AI Applications to Power Systems
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...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (156 p.) |
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(CKB)5680000000037591 (oapen)https://directory.doabooks.org/handle/20.500.12854/81232 (EXLCZ)995680000000037591 |
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Tjing Lie, Tek edt AI Applications to Power Systems Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 1 electronic resource (156 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Open access Unrestricted online access star 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. English Technology: general issues bicssc History of engineering & technology bicssc 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 3-0365-3653-1 3-0365-3654-X Tjing Lie, Tek oth |
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English |
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eBook |
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Tjing Lie, Tek |
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Tjing Lie, Tek |
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l t t lt ltt |
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Sonstige |
| title |
AI Applications to Power Systems |
| spellingShingle |
AI Applications to Power Systems |
| title_full |
AI Applications to Power Systems |
| title_fullStr |
AI Applications to Power Systems |
| title_full_unstemmed |
AI Applications to Power Systems |
| title_auth |
AI Applications to Power Systems |
| title_new |
AI Applications to Power Systems |
| title_sort |
ai applications to power systems |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2022 |
| physical |
1 electronic resource (156 p.) |
| isbn |
3-0365-3653-1 3-0365-3654-X |
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Not Illustrated |
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AT tjinglietek aiapplicationstopowersystems |
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(CKB)5680000000037591 (oapen)https://directory.doabooks.org/handle/20.500.12854/81232 (EXLCZ)995680000000037591 |
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