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Artificial Intelligence Techniques in Power Systems Operations and Analysis.
・ISBN 978-1-032-29492-6 paper GB£ 71.99
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| 著者・編者 | Singh, Nagendra / Tamrakar, Sitendra / Mewada, Arvind / Gupta, Sanjeev Kumar (eds.), |
|---|---|
| シリーズ | (Advances in Computational Collective Intelligence) |
| 出版社 | (Auerbach, UK) |
| 出版年月 | 2024 |
| ページ数 | 224 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A03-78314＞ |
解説
An electrical power system consists of a large number of generation, transmission, and distribution subsystems. It is a very large and complex system; hence, its installation and management are very difficult tasks. An electrical system is essentially a very large network with very large data sets. Handling these data sets can require much time to analyze and subsequently implement. An electrical system is necessary but also potentially very dangerous if not operated and controlled properly. The demand for electricity is ever increasing, so maintaining load demand without overloading the system poses challenges and difficulties.
Thus, planning, installing, operating, and controlling such a large system requires new technology. Artificial intelligence (AI) applications have many key features that can support a power system and handle overall power system operations. AI-based applications can manage the large data sets related to a power system. They can also help design power plants, model installation layouts, optimize load dispatch, and quickly respond to control apparatus. These applications and their techniques have been successful in many areas of power system engineering.
Artificial Intelligence Techniques in Power Systems Operations and Analysis focuses on the various challenges arising in power systems and how AI techniques help to overcome these challenges. It examines important areas of power system analysis and the implementation of AI-driven analysis techniques. The book helps academicians and researchers understand how AI can be used for more efficient operation. Multiple AI techniques and their application are explained. Also featured are relevant data sets and case studies.
Highlights include:
- Power quality enhancement by PV-UPQC for non-linear load
- Energy management of a nanogrid through flair of deep learning from IoT environments
- Role of artificial intelligence and machine learning in power systems with fault detection and diagnosis
- AC power optimization techniques
- Artificial intelligence and machine learning techniques in power systems automation