FAULT DIAGNOSIS OF WIND TURBINES BASED ON SMOTETOMEK OVERSAMPLING METHOD AND DOMAIN ADAPTIVE TRANSFER LEARNING
The installed capacity of wind power has grown significantly in recent years,and wind power accounts for an increasing proportion of the total generation capacity,while its fault process can pose a greater threat to the safety and stability of grid operation,so it is important to accurately diagnose and predict the faults occurring on wind turbines.SCADA data-driven fault diagnosis algorithms have been widely researched and applied,however,the high imbalance in the distribution of the number of SCADA normal and fault data poses a major challenge for establishing a high-performance fault diagnosis model.In order to obtain a fault diagnosis model that can accurately give fault categories on unbalanced data,this paper proposes a fault diagnosis algorithm framework that combines SMOTETomek oversampling method with domain adaptive migration learning.The data is first sampled into two-dimensional temporal window data using sliding window sampling technique,and then SMOTETomek oversampling operation is executed on this basis to retain and enrich the complete temporal fault features.To address the problem of noise information introduced by the oversampling algorithm,this paper introduces a domain adaptive migration learning algorithm to extract invariant features between the original data and the oversampled data,so that the noise information introduced by the oversampling algorithm can be filtered out.Experimental results in a real wind farm in China show that the proposed method can complete model training on highly unbalanced data,accurately identify each type of fault and accurately give the time window of the fault process,and the diagnostic performance is significantly better than that of the model obtained based on the previously commonly used oversampling method.
万方数据
维普
