Power System Transient Stability Prediction Based on Sample Pre-screening of SVM Algorithm and AdaBoost Algorithm

2021 ◽  
Author(s):  
Xingyun Chen ◽  
Kejian Nie
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Stability Prediction ◽  
Adaboost Algorithm ◽  
Svm Algorithm

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

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.

Power System On-line Transient Stability Prediction by Margin Indices and Random Forests

2019 ◽  
Author(s):  
Yuchuan Chen ◽  
Seyed Mahdi Mazhari ◽  
C.Y. Chung ◽  
Sherif O. Faried ◽  
Bingzhi Wang ◽  
...  
Keyword(s):  
Power System ◽  
Random Forests ◽  
Transient Stability ◽  
Stability Prediction ◽  
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