Individual Research Contribution to group project

• 09/02/2025 – Project Proposal & Initial Brainstorming

  • Proposed the project on integrating AI and IoT into Francis turbines to enhance energy efficiency and turbine health monitoring.

  • Conducted a brainstorming session to identify key areas of focus.

  • Researched and compiled reliable information on water turbines, including their advantages and disadvantages. Pros: Generates renewable green energy, contributes to sustainability. Cons: The Francis turbine lacks real-time data feedback, making it unable to adjust water flow dynamically, leading to reduced efficiency.

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  14/02/2025 – Research on Francis Turbine and AI-IoT Integration

  • Used ChatGPT to gather concise and structured information about:

    • The working principles of a Francis turbine.

    • The potential of AI and IoT in improving energy production efficiency and turbine maintenance.

  • Investigated how lack of real-time data impacts performance, such as:

    • Poor energy production efficiency due to the inability to regulate flow dynamically.

    • Health degradation of the turbine from excess stress and inefficiencies.

    • Excess power generation issues, leading to overvoltage, which can damage transmission lines and electrical equipment.

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  25/02/2025 – Refinement of Project Idea, Gap & Goal

  • Based on feedback, refined key aspects of the project:

    • Ideal situation: A system where AI and IoT optimize energy output efficiently.

    • Gap: Current Francis turbines lack real-time adaptability, reducing energy efficiency.

    • Goal: To implement AI-IoT integration for real-time monitoring and adaptive control.

  • Improved the purpose statement to be more concise and accurately defined.

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  04/03/2025 – In-depth Research on AI in Turbines

  • Conducted research using reliable sources on AI applications in turbine technology:

    • Machine learning & deep learning can be used to predict water flow and energy demand.

    • IoT sensors collect real-time data, allowing automated control of water valve geometry.

    • Goal: Ensure maximum energy efficiency by adjusting flow dynamically to match demand.

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  07/03/2025 – Consequences of Excess Energy & Technology Comparison

  • Analyzed what happens when the Francis turbine produces excess energy:

    • Grid instability & overvoltage can damage electrical components.

    • Reduced turbine lifespan due to operational strain.

  • Compared the proposed AI-IoT solution to the existing Digital Twin technology:

    • Digital Twin: Simulates turbine performance but lacks real-time adaptation.

    • AI-IoT integration: Provides real-time control and predictive maintenance, leading to higher efficiency and reliability.

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  11/03/2025 – Evaluation of AI-IoT Integrated Francis Turbine

  • Started evaluating the feasibility of integrating AI and IoT with Francis turbines.

  • Identified potential challenges and shortcomings:

    • Data privacy concerns with real-time monitoring.

    • High expertise requirements, leading to the need for a large number of skilled professionals.

    • High initial costs for implementation and training.

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  14/03/2025 – Refining Presentation & Slide Design

  • Enhanced slide presentation by:

    • Adding animations and visual aids to explain concepts more effectively.

    • Reducing text density and focusing on key points.

  • Refined speech content to improve flow and clarity.

  • Ensured parallel structure for better comprehension and logical progression of ideas.

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  18/03/2025 – Project Presentation

  • Delivered the presentation, explaining the AI-IoT integration concept for Francis turbines.

  • Discussed real-world implications, including potential improvements in energy efficiency, cost savings, and sustainability.

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  21/03/2025 – Grading Peer Presentations

  • Evaluated other groups' presentations, likely providing feedback on their research depth, clarity, and presentation skills.

  • Compared different project approaches and assessed how well they conveyed their concepts.