摘要
为降低输电线路覆冰事故的影响,对输电线路覆冰厚度进行准确的短期预测将能够有效地指导电网抗冰工作。为此从输电线路覆冰是一种时间累积过程的角度出发,提出了一种基于时间序列分析与卡尔曼滤波算法混合的线路覆冰短期预测模型。模型利用导线覆冰量的时间数据序列所具有的自相关性和时序性,有效减少了现有覆冰预测模型由于测量到的各种微气象因素存在的误差累积到覆冰预测结果中的影响。最后,通过搭建的电力系统微气候模拟平台进行模拟覆冰试验来对预测模型进行了验证,其短期预测平均绝对误差为0.78%。同时,通过从贵州电网在线监测系统上提取实际覆冰数据,验证了预测模型的短期预测平均绝对误差为2.58%。这证实了该模型的有效性,能够为输电线路除冰工作提供参考。
In order to reduce the effects of the transmission lines icing accident, we can guide the ice-resistant efforts effectively if the icing thickness on the transmission lines can be predicted. Therefore, we proposed a new transmission line icing short-term forecast model based on time series analysis and Kalman filtering. By using the relevance and scheduling of transmission lines icing time data sequence, the forecast model in this article could reduce the influence of the micro meteorological conditions＇ measurement errors on the existing icing forecast model results effectively. Finally, through constructing the micro climate simulation platform for power system and simulating the transmission lines icing to test the forecast model, the result shows that the short-term forecasting average absolute error of the model is 0.78%. At the same time, we used the actual extraction icing data from the on-line monitoring system of Guizhou Power Grid to test the model, the short-term forecasting average absolute error of the model was 2.58%. The validity of the forecast model in this article is confirmed and the model can be able to provide the reference for transmission lines deicing.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2017年第6期1943-1949,共7页
High Voltage Engineering
基金
国家自然科学基金(51177115)
陕西省重点科技创新团队(2014KCT-16)~~
关键词
输电线路
覆冰预测
时间序列
卡尔曼滤波
遗传算法
transmission line
icing forecasting
time series
Kalman filtering
genetic algorithm
