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Smart Energy and Electric Power Systems
Current Trends and New Intelligent Perspectives
Reviews the key technologies and methods necessary to utilize AI/ML in modern energy systems
Sanjeevikumar Padmanaban (Edited by), Jens Bo Holm-Nielsen (Edited by), Kayal Padmanandam (Edited by), Rajesh Kumar Dhanaraj (Edited by), Balamurugan Balusamy (Edited by)
9780323916646, Elsevier Science
Paperback / softback, published 23 September 2022
226 pages, Approx. 100 illustrations
22.9 x 15.2 x 1.5 cm, 0.36 kg
Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing. Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector.
1. Introduction: Artificial intelligence and Smart Power Systems
2. Integrated Architecture of Machine Learning and Smart Power System
3. Challenges and issues in Power Systems
4. Load shedding and related techniques to solve the power crisis
5. ML in distributed energy resources and prosumers market
6. ML-based electricity demand prediction
7. Applying ML to determine the power outage
8. Predictive and Prescriptive analytics for component fault detection
9. Balancing demand and supply of electricity with machine learning
10. Preventive care of grid hardware with anomaly detection
11. AI-based Smart feeder monitoring system
12. Algorithms for buss loss and reliability indices calculations
13. ML-based security solutions to protect smart power systems
14. Cyber-attacks ,security data detection, and critical loads in the power systems
15. Integration of AI/ML into the energy sector: Case Studies
Subject Areas: Electric motors [THRM]
