基于改进RBFNN的1 000 kV特高压线损预测

doi:10.11930/j.issn.1004-9649.202103090

中国电力 ›› 2022, Vol. 55 ›› Issue (5): 122-127,142.DOI: 10.11930/j.issn.1004-9649.202103090

基于改进RBFNN的1 000 kV特高压线损预测

杨建华¹, 肖达强¹, 张伟², 余明琼¹, 易本顺²

1. 国家电网有限公司华中分部，湖北武汉 430077;
2. 武汉大学电子信息学院，湖北武汉 430072

收稿日期:2021-03-25 修回日期:2022-04-14 出版日期:2022-05-28 发布日期:2022-05-18
作者简介:杨建华（1966—），男，硕士，高级工程师（教授级），从事电力市场管理与研究，E-mail：227262318@qq.com;张伟（1997—），男，通信作者，硕士，从事人工智能和大数据研究，E-mail：2456152371@qq.com;易本顺（1965—），男，博士，教授，从事人工智能、大数据分析、电磁测量仪表等研究，E-mail：yibs@whu.edu.cn
基金资助:
国家电网有限公司科技项目（SGHZ0000 JYJS1900155）。

Prediction of 1 000 kV UHV Line Loss Based on Improved RBFNN

YANG Jianhua¹, XIAO Daqiang¹, ZHANG Wei², YU Mingqiong¹, YI Benshun²

1. Central China Branch of State Grid Corporation of China, Wuhan 430077, China;
2. Electronic Information School, Wuhan University, Wuhan 430072, China

Received:2021-03-25 Revised:2022-04-14 Online:2022-05-28 Published:2022-05-18
Supported by:
This work is supported by Science and Technology Project of SGCC (No.SGHZ0000JYJS1900155)

摘要/Abstract

摘要： 针对特高压输电线线损与特征参数间关系复杂的特点，提出一种联合聚类优化算法（Canopy -K- means）和自适应二次变异差分进化（adaptive second mutation differential evolution，ASMDE）算法改进的径向基神经网络（radial basis function neural network，RBFNN）模型，用于特高压输电线线损的预测。通过理论分析确定特高压输电线线损的特征参数，采用Canopy-K-means聚类算法进行聚类，以此确定径向基（radial basis function，RBF）神经网络的隐藏层节点，从而确保RBF神经网络具有较优的隐藏层中心。用特征参数和线损的样本数据训练ASMDE算法优化的RBF神经网络，拟合出线损与特征参数之间复杂的非线性关系。以华中地区某特高压输电线路的历史数据为例，仿真验证了所提方法的实用性和有效性。

关键词: 特高压, 线损, 径向基神经网络, Canopy-K-means算法

Abstract: In view of the complex relationship between UHV transmission line loss and its characteristic parameters, this paper proposes a radial basis function neural network (RBFNN) model improved by use of the Canopy-K-means clustering algorithm and the adaptive second mutation differential evolution (ASMDE) algorithm to predict the UHV transmission line loss. The characteristic parameters of UHV transmission line loss determined by theoretical analysis are clustered through Canopy-K-means clustering algorithm to determine the hidden layer nodes of radial basis function (RBF) neural network, subsequently ensuring the RBF neural network to have a better hidden layer center. The RBF neural network optimized by ASMDE algorithm is trained with the sample data of characteristic parameters and line loss, so as to fit the complex nonlinear relationship between line loss and characteristic parameters. Finally, the historical data of a UHV transmission line in Central China is taken for simulation, and the results have verified the practicability and effectiveness of the proposed method.

Key words: UHV, line loss, RBFNN, Canopy-K-means algorithm

杨建华, 肖达强, 张伟, 余明琼, 易本顺. 基于改进RBFNN的1 000 kV特高压线损预测[J]. 中国电力, 2022, 55(5): 122-127,142.

YANG Jianhua, XIAO Daqiang, ZHANG Wei, YU Mingqiong, YI Benshun. Prediction of 1 000 kV UHV Line Loss Based on Improved RBFNN[J]. Electric Power, 2022, 55(5): 122-127,142.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202103090

https://www.electricpower.com.cn/CN/Y2022/V55/I5/122

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基于改进RBFNN的1 000 kV特高压线损预测

Prediction of 1 000 kV UHV Line Loss Based on Improved RBFNN

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基于改进RBFNN的1 000 kV特高压线损预测

Prediction of 1 000 kV UHV Line Loss Based on Improved RBFNN

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