Short-term icing forecasting based on phase space reconstruction wavelet neural network

YANG Jing; LI Yingna; ZHAO Zhengang; LI Chuan

doi:10.13873/J.1000-9787(2019)09-0108-04

Chinese

您当前的位置：

首页 >

文章列表页 >

Short-term icing forecasting based on phase space reconstruction wavelet neural network

更新时间：2025-09-11

- Short-term icing forecasting based on phase space reconstruction wavelet neural network
- Vol. 38, Issue 9, Pages: 108-111(2019)
- 作者机构：
  
  昆明理工大学信息工程与自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2019)09-0108-04
  CLC： TM752;TP183
- Published：2019
- 稿件说明：
移动端阅览
[1]杨静,李英娜,赵振刚,李川.基于相空间重构小波神经网络的短期覆冰预测[J].传感器与微系统,2019,38(09):108-111.

YANG Jing, LI Yingna, ZHAO Zhengang, et al. Short-term icing forecasting based on phase space reconstruction wavelet neural network[J]. 2019, 38(9): 108-111.
[1]杨静,李英娜,赵振刚,李川.基于相空间重构小波神经网络的短期覆冰预测[J].传感器与微系统,2019,38(09):108-111. DOI： 10.13873/J.1000-9787(2019)09-0108-04.

YANG Jing, LI Yingna, ZHAO Zhengang, et al. Short-term icing forecasting based on phase space reconstruction wavelet neural network[J]. 2019, 38(9): 108-111. DOI： 10.13873/J.1000-9787(2019)09-0108-04.

摘要

针对输电线路覆冰问题

提出一种基于相空间重构小波神经网络的短期覆冰预测模型。以云南昭通某110 kV输电线路覆冰厚度监测数据为基础

首先求取覆冰时间序列最大李雅普诺夫（Lyapunov）指数

对覆冰厚度进行非线性的动力学分析

确定序列具有混沌特性;其次由C-C法对系统进行相空间重构

找出数据变化的局部规律性;最后采用小波神经网络对相空间轨迹进行预测

并通过蚁群算法优化预测模型参数。实验表明:模型具有良好预测能力

对抗冰工作具有实际指导意义。

Abstract

Aiming at the problem of ice coating on transmission lines

a short-term icing prediction model based on phase space reconstruction wavelet neural network is proposed. Based on the ice thickness monitoring data of a110 kV transmission line in Zhaotong

Yunnan

the Lyapunov index of the ice-covered time series is obtained first

and the nonlinear dynamics analysis of the ice-covered thickness is carried out to determine the chaotic characteristics of the sequence. The phase space reconstruction of the system is carried out by the C-C method to find the local regularity of the data change. Finally

the wavelet neural network is used to predict the phase space trajectory

and the ant colony algorithm is used to optimize the prediction model parameters. Experiments show that the model has good predictive ability and has practical guiding significance for the work against icing.

关键词

Keywords

references

基于混沌-RBF神经网络的光伏发电功率超短期预测模型 [J]. 王育飞,付玉超,孙路,薛花. 电网技术 . 2018(04)

基于微气象的输电线路覆冰过程综合辨识方法 [J]. 檀磊,苗爱敏,李鹏,施心陵,邹勋,蒋建波,吴海龙. 云南大学学报(自然科学版) . 2017(06)

基于时间序列分析与卡尔曼滤波的输电线路覆冰短期预测 [J]. 黄新波,李弘博,朱永灿,王玉鑫,郑心心,王一各. 高电压技术 . 2017(06)

基于短期覆冰预测的电网覆冰灾害风险评估方法 [J]. 晏鸣宇,周志宇,文劲宇,郭创新,陆佳政,姚伟. 电力系统自动化 . 2016(21)

基于改进Messinger覆冰模型导线防冰临界电流计算及其影响因素分析 [J]. 刘国特,郝艳捧,阳林,陈彦,钟荣富. 电工技术学报 . 2016(18)

基于遗传算法与模糊逻辑融合的线路覆冰预测 [J]. 黄新波,王玉鑫,朱永灿,郑心心,李弘博,王一各. 高电压技术 . 2016(04)

输电线路覆冰厚度短期多变量灰色预测模型研究 [J]. 刘宏伟,陆佳政,赖旬阳,谭艳军,徐勋建,王银顺. 高电压技术 . 2015(10)

实测海杂波光电图像混沌特性研究 [J]. 李正周,丁浩,刘梅,田蕾,金钢. 传感器与微系统 . 2013(04)

Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model [J] . Guo-Feng Fan,Li-Ling Peng,Wei-Chiang Hong. Applied Energy . 2018

A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm [J] . Rui Hu,Shiping Wen,Zhigang Zeng,Tingwen Huang. Neurocomputing . 2017

Support Vector Machines in Urban Water Demand Forecasting Using Phase Space Reconstruction [J] . Sina Shabani,Peyman Yousefi,Gholamreza Naser. Procedia Engineering . 2017

Views

182

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

USV course control by ant colony optimization PID with mutation characteristics

Novel handheld detection system of power frequency electric field based on high speed demodulation circuit

Application of wireless sensor networks in power system

Related Author

YUAN Guangming

FENG Guoqiang

DING Chengjun

FENG Yubo

TANG Li-jun

GU Zhi-bin

PENG Chun-rong

LI Wei

Related Institution

School of Mechanical Engineering,Hebei University of Technology

Electric Power Research Institute,Yunnan Power Grid Corporation

State Key Laboratory of Transducer Technology,Institute of Electronics,Chinese Academy of Sciences

University of Chinese Academy of Sciences

Department of Electronics and Communication Engineering,North China Electric Power University

⁰