Failure Detection in Converter Fed Induction Machines under Different Operation Conditions Lucian Mihet - Popa1 and Mario Pacas2, IEEE Senior Member
Politehnica University of Timisoara, Department of Electrical Machines and Drives, 300223 Timisoara - Romania1 Siegen University, Institute of Power Electronics and Electrical Drives, 57068 Siegen-Germany2 Phone/Fax: +40-256-204402, E-mail:
[email protected],
[email protected]
Abstract – This paper focuses on the experimental investigation of methods for the detection of stator faults in cage rotor induction machines fed by voltage source inverters (VSI). Three experimental investigations: unbalance in one stator phase, turnto-turn fault in one phase and one stator phase open, have been performed to study the behavior of the electrical machine under these fault conditions. The work aims to provide further documentation for an advanced condition monitoring and detection system, by monitoring electrical quantities, in order to avoid undesirable operating conditions and to detect and diagnose electrical faults. A description of the measurement system including the data acquisition and processing is presented. Stator current signature, analysis of the space phasor of the stator current and of the instantaneous power as diagnostic techniques are considered. Index terms – Fault diagnosis, condition monitoring, electrical drive systems, VSI.
I. INTRODUCTION The reduction of operational and maintenance costs is a continuous requirement in modern electric drive systems. The increase of reliability and availability together with longer service intervals are often demanded in all kind of production machines. An important factor for fulfillment of these demands is to provide electrical drive systems with advanced condition monitoring systems able to detect incipient faults in electrical machines and drives during their operation. For small induction machines, the stator winding insulation degradation is one of the major causes of failure [1]. Other causes for failure of the squirrel cage or wound rotor induction machine are the operation under asymmetrical connections of the stator or rotor winding such as: Inter-turn fault resulting in the opening or shorting of one or more circuits of a stator phase winding, abnormal connection of the stator winding, inter-turn fault of a rotor phase winding or brushes, broken rotor bars or cracked rotor end-rings (cage rotor), static or dynamic air-gap irregularities. A serious damage to the stator core and windings result also from the rub between the rotor and the stator as a further failure [2, 3, 5, 6]. The objective of this paper is the development of methods for the condition monitoring of electric drive systems. This should contribute to increase the time, in which the induction machine drives will be in use by reducing the time spent in operating in undesirable load or fault conditions, by reducing
0-7803-8987-5/05/$20.00 ©2005 IEEE.
the number of outages and by providing an improved background for planning service with longer intervals. The early detection of various fault mechanisms could prevent catastrophic failure from occurring and would also allow for carefully planned repair actions. To achieve this goal, online or off-line strategies that measure current, voltage, speed and torque signals in a unified approach may be utilized. Modern measurement techniques in combination with advanced computerized data processing and acquisition show new ways in the field of induction machine drives monitoring by the use of spectral analysis of operational process variables. The study examines the behavior of a commercial drive with the original control system without modifications under faulty conditions. The quantities measured and estimated by the electronic of the drive and the external measured current and voltages are utilized in order to identify abnormal operating conditions, especially asymmetries of the stator winding. II. DESCRIPTION OF THE EXPERIMENTAL SYSTEM The equipment under test is an electrical drive system with VSI fed induction machine. The test machine is a 4 pole cage rotor induction machine (CRIM) rated at 7.5 kW, supplied by a commercial drive ABB-ACS 800 frequency converter in scalar operation mode. The load is simulated by a braking system composed of an induction machine and an ACS 600 frequency converter with a braking unit (braking chopper and an external braking resistor) to absorb the braking power as it is depicted in Fig. 1. The ACS 800 frequency converter is operated by using the proprietary PC tool Drive Window. Drive Window is a Windows based application for commissioning and maintaining drives equipped with fiber optic communication between the drive and the PC. The data were acquired by a laptop via Drive Window PC tool and by a DSO (Digital Storage Oscilloscope). All recorded data were processed externally by using the MATLAB software package to plot the currents and voltage spectra and to perform FFT analysis. Each measured variable has been recorded with a sampling frequency of 2.5 MHz and with a length of 50 000 points.
