基于PSO_SVM算法的输电线路覆冰舞动预测模型

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摘要输电线路舞动往往会导致金具磨损、闪络、断线等电力事故，对电力系统的安全具有很大的负面影响。利用ANSYS软件模拟不同档距、风速等状态下覆冰四分裂导线在平均风与脉动风作用下的动态响应，进而根据模拟获得的数据集和PSO_SVM（particle swarm optimization—support vector machines）算法构建了四分裂导线覆冰舞动预警模型，将档距、风速、初始风攻角作为模型的输入，覆冰导线是否舞动作为输出。同时，为验证该预测模型的实用性及有效性，将PSO_SVM模型与其他智能算法如BP(back propagation)、支持向量机（support vector machine，SVM）、遗传算法优化支持向量机（genetic algorithm—optimization support vector，GA_SVM）模型的预测结果进行比较，结果表明PSO_SVM模型的预测结果精度更高，对输电线路覆冰舞动预警具有一定的参考意义。

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	邹红波1
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	宋家乐1
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	刘媛2
	段治丰2
	张馨煜2
	宋璐2

关键词 ：粒子群优化算法, 神经网络, 支持向量机, 导线舞动

Abstract：The galloping of transmission lines often leads to electrical accidents such as wear of fittings, flash-over, and disconnection, which has a great negative impact on the safety of the power system. The dynamic response of the iced quad bundle conductor under the action of average wind and fluctuating wind was simulated by ANSYS software under the conditions of different spans, wind speeds, etc. Then, the early warning model was constructed based on the PSO_SVM（particle swarm optimization—support vector machines） algorithm and the data set obtained from the simulation, which takes the span, wind speed and initial wind angle of attack as the input, and whether galloping as the output. At the same time, the prediction results of the PSO_SVM model are compared with those of other intelligent algorithms such as BP(back propagation), SVM（support vector machine）, and GA_SVM（genetic algorithm—optimization support vector） models to verify the practicability and effectiveness of the prediction model. It is proved that the prediction results of the PSO_SVM model have higher accuracy, which has a certain reference significance for the early warning of the galloping of iced transmission lines.

Key words： particle swarm optimization neural network support vector machine（SVM） galloping of transmission lines

收稿日期: 2022-04-21 出版日期: 2023-02-15

引用本文:

邹红波1,2，宋家乐1,2，刘媛2，段治丰2，张馨煜2，宋璐2. 基于PSO_SVM算法的输电线路覆冰舞动预测模型[J]. 振动与冲击, 2023, 42(3): 280-286.
ZOU Hongbo1,2, SONG Jiale1,2, LIU Yuan2, DUAN Zhifeng2, ZHANG Xinyu2, SONG Lu2. Prediction model of galloping of iced transmission lines based on PSO-SVM algorithm. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(3): 280-286.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I3/280

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