scholarly journals A power system transient stability assessment method based on active learning

2021 ◽  
Author(s):  
Yuqiong Zhang ◽  
Qiang Zhao ◽  
Bendong Tan ◽  
Jun Yang
Keyword(s):  
Active Learning ◽  
Power System ◽  
Transient Stability ◽  
Assessment Method ◽  
Stability Assessment

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 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.

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.

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