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condition). The current waveform in the healthy phase is also a clear indication that a fault has occurred. Time-domain analysis using characteristic values to determine changes by trend setting and for extracting the amplitude information from current signals were used here as first evaluation tool.
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Fig. 1. Schematic diagram of the laboratory setup
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III. EXPERIMENTAL ARRANGEMENTS AND DATA PROCESSING
Fig. 2. Experimental arrangements of the induction machine for the different faults simulated. 10
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Three experimental investigations have been done to study the electrical behavior of the induction machine under faulty conditions. The unbalance of one phase of the stator is achieved by using a variable resistance in series to the corresponding phase. The case of one stator phase open and of a turn-to-turn fault was simulated by using a variable resistance in parallel to the stator phase and a switch. This approach simulates actually an asymmetry of the winding and is not very appropriate for the study of turn-to-turn faults. The different configurations are depicted in Fig. 2. The induction machine drive system has been monitored by measuring with appropriate sensors. The line to line and phase stator voltages and the stator currents were recorded by using a DSO. Besides, Drive Window was used for the acquisition of: the stator voltage, the stator current, the rotor speed, the electromagnetic torque, the power and the dc-link voltage. The stator voltages were measured by using differential voltage probes: GE 8115 ELDI TEST ELECTRONIC and the stator currents were measured using current probes Le Croy AP011. A. External monitoring of current and voltages
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Fig. 3 shows a comparison between stator phase currents of phase V (healthy phase) of the CRIM at nominal frequency f1 =50Hz during no-load operations, for a balanced case a), for a resistive unbalance (Runb=5⋅R1) in one stator phase b) and with one stator phase open c). Fig. 4 shows the same currents for a 50% load. For a failure with one phase open, a very clear difference appears in the current magnitude (2 times higher at no-load and around 2.7 times higher during load
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Fig. 3. Stator currents of phase V for a) the balanced machine, b) unbalanced machine and c) for the machine with one phase open at nominal frequency and no-load.
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one phase open
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as the module of the space phasor decreases if one phase is open. The results shown in Fig. 6 are even more inconsistent. The drive develops apparently a huge torque but nevertheless the velocity decreases. In fact, due to the failure, the current increases as was already explained in the last chapter. The drive makes all calculations based on this current and delivers wrong estimated values of all other variables.
balanced machine
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Fig. 4. Stator currents of phase V for the balanced machine, the unbalanced machine and for machine with one phase open at nominal frequency and 50% load .
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B. Monitoring of the variables by the drive
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All modern digitally controlled drives usually measure the currents of the inverter and the dc-link voltage for the calculation of the model of the machine used in the control of the drive. The drive examined in the present work offers the possibility to record the measured variables and some other quantities that can be used as indicators for failures of the machine. Fig. 5 and 6 show an acquisition of data via Drive Window PC Tool with monitoring possible indicators: the estimated speed of the motor, the estimated torque, dc-link voltage, the average of the total power, the average of stator line-to-line voltages and the average of stator currents. These quantities are either measured by the drive by using the built in sensors or calculated, based on the measured variables. The drive was operated in speed control mode with a constant speed as a reference. A failure of the induction machine was simulated by opening one of the stator phases in no load (Fig. 5) and in 50% load conditions (Fig. 6). The results in Figs. 5 and 6 show that the drive does not trip and continue in operation at no load and 50% load conditions despite of the opening of one phase. This fault produces appreciable changes in the quantities monitored by the drive such as: increased torque and torque pulsations, increased speed pulsations (no-load), increased stator current, increased power and power pulsations, decreased output voltage of the inverter (stator line-to-line voltage) and also increased stator voltage pulsations. In fact the drive supposes a healthy machine and makes all the calculation based on this assumption. Therefore the results delivered by the drive are somehow contradictory. Fig. 5 shows the failure at load zero. The drive shows an almost constant speed. Since there is no physical reason for a change in the speed, its estimation is acceptable. The estimated torque becomes 5 %, which is not a proper value since at no load the drive should accelerate. The apparent increase of power is probably the result of this miscalculation of the torque. The voltage calculation seems to be correct
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steady state one stator phase open
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Fig. 5. Induction machine drive variables during steady state and one phase open at no load and nominal speed. From t=11 s to 28 s the machine works with one phase open.
