重庆理工大学学报（自然科学）

重庆理工大学学报（自然科学） ›› 2023, Vol. 37 ›› Issue (2): 241-250.doi: 10.3969/j.issn.1674-8425(z).2023.02.027

• 电气·电子 • 上一篇    下一篇

基于改进核极限学习机的风电功率短期预测

黄文聪，潘 风，杨子潇   

  1. 湖北工业大学 太阳能高效利用及储能运行控制湖北省重点实验室，武汉 ４３００６
  • 出版日期:2023-03-21 发布日期:2023-03-21
  • 作者简介:黄文聪，男，博士，副教授，主要从事电力电子与电力传动、控制理论与控制工程和可再生能源预测研究，Ｅｍａｉｌ： １５８２７４７６３６３＠１６３．ｃｏｍ。

Short-term prediction of wind power based on improved kernel extreme learning machines

  • Online:2023-03-21 Published:2023-03-21

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摘要： ：针对环境变化造成风力发电功率波动大和核极限学习机易陷入局部最优解的问 题，构建了一种基于完全噪声辅助聚合经验模态分解（ｃｏｍｐｌｅｔｅｅｎｓｅｍｂｌｅｅｍｐｉｒｉｃａｌｍｏｄｅｄｅｃｏｍ ｐｏｓｉｔｉｏｎｗｉｔｈａｄａｐｔｉｖｅｎｏｉｓｅａｎａｌｙｓｉｓ，ＣＥＥＭＤＡＮ）、小波阈值去噪和粒子群算法优化核极限学习机 的风电功率短期预测模型。首先，利用 ＣＥＥＭＤＡＮ对风力发电输出功率密切相关的环境因素进 行分解，得到若干个规律性较强的模态分量，利用阈值去噪法对含噪声较多的第一模态分量进 行去噪，削弱环境因素的非平稳性；然后，将分解后的子分量和风电功率历史数据作为粒子群优 化后的核极限学习机算法的输入进行预测；最后，选用河北张家口某风电场的实测数据进行实 验对比分析。实验结果表明：所提出的改进风电功率预测组合模型的预测精度更高，适应于不 同季节环境下的风电功率预测。

关键词: 风电功率预测, 完全噪声辅助聚合经验模态分解, 小波阈值去噪, 核极限学习机, 粒 子群算法

Abstract: Aiming at the problem that wind power generation fluctuates greatly due to environmental changes and a kernel extreme learning machine is easy to fall into the local optimal solution, this paper constructs a short-term wind power prediction model of an optimized kernel extreme learning machine based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Analysis, wavelet threshold denoising and particle swarm algorithm. Firstly, CEEMDAN is used to decompose the environmental factors that are closely related to the output power of wind power generation, and several modal components with strong regularity are obtained. Besides, the threshold denoising method is used to denoise the first modal component containing much noise to weaken the non-stationarity of environmental factors. Then, after particle swarm optimization, the decomposed subcomponents and historical wind power data are used as the input of the kernel extreme learning machine algorithm for prediction. Finally, the measured data of a wind farm in Zhangjiakou, Hebei Province are selected for experimental comparison and analysis. The experimental results show that the improved wind power forecasting combination model proposed in this paper has higher forecasting accuracy and is suitable for wind power forecasting in different seasons.

中图分类号: 

  • TM714