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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 online resource (156 p.)
Soggetto topico: History of engineering & technology
Technology: general issues
Soggetto non controllato: artificial neural networks
automatic P2P energy trading
cellular computational networks
change detection
contingency
data analytics
data mining
deep Q-network
deep reinforcement learning
dynamic mode decomposition
event detection
feature extraction
filtering
fuzzy system
inter-area oscillations
load flow prediction
long short-term delayed reward
machine learning
machine-learning
manta ray foraging optimization algorithm
Markov decision process
modal analysis
multi-objective function
n/a
optimal power flow
optimization
optimization techniques
power quality
radial networks
reduced order modeling
self-healing grid
signal processing
situation awareness
steady-state security assessment
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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