Performance-Oriented Electric Motors Diagnostics in Modern Energy Conversion Systems
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This paper presents the analysis of a performance-oriented electric motors diagnostics in modern energy conversion system. With increased demand for electrical energy in world industries, the population of energy conversion devices such as generators/motors has greatly increased. As emerging and not being a mature enough technology in the application of renewable energy conversion or electric-drive transportation, the protection and diagnosis of electric motors have been extensively studied for safety and reliability. Meanwhile, motor phase currents commonly involve random noise components generated by harsh energy system environments, low- and high-order harmonics interferences caused by power inverters and fast switching devices, and various other design imperfections. Therefore, it is quite challenging to model the overall noise content and eliminate the disturbance while detecting motor fault signatures. Due to the inherent random variation of motor noise statistics, the noise model and elimination strategy should also be adaptively updated according to instantaneous noise conditions through which detection can be done with predefined performance expectation. Several successful solutions in the literature have managed to perform a diagnosis under certain noise conditions; however, a detailed performance and adaptability analysis covering arbitrary noise variation has not been satisfactorily addressed. This paper mainly deals with performance oriented threshold design strategies for fault signature detection utilizing the noise statistics of the motor phase current signal. The proposed solution is generalized to cover arbitrary Gaussian noise variations and derive the optimal form of the threshold that satisfies user's prior detection quality expectations. The mathematical derivations are proved through statistical theory and the experimental verifications are performed by using a 3-hp motor setup. © 2011 IEEE.
author list (cited authors)
Choi, S., Akin, B., Rahimian, M. M., & Toliyat, H. A.