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dc.contributor.editorArshad, Arshad
dc.date.accessioned2023-11-30T20:52:29Z
dc.date.available2023-11-30T20:52:29Z
dc.date.issued2023
dc.identifierONIX_20231130_9783036594101_236
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/128784
dc.description.abstractThere 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::A The Arts::AT Performing arts::ATF Films, cinemaen_US
dc.subject.classificationthema EDItEUR::A The Arts::AT Performing arts::ATJ Televisionen_US
dc.subject.otheraugmented reality
dc.subject.othercybersecurity
dc.subject.othersmart city
dc.subject.othersystematic literature review
dc.subject.othercyber security for smart cities
dc.subject.othercommunication wireless network
dc.subject.otherman-in-the-middle (MITM) attack
dc.subject.othernetwork intrusion detection system (NIDS)
dc.subject.othermalicious URLs
dc.subject.othercyber threat intelligence
dc.subject.otherensemble learning
dc.subject.otherinternet security
dc.subject.otherconvolution neural network
dc.subject.otherdeep learning
dc.subject.otherInternet of Things
dc.subject.otherintrusion detection
dc.subject.otherpath planning
dc.subject.otherMax-Min Ant Colony Optimization
dc.subject.otherdifferential evolution
dc.subject.otherCauchy mutation
dc.subject.othermalware detection
dc.subject.othermalware visualization
dc.subject.othertransfer learning
dc.subject.othernetwork traffic
dc.subject.otherexplainable AI
dc.subject.othercyber security
dc.subject.othertime series
dc.subject.otherfractal analysis
dc.subject.otherfractal dimension
dc.subject.otherHurst exponent
dc.subject.otherscaling exponent
dc.subject.othercyberattacks
dc.subject.otherelectricity theft detection
dc.subject.othersmart grids
dc.subject.otherrobustness
dc.subject.othersmart meters
dc.subject.otherTomek links
dc.subject.otherLevenberg–Marquardt backpropagation
dc.subject.otherprotection sensor
dc.subject.otherBayesian optimization
dc.subject.othermodular multilevel converter
dc.subject.othercomputer networks
dc.subject.othercyber attack
dc.subject.othersignal detection
dc.subject.othermachine learning
dc.subject.othersmart grid
dc.subject.othertransformer neural network
dc.subject.otherconvolutional neural network
dc.titleCybersecurity Issues in Smart Grids and Future Power Systems
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-9411-8
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036594101
oapen.relation.isbn9783036594118
oapen.pages242
oapen.place.publicationBasel


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