| 引用本文: | 陈勇,李鹏,张忠军,聂海福,沈鑫.基于PCA-GA-LSSVM的输电线路覆冰负荷在线预测模型[J].电力系统保护与控制,2019,47(10):110-119.[点击复制] |
| CHEN Yong,LI Peng,ZHANG Zhongjun,NIE Haifu,SHEN Xin.Online prediction model for power transmission line icing load based on PCA-GA-LSSVM[J].Power System Protection and Control,2019,47(10):110-119[点击复制] |
|
| 摘要: |
| 针对目前输电线路覆冰负荷预测模型存在的预测精度不足、模型参数选择随意性强、预测效率低等问题，提出了一种基于现场监测数据的输电线路覆冰负荷在线预测模型。首先基于主成分分析法(Principal Component Analysis, PCA)提取微气象数据中的有效信息，并采用遗传优化算法(Genetic Algorithm, GA)对惩罚系数等模型参数进行优化确定，建立离线最小二乘支持向量机(Least Squares Support Vector Machines, LS-SVM)模型。然后基于KKT条件(Karush-Kuhn-Tucker conditions)和增量在线学习算法，实现了回归函数和预测模型的在线更新。最后通过云南电网相关输电线路覆冰灾害的实例进行仿真分析。实验结果表明所提模型可有效地对现场输电线路覆冰负荷进行在线预测，单步长及多步长的预测效果均优于传统的覆冰预测模型，应用该预测模型可更好地为输变电系统的除冰和维护决策服务。 |
| 关键词: 输电线路 最小二乘支持向量机 覆冰预警 主成分分析 在线预测 |
| DOI：10.7667/PSPC20191015 |
| 投稿时间：2018-06-01修订日期：2018-08-11 |
| 基金项目:国家自然科学基金项目资助(61763049);云南省应用基础研究计划重点项目资助(2018FA032) |
|
| Online prediction model for power transmission line icing load based on PCA-GA-LSSVM |
| CHEN Yong,LI Peng,ZHANG Zhongjun,NIE Haifu,SHEN Xin |
| (School of Information, Yunnan University, Kunming 650500, China;Electric Power Research Institute, Yunnan Power Grid Corp, Kunming 650217, China) |
| Abstract: |
| Traditional icing load prediction models exist many shortcomings, such as forecasting inaccuracy, casualness in choosing model parameters, and low prediction efficiency. Thus, an online prediction model based on the field micrometeorological data is proposed to predict the icing load of power transmission line. Firstly, this paper extracts effective information from micrometeorological data based on Principal Component Analysis (PCA), and optimizes the regression parameters by Genetic Algorithm (GA), and builds and trains offline LS-SVM training model. Secondly, online updating of regression function and prediction model is realized based on Karush-Kuhn-Tucker conditions and incremental online learning algorithm. Finally, the validity of the model is evaluated by related transmission lines of Yunnan Power Grid. Experimental results indicate that this method could predict the real-time icing load on overhead power lines, obtaining better performance in single-step and multi-step forecast than traditional icing load prediction models , which could serve for deicing and maintenance decision for power transmission and distribution system. This work is supported by National Natural Science Foundation of China (No. 61763049) and Science and Technology Plan of Applied Basic Research Programs Key Foundation of Yunnan Province (No. 2018FA032). |
| Key words: transmission line least squares support vector machines icing alarming principal component analysis online prediction |
