Truncated Transient Stability Index for On-line Power System Transient Stability Assessment

2021 ◽  
Author(s):  
Xinlin Ye ◽  
Juan D. Morales ◽  
Jovica V. Milanovic
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Stability Index ◽  
Stability Assessment ◽  
On Line

scholarly journals An Improved On-line Contingency Screening for Power System Transient Stability Assessment

2017 ◽  
Vol 45 (8) ◽  
pp. 852-863 ◽  
Author(s):  
Tilman Weckesser ◽  
Hjörtur Jóhannsson ◽  
Mevludin Glavic ◽  
Jacob Østergaard
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Stability Assessment ◽  
Contingency Screening ◽  
On Line

Power System On-Line Transient Stability Assessment

2007 ◽  
Author(s):  
Hsiao-Dong Chiang ◽  
Yasuyuki Tada ◽  
Hua Li
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Stability Assessment ◽  
On Line

Methods for on-line transient stability assessment of power system

1984 ◽  
Vol 104 (5) ◽  
pp. 75-83 ◽  
Author(s):  
Susumu Yamashiro ◽  
Toichiro Koike
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Stability Assessment ◽  
On Line

scholarly journals Development of Back-Propagation Neural Network for Transient Stability Assessment of Power Systems

2019 ◽  
Vol 8 (4) ◽  
pp. 11637-11641
Keyword(s):  
Neural Network ◽  
Power System ◽  
Transient Stability ◽  
Research Work ◽  
Back Propagation ◽  
Stability Index ◽  
Back Propagation Neural Network ◽  
Stability Assessment ◽  
Target Feature ◽  
Wide Range

Transient Stability Assessment (TSA) is aspect of the power system dynamic stability assessment, which includes measuring the capacity of the system to stay synchronized under extreme disturbances. This research work shows the transient stability status of the power system following a major disturbance, such as a faults, line switchings, generator voltages. It can be predicted early based on response trajectories of rotor angle. This early prediction of transient stability is achieved by training a Back Propagation Neural Network (BPNN) taking trajectory of rotor angles as training features. Transient stability index (TSI) proposed in [4] is utilized as a target feature. The proposed methodology is tested with wide range of fault data collected from simulated IEEE 39-Benchmark system. The simulation results shows, utilization of BPNN for transient stability prediction resulted in better performance when compared to Radial Basis Neural Network (RBFNN) [4]..

Electric Power System Transient Stability Assessment Based on Bi-LSTM Attention Mechanism

2021 ◽  
Author(s):  
Nawaraj Kumar Mahato ◽  
Jie Dong ◽  
Chunyu Song ◽  
Zhimin Chen ◽  
Nan Wang ◽  
...  
Keyword(s):  
Power System ◽  
Electric Power ◽  
Transient Stability ◽  
Electric Power System ◽  
Attention Mechanism ◽  
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.

Transient Stability Assessment of a Power System Using Multi-layer SVM Method

2021 ◽  
Author(s):  
Qilin Wang ◽  
Chengzong Pang ◽  
Hashim Alnami
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Stability Assessment
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