scholarly journals Representational learning approach for power system transient stability assessment based on convolutional neural network

2017 ◽  
Vol 2017 (13) ◽  
pp. 1847-1850 ◽  
Author(s):  
Bendong Tan ◽  
Jun Yang ◽  
Xueli Pan ◽  
Jun Li ◽  
Peiyuan Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Power System ◽  
Convolutional Neural Network ◽  
Transient Stability ◽  
Learning Approach ◽  
Stability Assessment

Convolutional neural network-based power system transient stability assessment and instability mode prediction

2020 ◽  
Vol 263 ◽  
pp. 114586 ◽  
Author(s):  
Zhongtuo Shi ◽  
Wei Yao ◽  
Lingkang Zeng ◽  
Jianfeng Wen ◽  
Jiakun Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power System ◽  
Convolutional Neural Network ◽  
Transient Stability ◽  
Instability Mode ◽  
Stability Assessment

scholarly journals Power system transient stability assessment based on rough set and deep residual neural network

2021 ◽  
Vol 2121 (1) ◽  
pp. 012012
Author(s):  
Jian Chai ◽  
Xihuai Wang ◽  
Jianmei Xiao
Keyword(s):  
Neural Network ◽  
Power System ◽  
Rough Set ◽  
Transient Stability ◽  
Rough Set Theory ◽  
Assessment Method ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Stability Assessment ◽  
Combined Application

Abstract Machine learning algorithms have been widely used in power system transient stability evaluation. The combined application of data analysis and evaluation and neural network provides a new direction for power system transient stability analysis. After the actual power grid is running, there is obviously an imbalance between stable samples and unstable samples. The current deep learning network realizes the power system transient stability assessment method with too many redundant attributes, and the characteristics will inevitably be lost during the data transmission process. This leads to serious problems with the tendency of the training of the data-driven transient stability assessment model. The rough set theory algorithm is introduced to reduce the redundant attributes of power system transient data sets, which simplifies the difficulty of data training. At the same time, as the neural network deepens, the deep residual neural network model has a higher accuracy rate and effectively avoids the “gradient explosion” and “gradient dispersion” problems. Compared with the traditional neural network, it has better Evaluate performance.

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