Data Augment Using Deep Convolutional Generative Adversarial Networks for Transient Stability Assessment of Power Systems

2020 ◽  
Author(s):  
Jiamin Li ◽  
Hongying Yang ◽  
Liping Yan ◽  
Zonghan Li ◽  
Daowei Liu ◽  
...  
Keyword(s):  
Power Systems ◽  
Transient Stability ◽  
Generative Adversarial Networks ◽  
Stability Assessment ◽  
Adversarial Networks

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 Day-ahead renewable scenario forecasts based on generative adversarial networks

2020 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu
Keyword(s):  
Power Systems ◽  
Renewable Resources ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Power Sources ◽  
Renewable Power ◽  
Adversarial Networks ◽  
Future Behavior ◽  
Large Scale Integration ◽  
Scale Integration

<p>With the increasing penetration of renewable resources such as wind and solar, the operation and planning of power systems, especially in terms of large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised method for renewable scenario forecasts that considers spatiotemporal correlations based on generative adversarial networks (GANs), which have been shown to generate high-quality samples. We first utilized an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generated a large number of forecasted scenarios using stochastic constrained optimization. For validation, we used power-generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The experimental results validated the effectiveness of our proposed method and indicated that it has significant potential in renewable scenario analysis.</p>

scholarly journals Transient Stability Assessment of Multi-Machine Multi-Converter Power Systems

2019 ◽  
Vol 34 (5) ◽  
pp. 3504-3514 ◽  
Author(s):  
Hugo N. Villegas Pico ◽  
Brian B. Johnson
Keyword(s):  
Power Systems ◽  
Transient Stability ◽  
Stability Assessment

scholarly journals Maximal Positively Invariant Set Determination for Transient Stability Assessment in Power Systems

2019 ◽  
Author(s):  
Antoine Oustry ◽  
Carmen Cardozo ◽  
Patrick Pantiatici ◽  
Didier Henrion
Keyword(s):  
Power Systems ◽  
Transient Stability ◽  
Invariant Set ◽  
Stability Assessment ◽  
Positively Invariant Set

Transient Stability Assessment of Power Systems Based on Slow Feature Analysis

2019 ◽  
Author(s):  
Yabin Si ◽  
Daowei Liu ◽  
Hongying Yang ◽  
Zonghan Li ◽  
Youqing Wang
Keyword(s):  
Power Systems ◽  
Transient Stability ◽  
Feature Analysis ◽  
Stability Assessment ◽  
Slow Feature Analysis

Decision trees applied to on-line transient stability assessment of power systems

2003 ◽  
Author(s):  
L. Wehenkel ◽  
T. Van Cutsem ◽  
M. Ribbens-Pavella
Keyword(s):  
Power Systems ◽  
Decision Trees ◽  
Transient Stability ◽  
Stability Assessment ◽  
On Line

scholarly journals A Multifeature Fusion Approach for Power System Transient Stability Assessment Using PMU Data

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Guoqing Li ◽  
Zhenhao Wang ◽  
Zijiao Han ◽  
Xue Bai
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Area Measurement ◽  
Feature Subset ◽  
Stability Assessment ◽  
Wide Area Measurement System ◽  
Feature Spaces ◽  
Proposed Model ◽  
Fusion Approach

Taking full advantage of synchrophasors provided by GPS-based wide-area measurement system (WAMS), a novel VBpMKL-based transient stability assessment (TSA) method through multifeature fusion is proposed in this paper. First, a group of classification features reflecting the transient stability characteristics of power systems are extracted from synchrophasors, and according to the different stages of the disturbance process they are broken into three nonoverlapped subsets; then a VBpMKL-based TSA model is built using multifeature fusion through combining feature spaces corresponding to each feature subset; and finally application of the proposed model to the IEEE 39-bus system and a real-world power system is demonstrated. The novelty of the proposed approach is that it improves the classification accuracy and reliability of TSA using multifeature fusion with synchrophasors. The application results on the test systems verify the effectiveness of the proposal.

scholarly journals Post-disturbance transient stability assessment of power systems by a self-adaptive intelligent system

2015 ◽  
Vol 9 (3) ◽  
pp. 296-305 ◽  
Author(s):  
Rui Zhang ◽  
Kit Po Wong ◽  
Yan Xu ◽  
Zhao Yang Dong
Keyword(s):  
Power Systems ◽  
Transient Stability ◽  
Intelligent System ◽  
Stability Assessment ◽  
Self Adaptive
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