Smart Technique for Induction Motors Diagnosis by Monitoring the Power Factor Using Only the Measured Current

This paper is concerned with accurate, early and reliable induction motor IM fault detection and diagnosis using an enhanced power parameter measurement technique. IM protection devices typically monitor the motor current and/or voltage to provide the motor protection from e.g. current overload, over/under voltage, etc. One of the interesting parameters to monitor is the operating power factor (PF) of the IM which provides better under-load protection compared to the motor current based approaches. The PF of the motor is determined by the level of the current and voltage that are drawn, and offers non-intrusive monitoring. Traditionally, PF estimation would require both voltage and the current measurements to apply the displacement method. This paper will use a method of determining the operating PF of the IM using only the measured current and the manufacturer data that are typically available from the nameplate and/or datasheet for IM monitoring. The novelty of this work lies in detecting very low phase imbalance related faults and misalignment. Much of the previous work has dealt with detecting phase imbalance faults at higher degrees of severity, i.e. voltage drops of 10% or more. The technique was tested by empirical measurements on test rig comprised a 1.1 kW variable speed three phase induction motor with varying output load (No load, 25%, 50%, 75% and 100% load). One common faults was introduced; imbalance in one phase as the electrical fault The experimental results demonstrate that the PF can be successfully applied for IM fault diagnosis and the present study shows that severity fault detection using PF is promising. The proposed method offers a potentially reliable, non-intrusive, and inexpensive CM tool which can be implemented with real-time monitoring systems

[1]  S. Ahmed,et al.  Detection of Rotor Slot and Other Eccentricity-Related Harmonics in a Three-Phase Induction Motor with Different Rotor Cages , 2001, IEEE Power Engineering Review.

[2]  Lie Xu,et al.  Dynamic Modeling and Control of DFIG-Based Wind Turbines Under Unbalanced Network Conditions , 2007, IEEE Transactions on Power Systems.

[3]  A. J. Marques Cardoso,et al.  Airgap Eccentricity Fault Diagnosis, in Three-Phase Induction Motors, by the Instantaneous Power Signature Analysis , 1988 .

[4]  António J. Marques Cardoso,et al.  Airgap-Eccentricity Fault Diagnosis, in Three-Phase Induction Motors, by the Complex Apparent Power Signature Analysis , 2008, IEEE Transactions on Industrial Electronics.

[5]  N. Mohan,et al.  Control of a Doubly Fed Induction Wind Generator Under Unbalanced Grid Voltage Conditions , 2007, IEEE Transactions on Energy Conversion.

[6]  Zhe Zhang,et al.  Online rotor mixed fault diagnosis way based on spectrum analysis of instantaneous power in squirrel cage induction motors , 2004 .

[7]  A Ukil,et al.  Estimation of Induction Motor Operating Power Factor From Measured Current and Manufacturer Data , 2011, IEEE Transactions on Energy Conversion.

[8]  W. T. Thomson,et al.  Vibration and current monitoring for detecting airgap eccentricity in large induction motors , 1986 .

[9]  Antonio J. Marques Cardoso,et al.  Predicting the level of airgap eccentricity in operating three-phase induction motors, by Park's vector approach , 1992, Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting.

[10]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

[11]  B. Banerjee,et al.  Assessment of Voltage Unbalance , 2001, IEEE Power Engineering Review.