Transient stability contingency screening and ranking based on data mining

Recent Developments in Machine Learning for Energy Systems Reliability Management

“…The database generation is then usually done offline, given the extensive simulation cost to build it, while the application of the resulting model trained on the dataset can be done offline or online, depending on the application and the context. [15], [16], [19], [22], [27]- [29], [32], [34], [35], [37], [38], [41], [43], [45], [47], [49]- [52], [54]- [56], [63], [64], [67]- [73], [75], [76], [81]- [83], [85]- [88], [90], [92], [93], [98], [102], [105], [107], [113], [115]- [117] Voltage stability [26], [30], [39], [40], [42], [44], [46], [48], [53],…”

“…Logistic regression, after a stacked denoising auto-encoder is used in [52] to predict post-fault stability status. Less common approaches have also been proposed, such as unsupervised learning with PCA [102] and semi-supervised learning when few observations are labelled [19]. In the latter, the 'Tri-training' algorithm is used, which consists in training three models with a small subset of labelled data and then adding an unlabelled sample to the training set of a classifier only if the two other classifiers agree.…”

Probabilistic framework for transient stability contingency ranking of power grids with active distribution networks: application in post disturbance security assessment

“…TS CR has been addressed in several publications based on the TDS and equal area criterion [35–38], transient energy function [39–41], the combination of PMU with fuzzy neural network [42, 43], regression‐based model [44], data mining [45] and DT [46, 47], which are focused on the online CR. As a result, the pros and cons of these methods are almost limited by the speed of calculation and assessment.…”