| 引用本文: | 关永锋,喻 敏,胡 佳.基于自适应组合模型的超短期风速预测[J].电力系统保护与控制,2022,50(4):120-128.[点击复制] |
| GUAN Yongfeng,YU Min,HU Jia.Ultra-short-term wind speed prediction based on an adaptive integrated model[J].Power System Protection and Control,2022,50(4):120-128[点击复制] |
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| 摘要: |
| 风电场的风速预测对电力系统的稳定及安全运行有着重大的影响。考虑到风速序列具有间歇性和随机性等特征，提出一种基于参数优化的变分模态分解及极限学习机的组合模型，将其用于超短期风速预测。首先，采用变分模态分解算法将风速序列分解为一系列的平稳分量。以正交性为适应度函数，利用网格优化算法搜索变分模态分解的关键参数值——分解层数和惩罚因子，确保分解出来各模态分量之间的信息正交性，抑制耦合分量的产生。然后，利用极限学习机对各分量进行预测。针对极限学习机预测不稳定的问题，采用粒子群算法对其初始权值及阈值进行参数优化，对于该模型的输入维数则运用自回归差分移动平均模型的定阶结果进行自适应确定。最后，叠加各分量的预测值作为最终的预测结果。实验结果表明，所提出的组合模型在预测精度上显著优于其他基准模型。 |
| 关键词: 参数优化的变分模态分解 自回归差分移动平均模型 粒子群优化算法 极限学习机 超短期风速预测 |
| DOI：DOI: 10.19783/j.cnki.pspc.210446 |
| 投稿时间：2021-04-20修订日期：2021-08-28 |
| 基金项目:国家自然科学基金项目资助(51877161)；湖北省教育厅科研计划指导项目资助(2018006)；冶金工业过程系统科学湖北省重点实验室开放基金资助(Y202007) |
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| Ultra-short-term wind speed prediction based on an adaptive integrated model |
| GUAN Yongfeng,YU Min,HU Jia |
| (1. Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology,
Wuhan 430081, China; 2. College of Science, Wuhan University of Science and Technology, Wuhan 430065, China) |
| Abstract: |
| Wind speed prediction has a significant impact on the stable and safe operation of a power system. According to the intermittent and random nature of wind speed, an integrated model of variational modal decomposition (VMD) based on grid search optimization algorithm (GS) and PSO-ELM is proposed for ultra-short-term wind speed prediction. First, the VMD is used to decompose wind speed sequence into a series of sub-sequences. By taking the orthogonality as the fitness function, the GS is used to search the key parameters of VMD adaptively, including the number of decomposed layers and a penalty factor. The purpose is to ensure information orthogonality between each component and to suppress coupling components. Then, the extreme learning machine (ELM) method is used to predict each sub-sequence. Given the unstable prediction of this model, particle swarm algorithm (PSO) is used to optimize the parameters of the initial weight and threshold, and the input dimension of each sub-sequence is determined adaptively by using the auto-regressive integrated moving average model (ARIMA). Finally, the predicted results of each sub-sequence are superimposed to obtain the final predicted wind speed. The result shows that the proposed integrated model is remarkably superior to all considered benchmark models in prediction accuracy.
This work is supported by the National Natural Science Foundation of China (No. 51877161). |
| Key words: parameter optimized variational modal decomposition ARIMA PSO ELM ultra-short-term wind speed prediction |
