基于集成深度置信网络的精细化电力系统暂态稳定评估

为了进一步提高电力系统暂态稳定的预测精度及给出更精细化的评估结果,将深度学习与电力系统暂态稳定相结合,根据故障切除后发电机功角"轨迹簇"特征,提出一种基于集成不同结构的深度置信网络(DBN)的精细化电力系统暂态稳定评估模型。该模型的基分类器DBN能够有效地利用深层架构所具有的特征提取能力,充分挖掘出输入特征与暂态稳定评估结果之间的非线性映射关系。在新英格兰10机39节点系统上的实验结果表明,该方法不仅优于浅层学习框架,也比部分深度学习模型的性能更加优越。除此之外,该集成DBN算法不仅有较高的预测精度,而且可以有效地评估系统的稳定裕度和不稳定程度等级;在部分同步相量测量装置信息缺失以及含有噪声时,表现出较强的鲁棒性。

Refined Transient Stability Evaluation for Power System Based on Ensemble Deep Belief Network

In order to further improve the prediction accuracy of transient stability for power system and give more refined evaluation results, the deep learning is combined with the transient stability of power system. A refined evaluation model of transient stability for power system based on ensemble deep belief network (DBN) with different structures is proposed based on the characteristics of the generator power angle "trajectory cluster" after fault removal. The base classifier DBN of the model can effectively utilize the feature extraction ability of the deep architecture and fully exploit the nonlinear mapping relationship between the input features and the evaluation results of transient stability. Experimental results on the New England 10-machine 39-node system show that this method is not only superior to the shallow learning framework, but also superior to the partial deep learning model. In addition, the ensemble DBN algorithm not only has higher prediction accuracy, but also can effectively evaluate the stability margin and instability level of the system. It shows strong robustness when some information of phasor measurement unit (PMU) is missing and contains noise.