AI approaches to smart and sustainable power systems:
Today, the global power demand relies on a delicate balance between conventional and renewable energy systems, necessitating both efficient power generation and the effective utilization of these energy resources through appropriate energy storage solutions. Integrating microgrid systems into the ut...
Gespeichert in:
| Weitere Verfasser: | , , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey PA, USA
IGI Global
[2024]
|
| Schriftenreihe: | Advances in computational intelligence and robotics (ACIR) book series
|
| Schlagworte: | |
| Online-Zugang: | DE-91 DE-898 DE-1050 Volltext |
| Zusammenfassung: | Today, the global power demand relies on a delicate balance between conventional and renewable energy systems, necessitating both efficient power generation and the effective utilization of these energy resources through appropriate energy storage solutions. Integrating microgrid systems into the utility grid has become a critical facet of modern power systems. The intermittent and unpredictable nature of these energy sources poses a formidable challenge for academic scholars and researchers. This compels them to explore under-investigated areas, including energy source estimation, storage elements, load pattern prediction, coordination among distributed sources, and the development of energy management algorithms for precise and efficient control.AI Approaches to Smart and Sustainable Power Systems tackles these issues using cutting-edge AI techniques. It examines the most effective methods to optimize voltage, frequency, power, fault diagnosis, component health, and overall power system quality and reliability. AI empowers predictive and preventive maintenance for a sustainable energy future. The book focuses on emerging research areas, including renewable energy, power flow calculations, demand scheduling, real-time performance validation, and AI integration into modern power systems, accompanied by insightful case studies. |
| Beschreibung: | 1 Online-Ressource (xxii, 432 Seiten) Illustrationen |
| ISBN: | 9798369315873 |
| DOI: | 10.4018/979-8-3693-1586-6 |
Internformat
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| 520 | |a Today, the global power demand relies on a delicate balance between conventional and renewable energy systems, necessitating both efficient power generation and the effective utilization of these energy resources through appropriate energy storage solutions. Integrating microgrid systems into the utility grid has become a critical facet of modern power systems. The intermittent and unpredictable nature of these energy sources poses a formidable challenge for academic scholars and researchers. This compels them to explore under-investigated areas, including energy source estimation, storage elements, load pattern prediction, coordination among distributed sources, and the development of energy management algorithms for precise and efficient control.AI Approaches to Smart and Sustainable Power Systems tackles these issues using cutting-edge AI techniques. It examines the most effective methods to optimize voltage, frequency, power, fault diagnosis, component health, and overall power system quality and reliability. AI empowers predictive and preventive maintenance for a sustainable energy future. The book focuses on emerging research areas, including renewable energy, power flow calculations, demand scheduling, real-time performance validation, and AI integration into modern power systems, accompanied by insightful case studies. | ||
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| indexdate | 2024-12-17T19:01:36Z |
| institution | BVB |
| isbn | 9798369315873 |
| language | English |
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| spelling | AI approaches to smart and sustainable power systems L. Ashok Kumar, S. Angalaeswari, K. Mohanasundaram, Ramesh Bansal, Arunkumar Patil Artificial intelligence approaches to smart and sustainable power systems Hershey PA, USA IGI Global [2024] © 2024 1 Online-Ressource (xxii, 432 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Advances in computational intelligence and robotics (ACIR) book series Today, the global power demand relies on a delicate balance between conventional and renewable energy systems, necessitating both efficient power generation and the effective utilization of these energy resources through appropriate energy storage solutions. Integrating microgrid systems into the utility grid has become a critical facet of modern power systems. The intermittent and unpredictable nature of these energy sources poses a formidable challenge for academic scholars and researchers. This compels them to explore under-investigated areas, including energy source estimation, storage elements, load pattern prediction, coordination among distributed sources, and the development of energy management algorithms for precise and efficient control.AI Approaches to Smart and Sustainable Power Systems tackles these issues using cutting-edge AI techniques. It examines the most effective methods to optimize voltage, frequency, power, fault diagnosis, component health, and overall power system quality and reliability. AI empowers predictive and preventive maintenance for a sustainable energy future. The book focuses on emerging research areas, including renewable energy, power flow calculations, demand scheduling, real-time performance validation, and AI integration into modern power systems, accompanied by insightful case studies. Artificial intelligence Industrial applications Clean energy Technological innovations Renewable energy sources Technological innovations Sustainable development Angalaeswari, S. 1981- edt Bansal, Ramesh C. (DE-588)1243188707 edt K., Mohanasundaram 1977- edt Kumar, L. Ashok 1976- (DE-588)1082510823 edt Patil, Arunkumar 1985- edt Erscheint auch als Druck-Ausgabe, Hardcover 979-8-3693-1586-6 https://doi.org/10.4018/979-8-3693-1586-6 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | AI approaches to smart and sustainable power systems Artificial intelligence Industrial applications Clean energy Technological innovations Renewable energy sources Technological innovations Sustainable development |
| title | AI approaches to smart and sustainable power systems |
| title_alt | Artificial intelligence approaches to smart and sustainable power systems |
| title_auth | AI approaches to smart and sustainable power systems |
| title_exact_search | AI approaches to smart and sustainable power systems |
| title_full | AI approaches to smart and sustainable power systems L. Ashok Kumar, S. Angalaeswari, K. Mohanasundaram, Ramesh Bansal, Arunkumar Patil |
| title_fullStr | AI approaches to smart and sustainable power systems L. Ashok Kumar, S. Angalaeswari, K. Mohanasundaram, Ramesh Bansal, Arunkumar Patil |
| title_full_unstemmed | AI approaches to smart and sustainable power systems L. Ashok Kumar, S. Angalaeswari, K. Mohanasundaram, Ramesh Bansal, Arunkumar Patil |
| title_short | AI approaches to smart and sustainable power systems |
| title_sort | ai approaches to smart and sustainable power systems |
| topic | Artificial intelligence Industrial applications Clean energy Technological innovations Renewable energy sources Technological innovations Sustainable development |
| topic_facet | Artificial intelligence Industrial applications Clean energy Technological innovations Renewable energy sources Technological innovations Sustainable development |
| url | https://doi.org/10.4018/979-8-3693-1586-6 |
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