The prediction of smart grid stability represents a challenging research problem because this information can be very useful for the identification of the participants that lead to instabilities in the system and consequently it is very useful to determine configurations in which the system is stable even if some participants present abnormalities. The main contributions of this research article are: (1) the presentation of a machine learning methodology for predicting the smart grid stability which is based on features extraction using the tsfresh package from Python, (2) the selection of features using three methods namely, Binary Particle Swarm optimization Features Selection (BPSOFS), Binary Kangaroo Mob optimization Features Selection (BKMOFS) and Multivariate Adaptive Regression Splines (MARS), (3) the prediction of the system’s stability using four classifiers namely, Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT) and Multilayer Perceptron Classifier (MPC) and (4) the detection of instability sources using a method based on machine learning and statistics. The best prediction results are obtained when MPC is applied (93.8%) and when the features are selected using BPSOFS.
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Detection of Sources of Instability in Smart Grids Using Machine Learning Techniques
Semantic Scholar · Engineering · 2019
Abstract
The prediction of smart grid stability represents a challenging research problem because this information can be very useful for the identification of the participants that lead to instabilities in the system and consequently it is very useful to determine configurations in which the system is stable even if some participants present abnormalities. The main contributions of this research article are: (1) the presentation of a machine learning methodology for predicting the smart grid stability which is based on features extraction using the tsfresh package from Python, (2) the selection of features using three methods namely, Binary Particle Swarm optimization Features Selection (BPSOFS), Binary Kangaroo Mob optimization Features Selection (BKMOFS) and Multivariate Adaptive Regression Splines (MARS), (3) the prediction of the system’s stability using four classifiers namely, Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT) and Multilayer Perceptron Classifier (MPC) and (4) the detection of instability sources using a method based on machine learning and statistics. The best prediction results are obtained when MPC is applied (93.8%) and when the features are selected using BPSOFS.