| 引用本文: | 田壁源,刘琪,张新燕,等.基于APSO-GSA和相关向量机的短期风电功率预测[J].电力系统保护与控制,2020,48(2):107-114.[点击复制] |
| TIAN Biyuan,LIU Qi,ZHANG Xinyan,et al.Short-term wind power prediction based on APSO-GSA and correlation vector machine[J].Power System Protection and Control,2020,48(2):107-114[点击复制] |
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| 摘要: |
| 精确的短期风电功率预测建模对于提升新能源电力系统经济稳定运行十分重要。针对传统预测方法在小样本学习、精细化建模、概率性预测等方面的不足和易陷入局部最优的影响，首先以相关向量机(RVM)理论为核心，建立了基于RVM的风电功率预测模型。然后，针对万有引力搜索算法(GSA)缺少跳出局部最优机制和群体记忆功能，提出了一种结合自适应粒子群算法(APSO)的APSO-GSA混合优化算法，利用该算法对RVM模型参数进行优化。最后，以中国西北某风电场运行数据为例进行验证。结果表明，所提方法具有更高的建模精度和更快的收敛速度，实现了利用少量样本和简单模型对未来时刻风电功率的精确预测。 |
| 关键词: 风电功率预测 相关向量机 万有引力搜索算法 自适应粒子群算法 |
| DOI：10.19783/j.cnki.pspc.190336 |
| 投稿时间：2019-03-27修订日期：2019-05-13 |
| 基金项目:国家自然科学基金项目资助(51667018，51367015) |
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| Short-term wind power prediction based on APSO-GSA and correlation vector machine |
| TIAN Biyuan,LIU Qi,ZHANG Xinyan,WANG Yujie,ZHANG Yifan,GUO Hongyan,CHANG Xiqiang |
| (State Grid Urumqi Power Supply Company, Urumqi 830011, China;College of Electrical Engineering, Xinjiang University, Urumqi 830049, China) |
| Abstract: |
| Accurate short-term wind power forecasting modeling is very important to improve the economic and stable operation of new energy power systems. In view of the shortcomings of traditional forecasting methods in small sample learning, fine modeling, probability prediction and the influence of easily falling into local optimum, this paper firstly establishes the wind power forecasting model of Relevant Vector Machine (RVM) based on the theory of RVM. Then, aiming at the lack of local optimum mechanism and group memory function in Gravitational Search Algorithm (GSA), an APSO-GSA hybrid optimization algorithm is proposed to optimize the parameters of RVM model. Finally, a wind farm operation data in Northwest China is taken as an example to verify the effectiveness of the proposed method. The results show that the proposed method has higher modeling accuracy and faster convergence speed, and achieves accurate prediction of wind power at future time using a small number of samples and simple models. This work is supported by National Natural Science Foundation of China (No. 51667018 and No. 51367015). |
| Key words: wind power prediction relevant vector machine GSA APSO |
