A Single Detection And Diagnosis Algorithm For Electrical Faults in a Five-Phase Permanent Magnet Synchronous Motor Drive
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Abstract
In all processing and manufacturing industries, approximately half of the operating cost is contributed to the maintenance process. Due to high reliability, and fault-tolerant capability, five-phase Permanent Magnet Synchronous Motors (5ϕ-PMSM) are commonly used in high-power and fault-tolerant applications. Early-stage detection and diagnosis of faults can reduce maintenance costs. This paper proposes a single algorithm for detecting and diagnosing electrical faults such as inter-turn short circuit faults, phase-to-phase faults, phase-to-ground to ground faults, and open circuit faults in a 5ϕ-PMSM drive. The discrete wavelet transforms and statistical parameters extract the fault features from the normalized stator currents under normal and faulty conditions. A fuzzy logic system is adopted to diagnose electrical faults and faulty phases. Since the algorithm uses normalized stator currents for fault detection and diagnosis, it can be used for detecting and diagnosing electrical faults in 5ϕ-PMSM drive with any capacity. The time of fault detection and diagnosis process is less than two cycles of stator current. Finally, the proposed algorithm is experimentally validated using Raspberry Pi.
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