Transient Stability Assessment Using an Adaptive Fuzzy Classification Technique

A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

“…The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems.Energies 2016, 9, 778 2 of 20 from among massive sets of data, have been used to predict the transient stability [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The transient stability prediction can be treated as a two-class classification (stable and unstable) problem and solved by machine learning methods.…”

“…Recently, the construction of input data and the improvement of machine learning methods to increase the accuracy have been the main research focus. Methods such as neural networks [6-8], decision trees (DTs) [9][10][11], support vector machines (SVMs) [12][13][14][15], fuzzy theory [16,17] and some ensemble classifiers [18,19] had been used for post-fault transient stability analysis.In fact, a classifier should possess high prediction accuracy, but it is also important to estimate the credibility of each classification result, or the confidence level. The existing transient stability prediction only provides a prediction result but seldom considers the confidence level.…”