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

发布时间：2018-10-17 13:51

【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。
[Abstract]:In order to reduce the influence of icing accidents on transmission lines, accurate short-term prediction of the icing thickness of transmission lines will be able to effectively guide the anti-icing work of power grid. In view of the fact that the icing of transmission lines is a time accumulated process, a short-term prediction model of transmission line icing based on the mixture of time series analysis and Kalman filtering algorithm is proposed. The model makes use of the autocorrelation and time series of the lead ice amount to effectively reduce the influence of the existing ice prediction models on the accumulation of errors in various micrometeorological factors to the icing prediction results. Finally, the prediction model is verified by simulated icing test on the power system microclimate simulation platform, and the average absolute error of short-term prediction is 0.78. At the same time, by extracting the actual icing data from the on-line monitoring system of Guizhou Power Grid, it is verified that the average absolute error of short-term prediction of the prediction model is 2.58. This proves the validity of the model and can provide a reference for the deicing of transmission lines.
【作者单位】：西安工程大学电子信息学院;西安电子科技大学机电工程学院;
【基金】：国家自然科学基金(51177115) 陕西省重点科技创新团队(2014KCT-16)~~
【分类号】：TM752

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