Real-Time Data Fetching Approach for Performance Evaluation of a DFIG Wind Power Generation System Using an IoT-Enabled Wind Emulator

Abstract The increased integration of wind energy into the power system network requires advanced testing and performance evaluation methods to ensure reliability and efficiency. This paper presents an IoT-based real-time data collection method for analyzing the performance of the Wind Power Generation System (WPGS) using an intelligent IoT-enabled wind emulator system. The proposed system uses IoT to gather comprehensive real-time wind data, which is processed by a digitally controlled emulator to accurately assess wind turbine responses under different wind conditions. The advantage of this system is that IoT and cloud-based data analytics enable predictive analysis of the WPGS, helping evaluate its behavior and performance under various real-time scenarios. The approach is tested with a wind emulator setup consisting of a 1kW Doubly-Fed Induction Generator (DFIG) connected to a Brushless DC (BLDC) motor, where the wind turbine model is developed on the VEE Pro platform, integrating an IoT-NodeRed, cloud API, and FPGA controller to simulate real-world wind conditions. Experimental results demonstrate up to 87% accuracy in modeling WPGS performance based on real-time wind profiles and achieve a 95% health index in predictive maintenance monitoring, greatly enhancing grid integration reliability. This study provides a cost-effective, scalable, and adaptable solution for real-time wind energy analysis, supporting ongoing research and development.