The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …

Fast and accurate prediction and control of power system dynamic frequency after
disturbance is essential to enhance power system stability. Machine learning methods have …

Over the past decades, electric power systems (EPSs) have undergone an evolution from an
ordinary bulk structure to intelligent flexible systems by way of advanced electronics and …

With increase of practical power system complexity, power system online stability
assessment and control is more and more important. Application of the traditional model …

Smart-grid (SG) is a new revolution in the electrical utility industry (EUI) over the past
decade. With each moving day, some new advanced technologies are coming into the …

With the rapid growth of power systems measurements in terms of size and complexity,
discovering statistical patterns for a large variety of real-world applications such as …

The fast expansion of renewable energy sources, rising electricity demand, and the
requirement for improved grid dependability have made it necessary to create cutting-edge …

This paper focuses on a powerful and comprehensive overview of Deep Learning (DL)
techniques on Distribution Automation System (DAS) applications to provide a complete …

Fast and stochastic power fluctuations caused by renewable energy sources and flexible
loads have significantly deteriorated the frequency performance of modern power systems …

The automatic classification of power quality disturbances (PQD) is of great significance for
solving power quality problems. In this study, we propose an ensemble deep learning …