Although the monitoring of machine variables by the control electronics is today available in many commercial drives, it can be stated that in general it cannot be used as a reliable basis for predictive diagnostics and failure detection. Therefore the utilization of models that consider the different types of failure are necessary as enhancement of the control schemes of the drives. IV. FURTHER FAULT DETECTION TECHNIQUES As the data obtained from the drive are not appropriate for the detection of failures, other techniques have to be applied. The most common one is the detection based on the measured stator currents that are measured by means of external sensors as already explained. Additionally, the stator voltages are also recorded. The data will first be processed to synthesize suitable indicators. The indicators will then be resolved into spectra showing the frequency content of the indicators such as stator currents and voltages. For condition monitoring, Fourier analysis has been employed using the components of the
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n/min-1
current spectrum or the power spectrum as an indication of how the harmonic content of a signal varies. If the variation of the harmonic content can be related to specific faults then it may be useful as an indicator. 3000 2000 1000 0 -1000
steady at nominal speed
steady at nominal speed
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M/%
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Fig. 6. Induction machine drive variables during steady state and one phase open at no load and nominal speed. From t=11 s to 28 s the machine works with one phase open.
A. Machine current signature analysis (MCSA) Machine current signature analysis (MCSA) is a noninvasive, online or offline monitoring technique for the diagnosis of problems in induction machines, such as turn-to-turn fault [5-8, 11, 12], broken rotor bars [2-5] as well as for static and for dynamic eccentricity [2, 7]. Due to its powerful technique merits in diagnosis and detection of faults, the MCSA monitoring technique was chosen as a first fault detection method. The objective of this method is the identification of current components in the stator winding that are only a function of the developed fault and are not due to any other problem or mechanical drive characteristic. Equation (1) gives the frequency components in the air-gap flux that appear in the case of stator faults [2, 5, 8, 11 and 12]. ⎡n ⎤ (1) f h = f1 ⎢ (1 − s ) ± k ⎥ p ⎣ ⎦ f h is the frequency of the harmonic components, f1 the supply frequency, p the number of pole pairs and s the slip; n and k are integer numbers.
The air-gap flux induces corresponding current components in the stator winding. Thus, as it is well-known, the diagnosis of faults via MCSA is based on the detection of the frequency components given by equation (1) in the stator currents of the faulty machine. If the drive does not trip, it impresses a voltage of constant frequency and amplitude and eq. (1) holds. The behavior of the control system in case of a fault or asymmetries of the machine is non predictable. It will depend on its implementation. Therefore, only in the particular case that the system goes in steady state situation after the fault, the analysis of the harmonic components in the spectra of the currents can be used for the detection of asymmetries. Fig. 7 gives a comparison between the phase current spectra for the healthy machine, for the machine with an unbalanced phase and for the machine with one phase open at noload. Fig. 8 shows the same situation at 20 % of nominal load. In the case of unbalance, new harmonic components, marked by arrows, appear in the spectra of the stator currents as predicted by (1). One open phase in the stator is an extreme case of asymmetry, at no load and at light load the examined drive continues in operation and the induction machine works as single-phase machine. For unbalance as well as for single-phase operation the current spectrum contains a strong 3rd harmonic that is not present under symmetric operation. That means the amplitude information of 3rd harmonic could be a good indication able to point out the occurrence of an incipient stator fault. Fig. 9 shows stator currents in the time domain and their FFT analysis at no-load and at stator frequency f1=35.7 Hz, when a turn-to-turn fault was simulated by inserting a resistance in parallel on one phase. Again the spectrum of the stator current contains harmonics in the neighbourhood of the 3rd, 5th and 7th etc. Due to the fact that the study was limited to the simulation of an asymmetrical winding instead of a real turn-to-turn fault or of the effect of real short-circuit, more research is certainly needed. In Fig. 10 stator currents and their FFT are shown for the single phase operation at f1=50 Hz and 20% load. The 3rd, 5th and 7th odd harmonics are more prominent as in the frequency spectrum of a healthy machine at no load and could be used as indicator for the failure. B. Stator current space phasor The components of the stator current space phasor (iα, iβ) are a function of the instantaneous values of the stator currents (iU, iV, iW): iα = i U iβ =
1 3
(iV − iW )
(2)
and in the case of symmetrical machine and symmetrical feeding describe a circular pattern centered at the origin of the coordinates, if plotted in the α,β-plane [7]. This is a simple
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reference figure that allows the detection of faulty conditions by monitoring the deviations of the acquired patterns.
