时间累积架空输电线路覆冰预测模型与算法综述

doi:10.11930/j.issn.1004-9649.202309061

摘要/Abstract

摘要：

在覆冰条件的气象因素下，输电导线随时间累积形成的覆冰厚度、形状和分布变化，影响着电网系统的安全运行。按预测模型从覆冰生长到导线除冰各阶段存在的关联分析，讨论了各模型的优势差异以及相互存在组合的可能性。覆冰全周期存在着由微观到宏观的变化，影响着导线覆冰的生长进程。预测模型可按全周期组合，首先，对初始数据的降噪可解决数据发散，用主成分分析法的降维可提高预测精度。其次，模型中的支持向量机、混合的群智能优化算法、遗传算法组合等工具乃至交叉方式，都着力于覆冰过程的辨识与建模。再次，将热力融冰技术的负荷交流或涡流自热环应用在除冰阶段，使覆冰监测形成了动态闭环系统。最后，结合分析对输电线路覆冰预测的研究方向做了展望。

关键词: 时间累积, 气象因素, 架空输电线路, 覆冰, 预测模型, 算法

Abstract:

Under the meteorological factors of icing, the changes of icing thickness, shape and distribution on transmission conductor with time cumulation affect the safety operation of power grid system. Based on prediction models, this paper analyzes the association between various stages from icing growth to conductor deicing, and discusses the advantages of models and the possibility of mutual combination. The changes of icing from micro to macro in the whole icing circle affect the growth process of conductor icing. The prediction models can be combined based on the whole cycle. Firstly, the initial data is denoised to solve the data divergence, and the dimensionality reduction by the principal component analysis method can improve the prediction accuracy. Secondly, the combination and intercrossing mode of support vector machine, hybrid swarm intelligence optimization algorithm, genetic algorithm in the model focus on the identification and modeling of icing process. Thirdly, the application of thermal deicing techniques such as AC ice melting and eddy self-heating ring in the de-icing stage helps to form a dynamic closed-loop system for icing monitoring. Finally, an outlook is made on the research direction of icing prediction for transmission lines.

Key words: time cumulation, meteorological factor, overhead transmission line, icing, prediction model, algorithm

王传琦, 伍历文, 邓志斌, 邓伟锋, 杨彬. 时间累积架空输电线路覆冰预测模型与算法综述[J]. 中国电力, 2024, 57(6): 153-164, 234.

Chuanqi WANG, Liwen WU, Zhibin DENG, Weifeng DENG, Bin YANG. Review of Icing Prediction Model and Algorithm for Overhead Transmission Lines Considering Time Cumulative Effects[J]. Electric Power, 2024, 57(6): 153-164, 234.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202309061

https://www.electricpower.com.cn/CN/Y2024/V57/I6/153

图/表 5

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序号	模型的工具	模型的特点
1	基于高斯分布模型	数据挖掘，Matlab拟合分布曲线优化数据
2	基于支持向量机模型	以高维到低维的特征映射形成全新的正交特征
3	支持向量机覆冰预测模型	优化的BP神经网络模型，考虑气象因素的时间效应
4	非解析型的覆冰预测模型	基于支持向量机，多项式回归模型、时间序列模型和机器学习模型
5	线路覆冰预测ACO-PSO混合群算法模型	加权支持向量机回归，粒子群与蚁群混合的群智能优化算法
6	基于气象因素累积效应模型	SVM回归方法，以历史气象数据和覆冰厚度监测值组成训练集
7	基于数据挖掘和训练模型	SVM为训练回归模型，气象历史数据分类集合、信息挖掘
8	数据驱动算法和 LS-SVM预测模型	k均值邻近算法、聚类分析、小样本拟合
9	智能组合模型	采用快速独立分量分析、集成经验模态分解、支持向量机、遗传算法和Tabu搜索
10	遗传算法覆冰预测模型	优化BP的遗传算法-BP神经网络
11	时间数据序列与卡尔曼滤波算法模型	遗传算法、卡尔曼预测迭代方程、线性随机微分方程
12	回归函数和预测模型	主成分分析法、遗传算法，最优化条件和增量在线学习算法
13	遗传算法与模糊逻辑融合预测模型	组合模糊规则库、遗传算法
14	基于特征选择和混沌灰狼优化极值学习机预测模型	集成经验模态分解、随机森林和混沌灰狼优化极值学习机算法

序号	模型的工具	模型的特点
1	基于高斯分布模型	数据挖掘，Matlab拟合分布曲线优化数据
2	基于支持向量机模型	以高维到低维的特征映射形成全新的正交特征
3	支持向量机覆冰预测模型	优化的BP神经网络模型，考虑气象因素的时间效应
4	非解析型的覆冰预测模型	基于支持向量机，多项式回归模型、时间序列模型和机器学习模型
5	线路覆冰预测ACO-PSO混合群算法模型	加权支持向量机回归，粒子群与蚁群混合的群智能优化算法
6	基于气象因素累积效应模型	SVM回归方法，以历史气象数据和覆冰厚度监测值组成训练集
7	基于数据挖掘和训练模型	SVM为训练回归模型，气象历史数据分类集合、信息挖掘
8	数据驱动算法和 LS-SVM预测模型	k均值邻近算法、聚类分析、小样本拟合
9	智能组合模型	采用快速独立分量分析、集成经验模态分解、支持向量机、遗传算法和Tabu搜索
10	遗传算法覆冰预测模型	优化BP的遗传算法-BP神经网络
11	时间数据序列与卡尔曼滤波算法模型	遗传算法、卡尔曼预测迭代方程、线性随机微分方程
12	回归函数和预测模型	主成分分析法、遗传算法，最优化条件和增量在线学习算法
13	遗传算法与模糊逻辑融合预测模型	组合模糊规则库、遗传算法
14	基于特征选择和混沌灰狼优化极值学习机预测模型	集成经验模态分解、随机森林和混沌灰狼优化极值学习机算法

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