With recent advances in information and communication technology (ICT), the bleeding
edge concept of digital twin (DT) has enticed the attention of many researchers to …

Over the last decade, the number of cyber attacks targeting power systems and causing
physical and economic damages has increased rapidly. Among them, false data injection …

Internet of Things (IoT) devices are becoming ubiquitous in our lives, with applications
spanning from the consumer domain to commercial and industrial systems. The steep …

Although state estimation using a bad data detector (BDD) is a key procedure employed in
power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial …

The network attack detection model based on machine learning (ML) has received extensive
attention and research in PMU measurement data protection of power systems. However …

Smart Grid (SG) is the revolutionised power network characterised by a bidirectional flow of
energy and information between customers and suppliers. The integration of power …

The new power sector scenario has focused on integrating renewable energy sources into
the grid and achieving trustworthy consumer-utility-stakeholder relationships using smart …

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

State estimation methods used in cyber–physical systems (CPSs), such as smart grid, are
vulnerable to false data injection attacks (FDIAs). Although substantial deep learning …

Vulnerability of various machine learning methods to adversarial examples has been
recently explored in the literature. Power systems which use these vulnerable methods face …