IoT-enabled intelligent fault detection and rectifier optimization in wind power generators

Abstract

Optimizing PMSG systems in wind power with IoT is vital for energy efficiency and reliability. However, current IoT-based methods struggle with non-linear and non-stationary signals, leading to suboptimal performance and increased risks. To address these challenges, we propose ECS-Net, a novel framework that integrates Empirical Mode Decomposition (EMD), 1D Convolutional Neural Networks (1DCNN), and the Sparrow Search Algorithm (SSA), enhanced by IoT-enabled real-time monitoring. ECS-Net decomposes complex input signals using EMD, accurately detects faults through 1DCNN, and optimizes rectifier parameters via SSA, all within a responsive and adaptive framework. Experimental results demonstrate that ECS-Net achieves a fault detection accuracy of 93.7%, while reducing energy loss by 18.5% and thermal stress by 17.8% compared to existing methods. These improvements significantly enhance the longevity and stability of PMSG systems, especially in the dynamic and unpredictable environments of wind power generation.The integration of IoT technology within ECS-Net not only enables real-time monitoring and adaptive optimization but also sets a foundation for more intelligent and efficient renewable energy systems. This work marks an important advancement in applying signal processing and optimization techniques to wind energy, offering a scalable and effective solution for future developments.

Keywords

• IoT
• Wind power
• Fault detection
• PMSG optimization
• Signal processing
• Sparrow Search Algorithm