AI Applications to Power Systems

• Other Authors: Tjing Lie, Tek (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Technology: general issues; History of engineering & technology; 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; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.