scholarly journals Dynamic Preventive Control for Power System Using Transient Stability Assessment Based on the Pattern Recognition

1991 ◽  
Vol 111 (6) ◽  
pp. 611-618
Author(s):  
Hiroyuki Kita ◽  
Takahiro Mizuno ◽  
Ken-ichi Nishiya ◽  
Jun Hasegawa
Keyword(s):  
Pattern Recognition ◽  
Power System ◽  
Transient Stability ◽  
Stability Assessment ◽  
Preventive Control

Dynamic preventive control for power system using transient stability assessment based on pattern recognition

1992 ◽  
Vol 112 (8) ◽  
pp. 44-54 ◽  
Author(s):  
Hiroyuki Kita ◽  
Ken-Ichi Nishiya ◽  
Jun Hasegawa ◽  
Takahiro Mizuno
Keyword(s):  
Pattern Recognition ◽  
Power System ◽  
Transient Stability ◽  
Stability Assessment ◽  
Preventive Control

scholarly journals Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6953
Author(s):  
Yixing Du ◽  
Zhijian Hu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Short Term Memory ◽  
Risk Function ◽  
Stability Margin ◽  
Risk Level ◽  
Stability Assessment ◽  
Lstm Network ◽  
Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

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
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