Stator current analysis for preventive maintenance is an essential tool for industries. Its use is intended to serve three levels of analysis: supervision, diagnosis and monitoring of the state of damage to equipment. The main objective of this paper is to propose a diagnosis and monitoring method based on the analysis of the stator current for the detection and localization of a short-circuit fault occurred on the inverter (insulation fault of a phase). The proposed method uses signal processing techniques (temporal and spectral domain) combined with a machine learning technique to locate the faulty phase. The study begins with the application of the fast Fourier transform (FFT) to detect the harmonic characterizing the short-circuit fault of a phase of the inverter, and then a statistical study based on the skewness calculation is performed at the stator current spectrum for each phase. The second part of the study applies the random forest RF to locate the faulty phase. The features used to train the RF model are the amplitude of the harmonic f150 and the value of the skewness. The results obtained by RF show a good performance with a very high classification rate equal to 98.98%.
Citation
BILAL DJAMAL EDDINE Cherif ,
HILAL RAHALI ,
Mostefa TABBAKH ,
SENINETE Sara, ,(2022), Detection and Localization of Phase Insulation Fault in a Set Inverter-Induction Motor,Fifth International Conference on Electrical Engineering And Control Applications ICEECA’22,Khenchela- Algeria
