A Novel Ensemble Approach for Solving the Transient Stability Classification Problem

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],…”

“…Table II provides an overview of the main data pre-processing methods used for DSA and DSC discussed below. [34], [35], [37] Genetic algorithms [38], [39] Tree-based algorithms [40]- [43] Feature extraction PCA and variants [44]- [46] Fisher's linear discriminant [47] Shapelets for time series [48] Deep learning auto-encoders [49]- [52] Class imbalance Oversampling [24], [53], [54] Cost-sensitive learning [53], [55] Ensemble methods [41], [45], [56], [57] 1) Feature engineering: Given the large number of features necessary to fully describe the state of a power system and the need for fast algorithms, feature selection techniques are proposed in many papers. Too many features can lead to excessive training time and, if many features are not relevant, could decrease the performance of the learnt model.…”

“…The problem is particularly important, given that unstable events must be detected to guarantee the reliability of the system. To overcome this issue, several solutions have been proposed in the literature, such as oversampling the minority class [24], [53], [54], undersampling the majority class, exploiting a cost-sensitive learning [53], [55] and using ensemble methods [41], [45], [56], [57].…”

“…Ensemble methods can also help for class imbalance problems. For instance, in [56] the authors exploit the diverse extreme learning machine algorithm to determine transient stability. They show that adding diversity can improve accuracy in case of the class imbalance problem.…”

Grid-Forming Power Converters Tuned Through Artificial Intelligence to Damp Subsynchronous Interactions in Electrical Grids

“…Specifically, the relationship between system variables and stability indexes can be modeled to predict the post-fault state of the system. For instance, an ensemble approach based on extreme learning machines is adopted in [17] to predict stability after a disturbance, while a recurrent neural network with long short term memory (LSTM) cells is trained in [18] for the same task considering time dependencies in the data. Furthermore, prediction of damping state (i.e.…”