Gantt Chart and Expanded Scope

Building the Gantt Chart

Once the simulated dataset was constructed, the next step was to create a structured project timeline. I mapped out 12 tasks across 12 weeks, covering everything from system design to the final report and submission.

The full task flow can be seen in my notes. Rather than treating each task as completely separate. I deliberately overlapped different phases. This reflects how real engineering project work insights from one phase feed directly into the next; waiting for one task to finish before starting the next is inefficient.


Simulated Dataset – Why is it required?

From the notes I took this week, I confirmed that the dataset I created last week will serve as the foundation for several of the upcoming tasks
  • Writing and testing MATLAB code
  • Plotting RPM vs Power, Power per turbine, and Total farm output
  • Build Simulink models
  • Designing the IoT Dashboard
Rather than collecting real sensor data at this stage, the simulated dataset lets me continue testing and simulating the project digitally before testing it with hardware, which also lowers the risk of hardware delays blocking progress on the software and modelling side.


The RPM values in the dataset is grounded in the formula in my notes: RPM = (60 x V x TSR)/(Tt x D)

This formula will be used later to generate more realistic mini-turbine scenarios where each turbine has a different diameter or is exposed to different weather conditions



Discussion with Dr Purav – Expanded Scope

A significant development came from my meeting with Dr Purav. He pushed me to think beyond a single turbine simulation and consider a more challenging and real-world scenario: multiple wind farms feeding a single shared grid.

Possible test cases:

  • Several turbines are all generating power simultaneously.
  • Each turbine has a different RPM
  • All output feeds into a common grid.

These different conditions create a new set of problems that need to be addressed.
  • 1. Uneven Power Discrepancy: Different turbines producing at different rates means the total grid input is uneven and unpredictable. If one turbine spins faster than another, it contributes more power, which can cause an imbalance.
  • 2. Voltage Instability If turbines with different output voltages are connected directly to the same bus, the voltage at the grid connection point fluctuates, which can damage connected components or cause system failures.
  • 3. Inefficiency Without proper management, energy is lost in the mismatch between turbines. Some may be operating well below their optimal point, while others are overloaded.

Dr Purav's suggestion was to investigate how to make this project as efficient as possible. Some solutions I had in mind to increase efficiency:
  •  Power Aggregation — Combining outputs intelligently so the grid sees a stable, summed total rather than individual variable inputs
  •  DC-DC Converters — Using converters to regulate and standardise the voltage from each turbine before they connect to the shared bus
  •  Power Management Algorithms — Writing logic in MATLAB/Simulink that monitors each turbine's output and adjusts how they contribute to the grid in real time


What's next:

  • Start writing MATLAB scripts using the simulated dataset.
  • Generate initial plots: RPM vs Power, Power per turbine, Total farm output
  • Begin designing the multi-turbine simulation model in Simulink
  • Research DC-DC converter topologies suitable for small-scale wind energy aggregation




















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