输电线路PSOEMLSSVM覆冰预测模型
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通讯作者:

刘闯(1991),男,硕士,主要从事电力设备运行与维护的研究;Email:502290454@qq.com

中图分类号:

TM752

基金项目:

电网环境保护国家重点实验室开放基金(GYW51201700590)


PSOEMLSSVM forecasting model for the transmission lines icing
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    摘要:

    针对输电线路覆冰厚度预测方法存在的收敛速度慢、预测精度差等问题,考虑覆冰厚度影响因素,提出一种采用扩展记忆粒子群(PSOEM)进行参数寻优的方法,并将其应用到最小二乘支持向量机(LSSVM)中进行覆冰厚度预测。该方法在传统粒子群算法中引入扩展记忆因子,使粒子具有更强的搜索能力,从而加快收敛速度,提高预测精度。最后,采用实际线路覆冰数据对预测模型进行精度检验,结果表明,基于PSOEMLSSVM预测模型的平均相对误差均小于3%,与其他模型相比,预测效果最好。

    Abstract:

    According to the fact that the existing icing prediction methods has a slow convergence speed and poor prediction accuracy, a method based on particle swarm optimization with extended memory (PSOEM) is proposed under the consideration of the icing thickness influence to optimize parameters. It is applied to the least squares support vector machine (LSSVM) to predict icing thickness. The proposed method introduces an extended memory factor into the traditional particle swarm algorithm to make the particles have stronger search capabilities, thereby speeding up convergence and improving prediction accuracy. Finally, the actual line icing data is utilized to test the accuracy of the prediction model. It is shown that the average relative error of the prediction model based on PSOEMLSSVM is less than 3%. Compared with other models, the prediction effect is the best.

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刘闯,何沁鸿,卢银均,等.输电线路PSOEMLSSVM覆冰预测模型[J].电力科学与技术学报,2020,35(6):131-137.
LIU Chuang, HE Qinhong, LU Yinjun, et al. PSOEMLSSVM forecasting model for the transmission lines icing[J]. Journal of Electric Power Science and Technology,2020,35(6):131-137.

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  • 在线发布日期: 2021-04-16
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