| 引用本文: | 王 兰,李华强,吴 星,王羽佳.基于改进局域Volterra自适应滤波器的风电功率混沌时间序列预测模型[J].电力自动化设备,2016,36(8): |
| WANG Lan,LI Huaqiang,WU Xing,WANG Yujia.Wind power chaotic time series prediction model based on improved local Volterra adaptive filter[J].Electric Power Automation Equipment,2016,36(8): |
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| 摘要: |
| 针对风电功率混沌序列的特点，提出一种基于改进局域Volterra自适应滤波器的风电功率混沌时间序列预测模型。首先，针对邻近点及其坐标分量在时间上与预测点距离不同、对预测点的影响不同的特点，提出一种考虑时间影响并结合距离与演化趋势的综合判据；然后，对使用综合判据筛选出的相点建立改进局域Volterra自适应滤波器模型；最后，对我国某风电场的采集数据进行建模仿真。结果表明所提的改进模型具有较好的计算速度和较高的精度。 |
| 关键词: 风电 预测 短期预测 邻近点 局域Volterra自适应滤波器 混沌时间序列 模型 |
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| Wind power chaotic time series prediction model based on improved local Volterra adaptive filter |
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WANG Lan, LI Huaqiang, WU Xing, WANG Yujia
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Intelligent Electric Power Grid Key Laboratory of Sichuan Province，School of Electrical Engineering and Information，Sichuan University，Chengdu 610065，China
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| Abstract: |
| According to the features of wind power chaotic series，a wind power chaotic time series pre-diction model based on the improved local Volterra adaptive filter is proposed. Since different neighboring points together with their coordinate components have different time distances from the prediction point and have different influences on the prediction point，an integrated criterion considering the time influence and combining with the distance and the evolution trend is proposed for selecting the correlative neighboring points，which are then used to build the improved local Volterra adaptive filter model. The actual data of a wind farm are applied to build the prediction model for simulation and the simulative results show that，the proposed prediction model has faster speed and better accuracy. |
| Key words: wind power prediction short-term prediction neighboring points local Volterra adaptive filter chaotic time series models |
