摘要
为了进一步提高输电线路覆冰预测精度,提出一种基于改进哈里斯鹰算法(improved harris hawk optimiza-tion,IHHO)优化混合核极限学习机(hybrid kernel extreme learning machine,HKELM)的输电线路覆冰预测模型。在核极限学习机(KELM)中引入混合核函数,形成HKELM,利用黄金正弦、非线性递减能量指数和高斯随机游走等策略对IHHO算法进行改进;以IHHO算法的优化性能采用其对HKELM的权值向量和核参数进行优化,建立基于IHHO-HKELM的输电线路覆冰预测模型,并通过计算气象因素与覆冰厚度之间的灰色关联度确定覆冰预测模型的输入量。算例分析结果表明,IHHO-HKELM模型预测结果的均方误差、最大误差和平均相对误差分别为0.285、0.860 mm和2.83%,预测效果好于其他模型,将本文覆冰预测模型应用于其他覆冰线路,可获得良好的应用效果并验证模型的优越性和实用性。
To further improve the accuracy of transmission line icing prediction,a prediction model based on an improved Harris hawks optimization(IHHO)algorithm optimizing hybrid kernel extreme learning machine(HKELM)is proposed.The hybrid kernel function is introduced into the kernel extreme learning machine to form HKELM.The IHHO algorithm is improved by strategies such as golden sine,nonlinear decreasing inertia weight,and Gaussian random walk.The IHHO algorithm is then utilized to optimize the weight vector and kernel parameters of HKELM,establishing a transmission line icing prediction model based on IHHO-HKELM.The input variables of the icing prediction model are determined by calculating the grey relational grade between meteorological factors and icing thickness.The results of case studies show that the mean square error,maximum error,and average relative error of the IHHO-HKELM model are 0.285,0.860 mm,and 2.83%,respectively.The prediction effect is better than other models.Applying the icing prediction model in this paper to other icing lines can achieve good application effects and verify the superiority and practicality of the model.
作者
黄力
宋爽
刘闯
王骏骏
胡丹
何其新
鲁偎依
HUANG Li;SONG Shuang;LIU Chuang;WANG Junjun;HU Dan;HE Qixin;LU Weiyi(College of Electrical and New Energy,China Three Gorges University,Yichang 443002,China;Suizhou Power Supply Company,State Grid Hubei Electric Power Co.,Ltd.,Suizhou 441300,China;Jingmen Power Supply Company,State Grid Hubei Electric Power Co.,Ltd.,Jingmen 448000,China;Shiyan Power Supply Company,State Grid Hubei Electric Power Co.,Ltd.,Shiyan 442000,China;Jingzhou Power Supply Company,State Grid Hubei Electric Power Co.,Ltd.,Jingzhou 434000,China)
出处
《电力科学与技术学报》
CAS
CSCD
北大核心
2024年第4期33-41,共9页
Journal of Electric Power Science And Technology
基金
国家自然科学基金(61876097)
湖北省输电线路工程技术研究中心(三峡大学)开放基金(2019KXL05)。
关键词
输电线路
覆冰预测
核极限学习机
混合核函数
改进哈里斯鹰算法
transmission lines
icing prediction
kernel extreme learning machine
hybrid kernel function
improved Harris hawks optimization algorithm
