Abstract：In power system transient stability assessment, the measurement data of power system synchronous phasor measurement unit (PMU) may exist noise problems during acquisition and transmission process, and the transient stability and instability samples are imbalanced, resulting in the tendency of data-driven transient stability assessment model training and serious misjudgment problems. This paper proposes a power system transient stability assessment method based on improved deep residual shrinkage networks (IDRSN). First, the bottom-level measured electrical quantity is constructed as a feature map as the input of model, and the deep structure of model is used to establish the mapping relationship between the input and the stable result. Faced with noise problems, the model uses the attention mechanism to automatically learn the noise threshold through a soft threshold function to reduce noise and irrelevant feature interference; and through focus loss function (FL), the weight coefficient is introduced to correct the tendency of model training. Modulation factors is used to focus on misclassified samples to improve model training efficiency and evaluation performance. Through the simulation verification of the New England 10-machine 39-node system, the proposed model can effectively reduce the noise interference of different degrees, correct the bias of the model training on the imbalanced data set, and reduce the misclassified samples. Under different PMU configuration schemes, all are obtained better evaluation effect.
通讯作者:
郭鲁豫,女,1996年生,硕士研究生,研究方向为电力系统运行、分析。E-mail：guoluyu111@126. com
作者简介: 卢锦玲,女,1971年生,博士,副教授,研究方向为电力系统运行、分析与控制等。E-mail：lujinling@126. com
引用本文:
卢锦玲, 郭鲁豫. 基于改进深度残差收缩网络的电力系统暂态稳定评估[J]. 电工技术学报, 2021, 36(11): 2233-2244.
Lu Jinling, Guo Luyu. Power System Transient Stability Assessment Based on Improved Deep Residual Shrinkage Network. Transactions of China Electrotechnical Society, 2021, 36(11): 2233-2244.
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2021, Vol. 36 
