PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

A Review of Machine Learning Approaches to Power System Security and Stability

“…As voltage stability monitoring models are highly nonlinear complex models with large volume of dataset involved, the need for feature selection and reduction is highly important. Mohammadi and Dehghani [124] explained that the large quantities of power system attributes are not appropriate to be used directly as classifier's inputs. Hence, several feature extraction methods have been proposed in the literature.…”

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.…”

“…Generally, it also reduces the dimensionality of data. PCA and its variants are often used as a feature extraction tool [44]- [46], as well as Fisher's linear discriminant [47] and shapelets for time series [48] but recently, an approach based on deep learning auto-encoders was proposed [49]- [52].…”

“…A huge advantage of them is their interpretability, which is important in reliability management. Single Decision Trees (DTs) are used to predict the stability status of the system [36], [42], [44], [48], [92]- [94]. In [94], the authors explore a trade-off between predictive accuracy and interpretability.…”