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基于贝叶斯深度学习的新能源多场站临界短路比区间预测方法

Multiple Renewable Energy Station Critical Short Circuit Ratio Interval Prediction Method Based on Bayesian Deep Learning
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摘要 大规模新能源并网后电力系统的电压安全稳定问题突出,亟需一种兼具准确性和实用性的方法来评估系统的电压支撑强度。为此,该文提出一种基于贝叶斯深度学习的新能源多场站短路比(multiple renewable energy station short circuit ratio,MRSCR)智能增强方法。首先,聚焦于MRSCR缺乏准确的临界短路比(critical short circuit ratio,CSCR)问题,提出CSCR样本集的构建流程,并据此开发样本的批量仿真程序。然后,利用多门控混合专家网络对各新能源接入点的CSCR进行同步预测,并结合贝叶斯深度学习提升预测精度,量化预测不确定性。最后,考虑到点估计的弊端,提出一种基于动态阈值的不等式方法来给出兼具可靠性和清晰性的区间估计,为不同的决策需求提供多种属性的预测值。在CEPRI-FS-102节点系统上的测试结果表明,所提方法可有效提高电压支撑强度的评估精度和速度,其预测信息可为决策过程提供重要的指导意义。 After large-scale renewable energy is connected to the grid,the problem of voltage security and stability is prominent,so it is urgent to find a method with accuracy and practicability to evaluate the voltage support strength of the system.Therefore,an intelligent enhancement method of multiple renewable energy station short circuit ratio(MRSCR)based on Bayesian deep learning is proposed in this paper.First,focusing on the lack of accurate critical short circuit ratio(CSCR)of MRSCR,the construction process of CSCR sample set is proposed,and the batch simulation program of samples is developed accordingly.Then,the multi-gate mixture-ofexperts is used to synchronously predict the CSCR of each new energy access point,and Bayesian deep learning is combined to improve the prediction accuracy and quantify the prediction uncertainty.Finally,considering the disadvantages of point estimation,an inequality method based on dynamic threshold values is proposed to provide reliable and clear interval estimation,which can provide multiple attributes of predicted values for different decision requirements.The test results on the CEPRI-FS-102 bus system show that the proposed method can effectively improve the evaluation accuracy and speed of voltage support strength,and the prediction information can provide important guidance for the decision-making process.
作者 李保罗 徐式蕴 李宗翰 孙华东 于琳 LI Baoluo;XU Shiyun;LI Zonghan;SUN Huadong;YU Lin(Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University),Jinan 250061,Shandong Province,China;State Key Laboratory of Power Grid Safety and Energy Conservation(China Electric Power Research Institute),Haidian District,Beijing 100192,China)
出处 《中国电机工程学报》 EI CSCD 北大核心 2024年第14期5451-5462,I0002,共13页 Proceedings of the CSEE
基金 国家重点研发计划项目(2021YFB2400800)。
关键词 电压支撑强度 贝叶斯深度学习 多任务学习 短路比 system strength bayesian deep learning multi-tasking learning short circuit ratio
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