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AI Applications to Power Systems



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Autore: Tjing Lie Tek Visualizza persona
Titolo: AI Applications to Power Systems Visualizza cluster
Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica: 1 electronic resource (156 p.)
Soggetto topico: Technology: general issues
History of engineering & technology
Soggetto non controllato: self-healing grid
machine-learning
feature extraction
event detection
optimization techniques
manta ray foraging optimization algorithm
multi-objective function
radial networks
optimal power flow
automatic P2P energy trading
Markov decision process
deep reinforcement learning
deep Q-network
long short-term delayed reward
inter-area oscillations
modal analysis
reduced order modeling
dynamic mode decomposition
machine learning
artificial neural networks
steady-state security assessment
situation awareness
cellular computational networks
load flow prediction
contingency
fuzzy system
change detection
data analytics
data mining
filtering
optimization
power quality
signal processing
total variation smoothing
Persona (resp. second.): Tjing LieTek
Sommario/riassunto: Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.
Titolo autorizzato: AI Applications to Power Systems  Visualizza cluster
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910566478903321
Lo trovi qui: Univ. Federico II
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