Recent developments in machine learning for energy systems reliability management
L Duchesne, E Karangelos… - Proceedings of the …, 2020 - ieeexplore.ieee.org
This article reviews recent works applying machine learning (ML) techniques in the context
of energy systems' reliability assessment and control. We showcase both the progress …
of energy systems' reliability assessment and control. We showcase both the progress …
Detecting false data attacks using machine learning techniques in smart grid: A survey
The big data sources in smart grid (SG) enable utilities to monitor, control, and manage the
energy system effectively, which is also promising to advance the efficiency, reliability, and …
energy system effectively, which is also promising to advance the efficiency, reliability, and …
Adversarial attacks and defenses for deep-learning-based unmanned aerial vehicles
J Tian, B Wang, R Guo, Z Wang… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The introduction of deep learning (DL) technology can improve the performance of cyber–
physical systems (CPSs) in many ways. However, this also brings new security issues. To …
physical systems (CPSs) in many ways. However, this also brings new security issues. To …
Survey of machine learning methods for detecting false data injection attacks in power systems
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 …
physical and economic damages has increased rapidly. Among them, false data injection …
LESSON: Multi-label adversarial false data injection attack for deep learning locational detection
Deep learning methods can not only detect false data injection attacks (FDIA) but also locate
attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep …
attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep …
Deep learning for cybersecurity in smart grids: Review and perspectives
Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered
significant attention in recent years. The application of artificial intelligence (AI), particularly …
significant attention in recent years. The application of artificial intelligence (AI), particularly …
Verification of neural network behaviour: Formal guarantees for power system applications
A Venzke, S Chatzivasileiadis - IEEE Transactions on Smart …, 2020 - ieeexplore.ieee.org
This paper presents for the first time, to our knowledge, a framework for verifying neural
network behavior in power system applications. Up to this moment, neural networks have …
network behavior in power system applications. Up to this moment, neural networks have …
Evasion attacks with adversarial deep learning against power system state estimation
Cyberattacks against critical infrastructures, including power systems, are increasing rapidly.
False Data Injection Attacks (FDIAs) are among the attacks that have been demonstrated to …
False Data Injection Attacks (FDIAs) are among the attacks that have been demonstrated to …
A survey on applications of machine learning for optimal power flow
F Hasan, A Kargarian… - 2020 IEEE Texas Power …, 2020 - ieeexplore.ieee.org
Optimal power flow (OPF) is at the heart of many power system operation tools and market
clearing processes. Several mathematical and heuristic approaches have been presented in …
clearing processes. Several mathematical and heuristic approaches have been presented in …
Exploiting vulnerabilities of load forecasting through adversarial attacks
Load forecasting plays a critical role in the operation and planning of power systems. By
using input features such as historical loads and weather forecasts, system operators and …
using input features such as historical loads and weather forecasts, system operators and …