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Fig. 7. Spectra of the stator currents for a healthy machine, an unbalanced machine in one phase and one phase open at no-load
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f / Hz a) b) Fig. 9. Stator currents versus time, (a) and FFT of stator currents (b) when the machine works at no-load with an external additional resistance in parallel on one phase.
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a) b) Fig. 8. a) Spectra of all stator currents for a healthy machine, b) for an unbalanced machine, both at 20% load.
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Fig 11. Comparison between the stator current space phasor of a healthy machine and of a machine with one phase open.
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As expected, the frequency analysis of the components of the stator current space phasor result to be useful to detect the new faulty components directly related to the induction machine fault in both situations under no-load and load condition. The space phasor representation has the advantage that it reduces the number of variables to handle but contains the information of all three phases. 20
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Fig. 11 presents a comparison between the stator current space phasor for a healthy machine and a machine with one phase open. Fig. 12 depicts the stator current space phasor for a healthy machine and one with an external additional resistance in parallel, simulating a turn-to-turn fault at load condition. Under abnormal conditions such as turn-to-turn fault and also under one phase open, the deviations of the current pattern from a circular one are very clear in comparison with a balanced machine as can be seen in both figures. It looks like an ellipse under turn-to-turn fault and very close to a straight line under unbalanced condition. The balanced machine pattern differs slightly from the expected circular one, because the supply voltage is not sinusoidal.
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100 200 300 400 500 600 700 800 900 1000 f / Hz Fig. 13. FFT of the real component of the stator current phase phasor for a balanced machine and two failure cases at no-load and nominal speed.
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Fig 12. Comparison between the stator current space phasor of a healthy machine and a of machine with unbalance in one phase under load condition.
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Under abnormal conditions, the currents of the induction motor will contain new harmonic components at frequencies differing from the fundamental [9, 13]. Of course, these additional components are present also in both components of the space phasor of the stator current as can be seen in both Fig. 13 and Fig. 14 respectively as well as in the spectrum of the stator currents presented before using the MCSA detection technique. Fig. 13 presents the real component iα and Fig. 14 the imaginary component iβ of the stator current space phasor for a healthy machine and for the machine with one phase unbalance at no-load and f1=50Hz corresponding to the cases examined above. Fig. 15 and 16 show the frequency spectra of the components of the space phasor for a machine with unbalance in one phase and for a machine with one phase open under 20 % load. Under one phase unbalance, using an additional resistance in series (Radd=5 R1), new components appears around the odd harmonics. When one phase is open, as under no-load condition, 3rd and 5th harmonics amplitude is higher than at a balanced machine, pointing out that an incipient fault has appeared.
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Fig. 14. FFT of the imaginary component of the stator current phase phasor for a balanced machine and for the unbalanced machine at no-load and nominal speed.
