Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

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

Mathematical Problems in Engineering ◽

10.1155/2015/786396 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 4

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.

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An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

Applied Sciences ◽

10.3390/app10072255 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2255

Author(s):

Jun Liu ◽

Huiwen Sun ◽

Yitong Li ◽

Wanliang Fang ◽

Shuanbao Niu

Keyword(s):

Feature Selection ◽

Power Systems ◽

Power System ◽

Prediction Model ◽

Ensemble Learning ◽

Prediction Accuracy ◽

Transient Stability ◽

Stability Assessment ◽

Stability Prediction ◽

Electromagnetic Power

Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

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Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

IEEE Access ◽

10.1109/access.2020.2974915 ◽

2020 ◽

Vol 8 ◽

pp. 35231-35244

Author(s):

Baoqin Li ◽

Junyong Wu ◽

Liangliang Hao ◽

Meiyang Shao ◽

Ruoyu Zhang ◽

...

Keyword(s):

Power System ◽

Transient Stability ◽

Short Term Memory ◽

Stability Assessment ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory

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An AdaBoost-based tree augmented naive Bayesian classifier for transient stability assessment of power systems

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211047308 ◽

2021 ◽

pp. 1748006X2110473

Author(s):

Huimin Wang ◽

Zhaojun Steven Li

Keyword(s):

Power Systems ◽

Power System ◽

Transient Stability ◽

Classification Performance ◽

Bayesian Classifier ◽

Stability Assessment ◽

Naïve Bayesian Classifier ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Bus System

By focusing on the accuracy limitations of the naive Bayesian classifier in the transient stability assessment of power systems, a tree augmented naive Bayesian (TAN) classifier is adopted for the power system transient stability assessment. The adaptive Boosting (AdaBoost) algorithm is used in the TAN classifier to form an AdaBoost-based tree augmented naive Bayesian (ATAN) classifier for further classification performance improvement. To construct the ATAN classifier, eight attributes that reasonably reflect the transient stability or transient instability of a power system are selected as inputs of the proposed classifier. In addition, the class-attribute interdependence maximization (CAIM) algorithm is used to discretize the attributes. Then, the operating mechanism of the power system is used to obtain the dependencies between the attributes, and the parameters of the ATAN classifier are learned according to the Bayes’ theorem and the criterion of maximizing a posterior estimation. Four evaluation indicators of the ATAN classifier are used, that is, the value of Kappa, the area under the receiver operating characteristic curve (AUC), F1 score, and the average evaluation indicator. Lastly, experiments are implemented on the IEEE 3-generator 9-bus system and IEEE 10-generator 39-bus system. The simulation results show that the ATAN classifier can significantly improve the classification performance of the transient stability assessment of the power system.

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