This paper presents a comparative study of the predictive performances of neural network
time series models for forecasting failures and reliability in engine systems. Traditionally …

Power system security is one of the vital concerns in competitive electricity markets due to
the delineation of the system controller and the generation owner. This paper presents an …

We briefly review previous applications of machine learning (ML) in power grid analyses
and introduce our ongoing effort toward developing a generative-adversarial (GA) model for …

Transient stability assessment (TSA) of large power systems by the conventional method is a
time consuming task. For each disturbance many nonlinear equations should be solved that …

We propose a unified data modeling approach that is equally applicable to supervised
regression and classification applications, as well as to unsupervised probability density …

In this paper, the use of artificial neural networks (ANN) is proposed for solving the well
known power flow (PF) problem of electric power systems (EPS). PF evaluates the steady …

This paper proposes a supervised learning approach to fast and accurate power system
security assessment and contingency analysis. The severity of the contingency is measured …

Recent investigations have revealed the significant role of reactive power in blackout events.
The associated disturbances frequently emerge in the form of voltage instability and …

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is
proposed for the construction of radial basis function (RBF) networks with tunable nodes …

This paper presents a method for solving the load-flow problem of the electric power
systems using radial basis function (RBF) neural network with a fast hybrid training method …