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chine is fed from a PWM inverter, the spectra of stator power coinciding with the supply frequency or its multiples may indicate electrical faults in the stator, as unbalanced conditions of individual stator phases. The fault related spectral components tend to grow in amplitude with the severity of the fault, as can also be seen in Fig. 17, around 200 Hz for instance. Comparisons between spectrums of total power, for a machine with one phase unbalance at no-load and during loaded condition, point out that this detection method (instantaneous power) offers more information during loaded condition. Clearly, the diagnosis of stator faults, like one phase unbalance, is easier under high load condition, since at noload the rotor cage hardly carries any current. Due to the difficulties in measuring and sampling the voltages of the inverter, this method hast to be used with some care taking into account the voltage harmonics and asymmetries produced by the inverter.
I1an / dB
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D. Other signature analysis detection techniques
0
100 200 300 400 500 600 700 800 900 1000 f / Hz Fig. 15. FFT of the components of the stator current space phasor for a machine with one phase unbalance under load condition.
Other diagnostic procedures such as the analysis of the estimated electromagnetic torque and of the calculated flux linkage have been tested for all developed faults presented before, but they offer no real advantage in the examined cases.
I1an / dB
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Fig. 16. FFT of the real component of the stator current space phasor for a machine with a stator phase open under load condition.
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C. Instantaneous power
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u U (t) ⋅ i U (t) + u V (t) ⋅ i V (t) + u W (t) ⋅ i W (t)
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(3)
P1n / dB
pT (t) = p U (t) + p V (t) + p W (t) =
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Sometimes, reliable interpretation of the spectra is difficult, since distortions of the current waveform caused by the abnormalities in the drive system are usually minute. In this situation, an alternative medium for the motor signature analysis, namely the instantaneous power, is used [7, 10, 11, 12, 14]. Therefore, also the total power pT(t) was taken into consideration in this study. The total power pT(t) is the sum of the instantaneous powers of all phases given by:
-40
where uU(t), uV(t) and uW(t) are the phase voltages and iU(t), iV(t) and iW(t) denote the phase currents. Fig. 17 presents a comparison between total stator power spectrum of a healthy machine and the spectrum of an unbalanced machine and of a machine with one phase open at noload. Fig. 18 depicts the results of the machine under the same asymmetry conditions but for a load of 20%. If a ma-
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Fig. 17. FFT of the total stator power for a healthy machine, with one phase unbalance and with one phase open at no-load.
0
the control of torque and flux despite of the asymmetry condition of the machine.
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balanced machine
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APPENDIX
-30 -40
MACHINE DATA: PN=7.5 kW, 50 Hz, IN=8.6 A, VN=690 V, 4 poles, R1=0.94 Ω, X1=1.1 Ω, RFe=1.7 Ω, Xh=33.4 Ω, X2’=1.27 Ω, R2’=0.58 Ω.
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REFERENCES
-60 0 -10
unbalanced machine P1n / dB
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0
200
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1000 1200 f /Hz Fig. 18. FFT for a healthy machine and with one phase unbalance at 20% load.
