Fault Diagnosis of Wind Turbines using Scada Data by ML Techniques

Abstract

Fault diagnosis technology is crucial for the safety and stability of the operation of wind turbines. Efficient wind turbine fault diagnosis technology quickly identifies the types of failures in order to reduce the operating and maintenance costs of wind farms and improve the efficiency of power generation. The majority of wind farms currently use supervisory control and data acquisition (SCADA) systems to acquire operation and maintenance data. SCADA systems are rich in data pertaining to the working parameters of wind turbines. Moreover, fault diagnostic functionality is not common in SCADA systems. SVMs are a common intelligence technique used in the fault diagnostics of wind turbines. The choice of SVM parameters is essential for precise model classification. The penalty factor and kernel function parameter of SVM were optimi zed in this study along with the construction of the SSA-ABC wind turbine defect detection model using the Artificial Bee Colony (ABC), a brand-new and very effective optimization technique. Data are obtained from a wind farm SCADA system and, after pre-processing and feature selection, entail a faulting set. Evidence suggests that the ABC-SVM diagnostic model, which has a quick convergence rate and a potent optimization capability, effectively enhances the accuracy of wind turbine fault diagnosis when compared to the GS-SVM, GA-SVM, and PSO-SVM models. The ABC-SVM diagnostic model may also be utilized in real-world engineering applications to identify defects.