DOAJ Open Access 2020

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

Yanzhen ZHOU Xianyu ZHA Jian LAN Qinglai GUO Hongbin SUN +2 lainnya

Abstrak

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.

Topik & Kata Kunci

Electricity Production of electric energy or power. Powerplants. Central stations

Penulis (7)

Yanzhen ZHOU

Xianyu ZHA

Jian LAN

Qinglai GUO

Hongbin SUN

Feng XUE

Shengming WANG

Format Sitasi

APA MLA BibTeX

ZHOU, Y., ZHA, X., LAN, J., GUO, Q., SUN, H., XUE, F. et al. (2020). Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation. https://doi.org/10.11930/j.issn.1004-9649.201912113

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Informasi Jurnal

Tahun Terbit: 2020
Sumber Database: DOAJ
DOI: 10.11930/j.issn.1004-9649.201912113
Akses: Open Access ✓