Energy-based Kinetic Energy Features for Online Dynamic Security Assessment using Bayes by Backprop Machine Learning Algorithm

In the following work, A machine learning-based Online Dynamic security assessment (DSA) scheme is proposed for which Energy-based Kinetic energy terms are applied as input features. The aim is to develop simple pre-processed input features that are easier to calculate and when applied as inputs, require fewer samples of data to develop a trained model. The model is trained using the current system topological condition. The proposed scheme is implemented using a Bayesian neural network (BNN), which is developed by the Bayes-by-Backprop algorithm. The performance of the proposed input features is demonstrated with IEEE 39 bus test system. When the Energy-based kinetic energy terms are applied as input features it is shown that the model can be trained with fewer samples of data.