| 引用本文: | 杨 茂,郭镇鹏,王 达,等.基于图神经网络的短期风电功率群体预测方法[J].电力系统保护与控制,2025,53(19):79-88.[点击复制] |
| YANG Mao,GUO Zhenpeng,WANG Da,et al.Short-term wind power group forecasting method based on graph neural networks[J].Power System Protection and Control,2025,53(19):79-88[点击复制] |
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| 摘要: |
| 为降低风电波动性对电力系统的影响，提出了计及时空关联性的大规模风电场群短期功率预测方法，同步输出所有风电场的短期功率预测结果。首先，提出了综合考虑风速、风向的空间相关性评价指标，进一步建立表征风电场群时空相关性的图拓扑结构。然后，构建一种深度残差图注意力网络挖掘多风电场间的时空相关特征，在训练过程中保存数据中蕴含的时空价值信息。最后，提出了虚假预测评价指标，评估场站预测功率在汇聚成集群预测功率时的虚假预测成分，使场群预测结果评价更加公平。以中国吉林省的某20个风电场组成的风电场群为研究对象开展实验，实验结果表明提出的风电功率预测模型的日前功率预测准确率达到91.68%。 |
| 关键词: 图注意力网络 深度残差网络 时空相关性 短期风电功率预测 误差评估 |
| DOI：10.19783/j.cnki.pspc.241190 |
| 投稿时间：2024-09-03修订日期：2024-11-17 |
| 基金项目:国家重点研发计划项目资助(2022YFB2403000) |
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| Short-term wind power group forecasting method based on graph neural networks |
| YANG Mao1,GUO Zhenpeng1,WANG Da1,ZHANG Wei1,WANG Bo2,JIANG Renxian1,SU Xin1 |
| (1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education
(Northeast Electric Power University), Jilin 132011, China; 2. State Key Laboratory of Operation and Control of
Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China) |
| Abstract: |
| To reduce the impact of wind power fluctuations on power systems, a short-term power forecasting method for large-scale wind farm clusters is proposed, which accounts for spatiotemporal correlation and simultaneously outputs short-term power predictions for all wind farms. First, an evaluation index that comprehensively considers the spatial correlation of wind speed and direction is proposed, and a graph topology structure is further established to characterize the spatiotemporal correlation of wind farm clusters. Then, a deep residual graph attention network is constructed to mine the spatiotemporal correlation features between multiple wind farms, preserving the valuable spatiotemporal information embedded in the data during training. Finally, a false prediction evaluation index is proposed to assess the false prediction components of the predicted power at the station when aggregated into cluster prediction power, making a fairer evaluation of cluster prediction results. Experiments are conducted using a cluster composed of 20 wind farms in Jilin province, China. Results show that the proposed wind power forecasting model achieves a day-ahead power prediction accuracy rate of 91.68%. |
| Key words: graph attention network deep residual network spatiotemporal correlation short-term wind power forecasting error evaluation |