V. CONCLUSION The objective of this paper was to examine different types of faults and to test detection methods of condition monitoring of electrical drive systems.Numerous measurements were carried out in a laboratory setup using a commercial drive and monitoring electrical quantities with the built-in sensors of the drive and with additional external sensors. Since the monitoring of the electrical quantities by the drive seems to be inadequate for purposes of diagnosis, currents and voltages were measured under different asymmetry conditions of the induction machine. Time-domain analysis using characteristic values to determine changes by trend setting and for extracting the amplitude information from various signals has been used as first evaluation tool. The experimental results show that in the examined case and with the utilized inverter the monitoring and analysis of stator currents for identifying the presence of different types of unbalance of the induction machine is possible despite of the inverter feeding. The results presented in this paper verify that asymmetries of the induction machine with different degrees of severity can also be identified in VSI fed induction machines using the well-know techniques that were developed for the induction machine on the mains if the behavior of the control system of the drive is known and is taken into consideration for the analysis of the data. The study has to be extended to drives equipped with other control schemes like DTC and field-orientated control, in which the control reacts to the asymmetry by trying to keep
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[1] A.H.Bonnet and G.C.Soukup, “Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors”, IEEE Transactions on Industry Applications, Vol. 28, pp. 921-937, July/August 1992. [2] S. Nandi and H.A. Toliyat, “Fault Diagnosis of Electrical Machines – A Review”, IEEE Industry Applications Conference, 1999. ThirtyFourth IAS Annual Meeting. Vol. 1, 1999, pp. 197-204. [3] H.A. Toliyat, T.A. Lipo, “Transient Analysis of Cage Induction Machines under Stator, Rotor Bar and End Ring Faults”, IEEE Trans. On Energy Conv., Vol. 9, No. 4, June 1995. [4] G.B. Kliman, W.J. Premerlani, R.A. Koegl and D. Hoeweler, “A new approach to online fault detection in ac motors”, IEEE-IAS Annual Meeting Conference, pp. 687-693, San Diego, CA, 1996. [5] W.T. Thomson and M. Fenger, “Current Signature Analysis to Detect Induction Motor Faults”, IEEE Industry Applications Magazine, pp. 26-34, July/August 2001. [6] William T. Thomson, “On-line MCSA to diagnose shorted turns in low voltage stator windings of 3-phase induction motors prior to failure”, Electric Machines and Drives Conference, 2000. IEMDC 2001, IEEE International, 2001, pp. 891-898. [7] M. El H. Benbouzid and G.B. Kliman, “What stator current processing-based technique to use for induction motor rotor faults diagnosis?”, IEEE Transactions on Energy Conversion, Vol. 18, No. 2, June 2003, pp 238 – 244. [8] W.T. Thomson, M. Fenger, “Industrial application of current signature analysis to diagnose fault in 3-phase squirrel cage induction motors”, Pulp and Paper Industry Technical Conference. Conference Record of 2000 Annual Meeting, pp. 205-211. [9] F. Filippetti, G. Franceschini, G. Gentile, S. Meo, A. Ometto, N. Rotondale, C. Tassoni, “Current pattern analysis to detect induction machine non rotational anomalies”, ICEM 98, International Conference on Electrical Machines, September 2-4, 1998, Istanbul-Turkey, Vol. 1, pp. 448-453. [10] S.F. Legowski, A.H.M. Sadrul Ula and A.M. Trzynadlowski, “Instantaneous power as a medium for the signature analysis of induction motors”, IEEE Transactions on Industry Applications, Vol. 32, No. 4, July/August 1996, pp. 904-909. [11] L. Mihet-Popa, B. Bak-Jensen, E. Ritchie and I. Boldea, “Condition Monitoring of Wind Generators”, Record of IEEE-IAS 38th Annual Meeting, Salt Lake City-USA, 2003, 12-16 October, Vol. 3, pp. 1839-1846. [12] L. Mihet-Popa, B. Bak-Jensen, E. Ritchie and I. Boldea, “Current Signature Analysis to Diagnose Incipient Faults in Wind Generator Systems”, ELECTROMOTION 2003, Marrakech-Morocco, 26-28 November, Vol. 2, pp. 647-652. [13] A. Bellini, F. Fillipetti, G. Franceschini, C. Tassoni and G.B. Kliman, „Quantitative evaluation of induction motor broken bars by means of electrical signature analysis”, IEEE Transactions on Industry Applications, Vol. 37, No. 5, September / October 2001, pp. 1248-1255. [14] A.M. Trzynadlowski and E. Ritchie, „Comparative investigation of diagnostic media for induction motors: a case of rotor cage faults”, IEEE Transactions on Industrial Electronics, Vol. 47, No. 5, October 2000, pp. 1092-1099.
