Hybrid Gaussian process regression and Fuzzy Inference System based approach for condition monitoring at the rotor side of a doubly fed induction generator

Abstract

With the applications of Machine Learning (ML) in condition monitoring (CM) of wind turbines (WTs), regression-based approaches are mainly for WT power curve modelling to evaluate its performance. A fitted performance curve (PC) of power versus wind speed is prevailing and straightforward for WT performance monitoring. As WT operations could be affected by multiple parameters. It is difficult to identify the fault type merely by a power curve. Particularly at the rotor side of doubly fed induction generator (DFIG), there are miscellaneous electrical disturbances that have their distinct effects on current signals. Hence, by merely processing the rotor currents, a PC of standard deviation of currents versus wind speed can be employed for CM of rotor-side faults. A non-parametric regression model, Gaussian process regression (GPR), is used to predict the PC under normal operation, and afterwards, Fuzzy Inference System (FIS) is applied for fault diagnosis. Therefore, the paper proposes a hybrid GPR and FIS approach towards condition monitoring (CM) and fault diagnosis at the rotor side of a DFIG by merely processing rotor currents through various signal processing techniques, where miscellaneous electrical disturbances can be recognized and localized. The comparative results show that the hybrid approach has superiority over two common ML classifiers, support vector machine (SVM) and decision tree (DT), for fault diagnosis under both test cases including drift faults and excluding drift faults. By disregarding drift faults, this hybrid approach is validated to be efficient for a real-time application with overall promising performance metrics in terms of accuracy (98.29%), dependability (98.12%), and security (100%). The diagnosed results on individual faults and research shortcomings are also discussed.