Monitoring and diagnosis of faults in interior permanent magnet motors using discrete wavelet transform

This paper presents a novel application of the discrete wavelet transform (DWT) based algorithm for detecting and diagnosing various disturbances caused by electrical faults in the three-phase interior permanent magnet (IPM) motor. The DWT coefficients of fault currents of different levels of resolution using a selected mother wavelet are transformed to the root mean square (RMS) values through the determination of signal energies of different frequency bands of the DWT. The RMS values are then used in a rule-based classifier (RBC) in order to differentiate between different faulted and normal conditions. The criterion for the fault detection is the comparison of the DWT coefficients of the fifth level details (d
⁵
) of fault currents using a selected mother wavelet with a threshold determined experimentally during the healthy condition of the motor. The complete protection technique incorporating the proposed DWT based diagnosis algorithm is implemented in real-time using the ds1102 digital signal processor board for a laboratory 1-hp IPM motor. It is found that the proposed DWT based protection algorithm is very fast, and has responded at the instant or within one cycle of the fault occurrence in all cases of investigated faults.