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Real-time data fetching approach for performance evaluation of a DFIG wind power generation system using an
R Sitharthan1, M Rajesh2, R Senthil Kumar3
1Centre for Smart Grid Technologies, Vellore Institute of Technology, Chennai, Tamil Nadu, India. sitharthan.r@vit.ac.in.
Scientific Reports
|November 18, 2025
Summary
This study introduces an IoT-based system for real-time wind energy analysis. It enables predictive performance evaluation of wind power generation systems, enhancing grid reliability.
Area of Science:
- Electrical Engineering
- Renewable Energy Systems
- Internet of Things (IoT)
Background:
- Increasing wind energy integration necessitates advanced methods for system reliability and performance evaluation.
- Current methods may lack real-time data processing and predictive capabilities for wind power generation systems.
- Ensuring the efficiency and stability of the power grid with diverse renewable sources is a critical challenge.
Purpose of the Study:
- To present an IoT-based real-time data collection and analysis method for Wind Power Generation Systems (WPGS).
- To develop an intelligent, IoT-enabled wind emulator for assessing turbine performance under varied conditions.
- To leverage cloud-based analytics for predictive analysis and performance evaluation of WPGS.
Main Methods:
- Utilized an IoT-enabled wind emulator with a 1 kW Doubly-Fed Induction Generator (DFIG) and Brushless DC (BLDC) motor.
- Developed a wind turbine model on the VEE Pro platform, integrating IoT-NodeRed, cloud API, and FPGA controller.
- Achieved real-time synchronization between global weather data and emulator control with a latency of 180 ms.
Main Results:
- Experimental results demonstrated 87% model accuracy (Mean Absolute Percentage Error) between theoretical and emulator power outputs.
- The system achieved a 95% health index reliability.
- A near-unity grid power factor of 0.999 was recorded.
Conclusions:
- The proposed IoT-based system offers a cost-effective, scalable, and adaptable solution for real-time wind energy analysis.
- This approach enhances the evaluation of Wind Power Generation System performance under dynamic conditions.
- The findings support ongoing research and development in grid integration of renewable energy sources.
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