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IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 1, MARCH 2005
Power Engineering Letters____________________________________________________ Diagnosis of Stator Short Circuits in Brushless DC Motors by Monitoring Phase Voltages M. A. Awadallah, Student Member, IEEE, and M. M. Morcos, Senior Member, IEEE
Abstract—Two adaptive fuzzy systems are developed for identification and location of short-circuit faults on one phase of the phasemodulation brushless dc motors. Motor performance is computed by a discrete-time numerical model. The waveform of line-to-neutral voltages summation is monitored for feature extraction. Diagnostic indices are derived by processing the characteristic signal using discrete Fourier transform (DFT) and short-time Fourier transform (STFT). Testing results show an acceptable diagnosing performance of the proposed scheme. Index Terms—Adaptive fuzzy techniques, fault diagnosis, fault location, phase-modulation (PM) brushless dc motors.
Fig. 1. Schematic diagram of the integrated drive system.
I. INTRODUCTION
W
INDING short circuits represent 30%–40% of electric machines’ faults. Faulty windings normally harbor high short-circuit currents that produce excessive heat loss and affect machine performance. The deviation of Park’s vector modulus of the line currents from circular to elliptic shape could detect the fault in three-phase induction motors [1]. Monitoring the supply-current waveform could diagnose the fault in voltagesource inverter-fed PM brushless dc motors, running without current chopping control [2]. This letter presents two independent adaptive neuro-fuzzy inference systems (ANFIS) for comprehensive diagnosis of the stator interturn shorts in brushless dc motors. A schematic diagram of the current-source inverter-fed motor is shown in Fig. 1. Healthy and faulty performances of the motor, which has a trapezoidal back electromotive force (EMF), are obtained through a numerical discrete-time model [3]. Summation of the line-to-neutral voltages is monitored and processed by discrete Fourier transform (DFT) and short-time Fourier transform (STFT) to derive appropriate diagnostic indices. II. SYSTEM PERFORMANCE
Performance characteristics of the 12-V, 1000-r/min, six-pole, 1/4-hp motor were obtained under normal operation through a discrete-time model. Normal conditions included perfect and imperfect commutations as well as noisy operation in order to avoid false-fault alarms in such cases. The shorted section of the faulty phase was modeled by an EMF source in Manuscript received May 12, 2003; revised April 22, 2004. Paper no. PESL00063-2003. The authors are with Kansas State University, Manhattan, KS 66506 USA (e-mail:
[email protected]). Digital Object Identifier 10.1109/TEC.2004.841524
Fig. 2. Waveform of the phase voltage summation at 1000 r/min and 20-A current command under healthy and faulty operations.
series with a resistance and self inductance. Mutual inductances to the healthy windings were also taken into consideration. Parameters of the shorted and healthy sections of the faulty phase were obtained by modifying the normal values based on the number of faulty turns. Resistance, mutual inductance, and EMF amplitudes are proportional to the number of turns, while self inductances are proportional to the square of the number of turns. Moreover, the fault current was added to the state variables of the system. Normal and faulty performances were compared, and the phase-voltage’s summation was selected as the characteristic waveform to identify the fault. Under normal operation, although phase voltages are equal in magnitude and shifted in time by 120 electrical, they do not add up to zero due to the harmonic contents of the trapezoidal EMFs. However, leakage components of the phase voltages cancel each other under normal operation, which makes the
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IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 1, MARCH 2005
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TABLE I TESTING RESULTS OF THE PROPOSED SYSTEM
voltage summation waveform free of chopping transients. Deviations of the phase-voltage summation under fault are due to two factors: reduction in the EMF magnitude across the faulty phase and discrepancy of motor parameters. Fig. 2 shows the characteristic waveform at 1000-r/min and 20-A current command under normal operation, and three shorted turns on phase C. III. FEATURE EXTRACTION Full diagnosis of the interturn short circuit requires the realization of the fault occurrence, determination of the number of shorted turns, and identification of the faulty phase. Similar fault conditions across different phases resulted in cyclically identical characteristic waveforms. Hence, full characterization of the fault is impossible using only one transform process (DFT) of the waveform. A complete electrical cycle of the voltage summation waveform was processed by DFT. The frequency of the second component of the spectral sequence and the magnitudes of the second and fourth components were enough to realize the fault occurrence and determine the number of shorted turns. Another transform process was necessary in order to identify the faulty phase. The characteristic waveform was processed using STFT to extract the frequency components during the first three switching states independently. Signed magnitudes of the first spectral components of each sequence–together–could identify the faulty phase. IV. ANFIS DIAGNOSTICS Two independent ANFIS were developed to automate the comprehensive diagnosis task: one system to realize the fault existence and determine the number of shorted turns ANFIS and another system to identify the faulty phase ANFIS . Both systems were first-order Sugeno-type ANFIS with three inputs, one output, and 12 Gaussian membership functions per input. Inputs to ANFIS represented the three diagnostic indices for determination of the number of shorted turns, and its output was either zero (at normal operation) or an integer (implying the number of shorted turns under fault). Meanwhile, inputs to
ANFIS , which operated sequentially upon a fault alarm issued by ANFIS , represent the three phase-identification indices mentioned above. ANFIS output was an integer, indicating the faulty phase. Training data sets of both systems contained diagnostic indices that were derived from simulated waveforms under various healthy and faulty operations and different loading conditions. Diagnostic ANFIS were tested at some operating points that were not included in the training data sets. Testing results, summarized in Table I, show that both systems have performed acceptably well in diagnosing the fault. V. CONCLUSION An ANFIS-based comprehensive and automatic diagnostic system for stator interturn short circuits in PM brushless dc motors is presented. System performance is obtained at normal conditions using a numerical discrete-time model. The model is modified to accommodate the faulty section and account for the fault current. Healthy and faulty performances are compared, and the waveform of phase voltage summation is selected as the characteristic signal. Appropriate diagnostic indices for realization of fault existence and determination of shorted turns are derived upon processing the voltage waveform using DFT. The STFT is used to extract indices for faulty phase identification. Two independent, sequentially-operating ANFIS are developed to detect and locate the fault. Testing results show acceptable performance of the proposed technique in full diagnosis of the fault. REFERENCES [1] A. J. M. Cardoso, S. M. A. Cruz, and D. S. B. Fonseca, “Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park’s vector approach,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 595–598, Sep. 1999. [2] M. A. Awadallah and M. M. Morcos, “Adaptive-fuzzy-based stator-winding fault diagnosis of PM brushless DC motor drive by monitoring supply current,” IEEE Power Eng. Rev., vol. 22, no. 12, pp. 46–49, Dec. 2002. [3] N. A. Demerdash and T. W. Nehl, “Dynamic modeling of brushless DC motors for aerospace actuation,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-16, no. 6, pp. 811–821, Jun. 1980.
