Transient Stability Assessment of Power System Incorporating DFIM-Based Pumped Storage Hydropower and Wind Farm

2020 ◽  
pp. 131-152
Author(s):  
Mohsen Alizadeh Bidgoli ◽  
Saman Atrian ◽  
Weijia Yang ◽  
Francisco M. Gonzalez-Longatt
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Transient Stability ◽  
Stability Assessment ◽  
Pumped Storage

Transient stability assessment for power system with wind farm considering the stochasticity

2021 ◽  
Author(s):  
Yicen Liu ◽  
Qunying Liu ◽  
Fengkai Hu ◽  
Yin Xu ◽  
Xing Chen ◽  
...  
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Transient Stability ◽  
Stability Assessment

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

Wind Farm Grid Integration Simulation Analysis of the Impact of Power System Transient Stability

2013 ◽  
Vol 805-806 ◽  
pp. 334-337
Author(s):  
Shi Wei Su ◽  
You Wei Zhou ◽  
Wei Xiong
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Transient Stability ◽  
Simulation Analysis ◽  
Direct Access ◽  
Grid Integration ◽  
Access System ◽  
Wind Dispersion ◽  
The Impact ◽  
Single Set

Analysis compares the direct access to a single set of wind power systemTwo groups of wind farm access system directlyMultiple sets of wind farm access system directly And Multiple sets of wind dispersion access system's impact on power system transient stability. And compare the simulation results, Concluded that wind farm access capacity and its topology structure's influence on system transient stability.

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
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