Week 1: Project Overview

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 micro wind turbine with turbine blades mechanically connected to a generator. Sensors attached to an ESP32 microprocessor will measure the electrical power produced by the turbine. The microcontroller will wirelessly provide real-time data to a dashboard that can be seen on a smartphone or other device.

Key parameters to be measured include:

  • RPM
  • Voltage
  • Current
  • Power
MATLAB will be used to log and evaluate the gathered data, producing performance curves and time-based charts. Simulink will also be used to create a digital twin of the system, which will enable comparisons between simulated behaviour and actual experimental outcomes.
A low-power LED is used as a visual electric load, proving successful energy generation.

System Flow:
  • Airflow from the desk fan drives the wind turbine
  • The turbine rotates a generator to produce electricity
  • Voltage and current sensors measure electrical output
  • an ESP32 processes sensor data and transmits it via Wi-Fi
  • Data is displayed on a live dashboard and logged for MATLAB analysis
  • A simulink model is used to simulate and validate system behavior.


Component Selection.


These components were chosen to ensure reliable measurements while keeping the system safe and low-voltage.




Initial Risk Assessment:

A few potential risks were indentified during the planning stage
  • Low generated voltage/current: mitigated by selecting a higher-output turbine generator and using a DC-DC boost converter.
  • Noisy sensor readings: mitigated through filtering, smoothing capacitors, and appropriate sampling rates.
  • Mechanical instability: mitigated by secure mounting and balanced turbine blades.
  • Wi-Fi communication issues: mitigated through local serial monitoring and data buffering.
Summary.

Week 1 successfully established the project scope, system architecture, and component selection. The groundwork has been laid for mechanical construction and sensor integration in the coming weeks.




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