Performance of Fault Severity Estimation in 7-Phase Electrical Machines under Noisy Conditions

This work proposes a method for estimating fault severity in the presence of noise using the measured currents for a 7-phase electrical machine. The method is based on analytical models in stationary reference frames and analysis of the DC and fundamental components in the four fictitious machines. The slope of the decision function from the CUSUM algorithm, which will be noticeably different depending on the fault severity, is used to assess the performance of the fault severity estimation rapidly. The effects on the decision function’s slope of the fault severity estimation for different noise levels are evaluated. The simulation results show that even in presence of high noise levels, the decision function is an efficient fault estimation indicator. When the noise level is high, the decision function and its slope are noisier. Conversely, the decision function and its slope are less noisy when the noise level is low. The results also show that for the three fault types under study (gain fault, phase shift fault, and mean value fault), the current components of the fictitious machines in the stationary frames have distinct robustness to noise.