Development of Artificial Intelligence Based Real Time Maximum Power Point Tracking Controller for a Hybrid Renewable Energy System
- Indbinding:
- Paperback
- Sideantal:
- 274
- Udgivet:
- 8. februar 2023
- Størrelse:
- 152x15x229 mm.
- Vægt:
- 402 g.
- 8-11 hverdage.
- 11. december 2024
På lager
Forlænget returret til d. 31. januar 2025
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- Adgang til 70.000+ titler
- Ingen binding
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Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Development of Artificial Intelligence Based Real Time Maximum Power Point Tracking Controller for a Hybrid Renewable Energy System
An informative book that examines the use of artificial intelligence (AI) in renewable energy systems is "Development of Artificial Intelligence Based Real Time Maximum Power Point Tracking Controller for a Hybrid Renewable Energy System." The book, written by Mohammad Junaid Khan, explores the creation of an AI-based real-time maximum power point tracking (MPPT) controller.
The discussion of existing renewable energy sources and how they might be incorporated into a hybrid system opens the book. The fundamentals of maximum power point tracking and the conventional methods employed for this goal are then covered in depth. In order to create a real-time MPPT controller that can maximise the performance of the renewable energy system, the author uses AI approaches including neural networks and fuzzy logic systems.
The designed controller's performance analysis and experimental results are also included in the book, demonstrating how effective it is in comparison to other approaches. The author wraps off by talking about the potential of AI-based MPPT controllers to revolutionise the renewable energy industry.
For researchers, engineers, and students interested in the application of AI in renewable energy systems, "Development of Artificial Intelligence Based Real Time Maximum Power Point Tracking Controller for a Hybrid Renewable Energy System" is a great resource.
The discussion of existing renewable energy sources and how they might be incorporated into a hybrid system opens the book. The fundamentals of maximum power point tracking and the conventional methods employed for this goal are then covered in depth. In order to create a real-time MPPT controller that can maximise the performance of the renewable energy system, the author uses AI approaches including neural networks and fuzzy logic systems.
The designed controller's performance analysis and experimental results are also included in the book, demonstrating how effective it is in comparison to other approaches. The author wraps off by talking about the potential of AI-based MPPT controllers to revolutionise the renewable energy industry.
For researchers, engineers, and students interested in the application of AI in renewable energy systems, "Development of Artificial Intelligence Based Real Time Maximum Power Point Tracking Controller for a Hybrid Renewable Energy System" is a great resource.
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