考虑新能源随机性的新型电力系统图深度学习稳定指标概率分布评估模型

Graph Deep Learning Based Stability Index Probability Distribution Assessment Method of New Power System Considering the Stochastic Output of Renewable Energy

新型电力系统的新能源大规模随机性出力特点导致目前在线暂态稳定扫描结论面临失效风险,这一问题从元件建模精度、系统拓扑适应性和计算速度三个方面同时对现有方法提出挑战。因此提出了基于重概率化改进的置信带方法和图深度学习的稳定指标概率分布及置信带评估模型,通过图深度学习模型快速评估少量采样点,再由重概率化置信带方法扩增结果,利用标签共现知识引导时域仿真对准确率进行增强,最后使用置信带方法给出随机出力下的稳定概率分布和区间形式的评估结论。该方法优点在于利用了图深度学习的拓扑适应能力和快速计算特点,且不受元件建模精度限制,置信带计算结论具有可靠的理论背景,能够评估稳定概率分布。在IEEE-39和IEEE-300节点系统上的评估精度验证表明,所提方法能够高精度地预测指定方式暂态稳定指标,并给出可靠的概率评估结论。

The characteristics of large-scale stochastic output of renewable energy in the new power system have posed a risk of failure in the current online transient stability assessment results.This issue simultaneously challenges existing methods in terms of component modeling accuracy,system topology adaptability and computational speed.In this study,a novel approach is proposed that combines improved confidence band method based on re-probability and a probability distribution and confidence band evalua⁃tion model of stable index based on graph deep learning.The graph deep learning model rapidly evaluates a few sampled points,which are then expanded using the confidence band method based on re-probability.Temporal domain simulations are guided by cooccurrence knowledge from labeled data to enhance accuracy.Finally,the confidence band calculation method is employed to derive the stability probability distribution and assessment conclusion in intervals under stochastic output conditions.The method capitalizes on the topological adaptability and rapid computation inherent to graph deep learning.Moreover,it remains unhindered by limitations in component modeling accuracy.The conclusions drawn from the confidence band calculation are firmly rooted in theory and can evaluate the stable probability distribution.Evaluation accuracy verification on the IEEE-39 and IEEE-300 bus systems demonstrates the efficacy of the proposed method in accurately predicting specified transient stability indices and delivering reliable probability assessments.