Faulty diagnostics model for wind power plant application using AI

Abstract

Researchers desperately ought to build an efficient turbine problem detection & simulation environment given the quick increase in wind energy capacity and the steadily rising operational cost duration of wind generators. The two aspects of fault identification & fault prediction are employed to describe the primary problem features of wind generators. We examine and synthesize the studies on fault detection techniques that rely on vibrating, electrical impulse evaluation, and pattern matching to address the challenging difficulties of defect detection. During the same period, we highlight the computationally efficient, constraints, and potential directions of distinct approaches. We review recent study developments & suggest a defect forecasting model that utilizes a physical breakdown prototype with data analysis theory fused depending on the mechanics and electronic subsystem degradation features of wind energy. In this study, we employ the Internet of Things (IoT) on Deep Learning (DL) architecture to forecast and identify wind energy generation issues. The testing findings demonstrate the method's ability to diagnose faults logically and forecast their types.

Keywords

• Wind power capacity
• Fault diagnosis
• Wind turbines
• Data-driven model fusion