Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation

doi:10.11930/j.issn.1004-9649.201912113

Abstract

Abstract: In traditional data-driven power system transient stability assessment methods, the impact of noise in the collected data and the information missing problems are rarely considered for the transient stability prediction. To deal with these problems, this paper presents a method for transient stability prediction based on data augmentation and deep residual network (ResNet). Firstly, the original training data is extended with consideration of the noise data and data-missing conditions. Then, the real-time data of the disturbed generator is used as input features. Considering the similarity between high-dimensional time series data and images, the deep residual network, which is an improved algorithm based on convolutional neural networks, is used to build transient stability assessment model. The case studies show that the proposed method can improve the generalization ability of the model, and has better robustness in dealing with noise data or data missing problems.

Key words: transient stability, deep learning, deep residual network, data augmentation, power system, noise, information missing

ZHOU Yanzhen, ZHA Xianyu, LAN Jian, GUO Qinglai, SUN Hongbin, XUE Feng, WANG Shengming. Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation[J]. Electric Power, 2020, 53(1): 22-31.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201912113

https://www.electricpower.com.cn/EN/Y2020/V53/I1/22

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