版), 2016, 48(2):121-126.