IoT, Embedded Systems, Renewable Energy monitoring

After completing the MATLAB analysis, I focused on developing a Simulink model to validate the analytical approach through visual simulation. The objective was to show that the MATLAB-developed multi-turbine grid integration techniques could be effectively applied in a block-diagram simulation environment, offering a supplementary validation technique and laying the groundwork for possible real-time implementation. Simulink was selected because, in contrast to pure code, it allows for the visual depiction of system design, which facilitates the understanding of signal flow and data connections. The next natural step after this software-focused research would be to deliver Simulink models to embedded hardware for real-time control applications. Implementation Approach: Model Architecture The Simulink model was designed with a hierarchical structure representing the complete wind farm system: Turbine layer (4 constant Blocks): Represents individual turbine power outputs Turbine 1: 1....

What I did: With the basic MATLAB analysis completed in week 3, this phase focused on a deeper investigation of the multi-turbine grid integration problems identified earlier. I need to test multiple scenarios to understand which factors most affect grid stability, power output, and system efficiency. Test conditions: Scenario 1: different turbine sizes 4 Turbines ranging from 0.4m to 1 m in diameter All turbines are exposed to the same wind speed of 5 m/s. Tests how physical size affects power contribution Scenario 2: different wind speed All turbines are 0.7m in diameter. Wind speeds range from 3 m/s to 7.5 m/s. Scenario 3: the perfect conditions All turbines and wind speeds stay constant for all turbines 0.6 m in diameter, 4.5 m/s wind This will be used as a baseline for comparison Scenario 4: worst case Extreme differences 0.3 m-1.2 m in diameter wind from 2 m/s to 8 m/s This will be used as a stress test for grid management. Key findings: Power variation analysis: Scenario 3 achie...

Writing the MATLAB script I developed two main MATLAB scripts: wind_turbine_analysis.m - Single turbine simulation using dataset multi_turbine_simulation.m - Multiple turbine wind farm simulation The first script loads the dataset (Voltage, Current, RPM, Power) and performs a comprehensive analysis as seen in the command window. The script automatically calculates mean, standard deviation, min and max for all variables, which gives us an insight into the system's variability and behaviour. What the time series shows. The voltage varies between approximately 5.8V and 11.4V over 60 seconds, following a smooth pattern that simulates varying wind conditions. The current response shows an inverse relationship; when the voltage is high, the current seems to be lower, which is realistic for a system with varying load conditions. Power output ranges from around 0.2W to nearly 5W, staying withing the specified 0-5W range. RPM and Power Relationship The scatter plot shows a clear positive co...

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 simul...

Simulated dataset created in order to start writing MATLAB code and test plots. A Simulink model can also be created to represent the expected behaviour of the wind turbine system and enable early validation of the data processing and analysis workflow before hardware integration. Used Claude. AI to create a dataset within these values. Once the dataset was created, I then remade it twice to fit the range and added another column to facilitate power. Once the dataset was created at intervals of 1 second and a range of 0-60. I can now use this dataset to simulate a test and plot graphs, write the MATLAB codes, create a Simulink model and design the dashboard online.

Designing and implementing a small-scale wind energy system that combines data analytics, embedded sensing, Internet of Things connectivity, and simulation-based validation is the goal of this project. The motivation behind this project is to gain hands-on experience with a complete engineering workflow, starting from physical energy generation and extending to real-time monitoring, MATLAB analysis, and digital twin modelling. Because wind energy is inherently changeable, it was selected as the focal point because it is perfect for showcasing real-time sensing, data processing, and system modelling. By using a controlled wind source such as a desk fan, the system can be tested repeatedly under different conditions while remaining safe and practical for indoor experimentation. This study closely relates to contemporary engineering techniques utilised in smart systems, predictive modelling, and renewable energy monitoring. Project Overview The project entails constructing a prototype mic...