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AI Applications to Power Systems
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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.
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Keywords
- 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
- History of engineering & technology
- 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
- Technology, engineering, agriculture
- Technology: general issues
- total variation smoothing
Links
DOI: 10.3390/books978-3-0365-3654-5Editions