Current Signature Analysis to Diagnose Incipient Faults in Wind Generator Systems

Transcription

1 Current Signature Analysis to Diagnose Incipient Faults in Wind Generator Systems Lucian Mihet Popa *, Birgitte Bak-Jensen **, Ewen Ritchie ** and Ion Boldea * * Department of Electrical Machines and Drives, Politehnica University of Timisoara, 1900 Timisoara - Romania ** Institute of Energy Technology, Aalborg University, 9220 Aalborg-Denmark Abstract This paper focuses on the experimental investigation for incipient fault detection and fault detection methods existing in the literature, suitably adapted for use in wind generator systems using Doubly Fed Induction Generators (DFIGs). Three main experiments (one for stator phase unbalance, one for rotor phase unbalance and one for turn-to-turn faults) have been performed to study the electrical behaviour of the DFIG. The article aims to provide further documentation for an advanced condition monitoring system for wind generators, in order to avoid undesirable operating conditions and to detect and diagnose incipient electrical faults. A description of the measurement system and experimental investigation are presented and stator currents and instantaneous power spectra of the DFIG are analyzed. I. INTRODUCTION The reduction of operational and maintenance costs is continuously required of modern wind generators. In particular, with plans for extending wind farms at sea (making the generators more inaccessible), it is required to increase reliability and production time, simultaneously with an increase of service interval. An important factor enabling this is to provide wind generators with advanced condition monitoring systems and monitoring the generator during operation. This will help to avoid undesirable operating conditions, and to detect incipient faults in the components to detect sensor and actuator faults. Machine current signature analysis (MCSA) is a non-invasive, online or offline monitoring technique for the diagnosis of problems in induction machines [4, 5, 6]. [4, 5, 7, 8, 9] use spectrum analysis of machine line current (MCSA) to detect broken bar faults. The presence of static and dynamic eccentricity can also be detected using motor current signature analysis - MCSA [2, 10, 13]. Diagnostic media other than the current include the input power and estimated torque [15-16]. In place of the stator current, the instantaneous power may be used as a medium for the machine signature analysis oriented towards mechanical and electrical faults detection in a drive system [7, 15, 16]. The objective of this paper is to develop and test methods of condition monitoring, existing in the literature, suitably adapted for implementation in wind generator systems. This should contribute to increasing the time in which the generator will produce energy, by reducing the time spent in operating in undesirable load or fault conditions. II. DESCRIPTION OF THE EXPERIMENTAL SYSTEM The experimental system is a model of a wind generator system. The generator is a doubly fed induction generator (DFIG), with slip rings, rated at 11 kw, provided with a gearbox, as described in the appendix. The wind turbine rotor is emulated by use of a drive system scaled for driving the DFIG. The drive system is composed of a 15 kw squirrel cage induction motor and a 22 kvafrequency converter. Two back-to-back PWM-VSI converters, with a dc link including a dc capacitor filter are used to control the rotor current, active and reactive power flow, as can be seen in Fig. 1. Freq. Conv. IM 3~ Model of Wind Turbine Gear IG Back to back Converter Model of Wind Generator System COND. MONITORING SYSTEM Model of Generator System Fig. 1. Schematic diagram of the laboratory model of a wind generator system. The most important feature of the measurement system is a complete condition monitoring system, which comprises many subsystems like transducers, signal conditioning boxes and data acquisition devices. A. The Condition Monitoring System A complete condition monitoring system could comprise many subsystems, each monitoring a particular part of the wind generator. This study includes the design and commissioning of a measuring system comprising the AD Card - ICS 645, signal conditioning boxes and transducers. The Rogowski Current Transducer-RGF 75 was selected to measure stator and rotor currents because of its merits compared to other transducers, such as: have a very wide bandwidth, provide an isolated measurement at around ground potential, can measure large current

