Cybersecurity Issues in Smart Grids and Future Power Systems

doi:10.3390/books978-3-0365-9411-8

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https://mdpi.com/books/pdfview/book/8250

Contributor(s)

Arshad, Arshad (editor)

Language

English

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Abstract

There has been an increased interest in renewable energy sources in the last few decades. Modern power systems rely mainly on power electronic-based generation and loads, leading to the adoption of smart grids that leverage digital communication infrastructure. Smart grids have several advantages, including the potential to provide consumers with a continuous power supply, reduced line losses, enhanced renewable output and storage, consumer participation in electricity markets, and demand-side responsiveness. Future power systems, also known as smart grids, will rely more on renewable energy sources, such as solar and wind, as well as storage. Power electronic converters are used in renewable energy generation and storage. Each converter/inverter manufacturer has an algorithm for programming and optimizing hardware. Furthermore, these converters rely on communication protocols to respond to any signal from the system operator. As a result, cyber-attacks on these smart converters/inverters are a concern. Although numerous cyber–physical systems (CPS) have been presented, no universal CPS standard can be employed with various types of converters. This reprint is a collection of specialized work addressing cybersecurity challenges.

URI

https://directory.doabooks.org/handle/20.500.12854/128784

Keywords

augmented reality; cybersecurity; smart city; systematic literature review; cyber security for smart cities; communication wireless network; man-in-the-middle (MITM) attack; network intrusion detection system (NIDS); malicious URLs; cyber threat intelligence; ensemble learning; internet security; convolution neural network; deep learning; Internet of Things; intrusion detection; path planning; Max-Min Ant Colony Optimization; differential evolution; Cauchy mutation; malware detection; malware visualization; transfer learning; network traffic; explainable AI; cyber security; time series; fractal analysis; fractal dimension; Hurst exponent; scaling exponent; cyberattacks; electricity theft detection; smart grids; robustness; smart meters; Tomek links; Levenberg–Marquardt backpropagation; protection sensor; Bayesian optimization; modular multilevel converter; computer networks; cyber attack; signal detection; machine learning; smart grid; transformer neural network; convolutional neural network