Decentralized coordinated control for large power system based on transient stability assessment

2013 ◽  
Vol 46 ◽  
pp. 153-162 ◽  
Author(s):  
Chun-Xia Dou ◽  
Jinzhao Yang ◽  
Xiaogang Li ◽  
Ting Gui ◽  
Yefei Bi
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Coordinated Control ◽  
Stability Assessment ◽  
Large Power

scholarly journals Stacked-GRU Based Power System Transient Stability Assessment Method

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 121 ◽  
Author(s):  
Feilai Pan ◽  
Jun Li ◽  
Bendong Tan ◽  
Ciling Zeng ◽  
Xinfan Jiang ◽  
...  
Keyword(s):  
Power System ◽  
New England ◽  
Transient Stability ◽  
Time Series Data ◽  
Assessment Method ◽  
Complex Model ◽  
Power Grids ◽  
Series Data ◽  
Stability Assessment ◽  
Large Power

With the interconnection between large power grids, the issue of security and stability has become increasingly prominent. At present, data-driven power system adaptive transient stability assessment methods have achieved excellent performances by balancing speed and accuracy, but the complicated construction and parameters are difficult to obtain. This paper proposes a stacked-GRU (Gated Recurrent Unit)-based transient stability intelligent assessment method, which builds a stacked-GRU model based on time-dependent parameter sharing and spatial stacking. By using the time series data after power system failure, the offline training is performed to obtain the optimal parameters of stacked-GRU. When the application is online, it is assessed by framework of confidence. Basing on New England power system, the performance of proposed adaptive transient stability assessment method is investigated. Simulation results show that the proposed model realizes reliable and accurate assessment of transient stability and it has the advantages of short assessment time with less complex model structure to leave time for emergency 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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