2 without saturating, accuracy typically ± 1% and linearity ± 0.05% full scale / 0.1% actual reading. The stator voltages were measured using a High Voltage Differential Probe-P5200. The P5200 provides a safe means of measuring circuits with floating potentials up to 1000 V RMS from ground and up to 1300 V (DC + peak AC) differential. A1. The Signal Conditioning Signal Conditioning provides the interface between the signals/sensors and the measurement system. It improves the performance and reliability of the measurement system with a variety of functions, including: signal amplification, attenuation, isolation, filtering, multiplexing, linearization, sensor conditioning, offsetting and noise reduction. In our set up it was imperative to adapt the signals from transducers as the DAQ ICS 645 accepts the input signals with peak amplitudes of ± 1.03 V with an input impedance of 500 Ohms. Voltage signals, acquired by high voltage differential probes, required attenuation of the signal while the voltage signals obtained from Rogowski coils required filtering and offsetting of the signal, as can be seen in Fig. 2. COIL TRANSDUCERS R0 C0 R1 + - C1 Rogowski Current Transducer VOUT BNC Connect. Signal Conditioning R1 R1 - + R2 R2 Vo= 1V OSCILLOSCOPE Data Acquisition Device ICS Fig. 2. Single channel block diagram of current sensors conditioning. A2. The Data Acquisition Device The Data Acquisition (DAQ) device ICS-645 is designed for high frequency, high precision, high-density data acquisition and high-speed test and measurement applications. It combines the ultimate in analog and digital technologies to provide up to 16 channels and sample rates of up to 5 MHz/channel. The AD Card ICS 645 is able to convert 16 channels simultaneously at up to 5 MHz sampling frequency, and with 16 bit conversion. All recorded data were processed using the MATLAB software package to plot the currents and voltage spectra and then to perform the FFT analysis. For each variable, 2 16 values were recorded, with the sampling frequency of 2.5 MHz and with the amount of data points of III. EXPERIMENTAL ARRANGEMENTS Methods for the prediction of electrical behaviour in deteriorating induction machines allow for an arbitrary number of winding deterioration processes to be simulated on the stator and / or the rotor. Forms of deterioration might include high or low impedance between phases, between coils in a single phase, or between a phase and ground [1]. L V=(15 & 50) mh Switch on / off B A C DFIG Switch on / off a 1 ). a 2 ). c) Fig. 3.a 1 ) One stator phase inductive unbalance; a 2 ) One stator phase resistive unbalance; One rotor phase resistive unbalance c) Turn-to-turn fault. Three experimental investigations were performed to study the electrical behaviour of the induction machine: one stator phase unbalance using a variable resistance and inductance in series on one phase, one rotor phase unbalance using a resistance of the same value as the rotor phase resistance inserted in series on one rotor phase, and a turn-to-turn fault using an inductance in parallel on one stator phase, as depicted in Fig. 3. The stator of the three-phase induction generator (DFIG) was connected to the star connected power source, also in star (wyes) connection. The procedure, which was used in creating an unbalance in one stator or rotor phase, consisted of increasing the impedance of that phase using a variable resistance (inductance) connected in series, as can be seen in Figs. 3a 1, 3a 2 and 3b. For instance, when a variable resistance of 10 Ohms was inserted between the grid and the stator phase terminals on one phase, the stator impedance became 7 times larger than in the balanced case. Placing an inductance and/or a resistance in parallel with one phase, as shown in Fig. 3c), simulated deterioration of the turn-to-turn insulation. The procedure consisted of decreasing (weakening) the impedance of one stator phase by inserting a variable resistance and/or inductance in parallel on that phase. To achieve this goal, on-line or off-line strategies that measure current and voltage may be utilized. B A C DFIG

3 IV. CURRENT SIGNATURE ANALYSIS TO DETECT INDUCTION GENERATOR FAULTS 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, 6, 14], broken rotor bars [4, 5, 8, 10, 12], static or / and dynamic eccentricity [2, 7, 11, 12, 13]. Due to its powerful technical merits in diagnosis and detection of faults the MCSA monitoring technique was chosen as a fault detection method for further investigation. The stator and rotor currents monitoring system that has been developed consists of three main sub-systems. These include: signal conditioning, data acquisition and data analysis. Data acquisition and data analysis sections are accessed by MATLAB software package run from a PC. The controlling software allows the operator of the system to store the data in data files for future processing. The data will first be processed to synthesise suitable indicators. The indicators will then be resolved into spectra, showing the frequency content of the indicators such as stator and rotor currents. For Condition Monitoring, Fourier analysis has been employed using the coefficients of the 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. A. MCSA to Diagnose Stator Turn-to-Turn Faults In addition the Figs. 4 make clear that the difference between balanced operation and under turn-to-turn fault cases follows the phase angle of stator current - Is1. For instance the phase angle is changed around 125 Hz (under turn-to-turn) where a new faulty component exist as well. 125Hz The objective of this method is to identify current components in the stator winding that are only a function of shorted turns and are not due to any other problem or mechanical drive characteristic. The following equation gives the components in the air-gap flux waveform that are a function of shorted turns [3, 4, 5, 6, and 14]: n f st = f1 ( 1 s) ± k.(1) p f st = stator frequency components that are a function of shorted turns, f 1 =supply frequency, n=1,2,3 k=1,3,5, p=pole-pairs, s=slip The diagnosis of shorted turns via MCSA is based on detecting the frequency components given by equation (1) in that these rotating flux waves can induce corresponding current components in the stator winding. Figures 4 give the line current spectra for all stator phases under turn-to-turn fault (Fig 4 and with no stator fault (Fig. 4. The obvious change, in Figure 4, is that completely new current components exist around 125 and 375 Hz and can only be due to the shorted turn, as per theory with k=1, n=3 and k=1, n=13 (equation 1). Fig. 4. FFT of stator currents under turn-to-turn fault in one stator phase of DFIG ( and for a healthy machine ( at P G =2kW, s=-0.016, V S =390 V, I S =4.7 A. The data were recorded and processed by Matlab software package and acquired via ICS-645. B. MCSA to Diagnose Stator Winding Unbalance Figs. 5 show a comparison between the stator currents spectrum under one stator phase resistive unbalance (R=10 Ω) and one stator phase inductive unbalance (L=50 mh). The inductive unbalance creates the occurrence of new components in the stator current spectrum around 125 Hz (k=1 and n=3) and around 425 Hz (k=1 and n=15) while the resistive unbalance provokes the occurrence of new components in the stator current spectrum around 125 Hz (k=1 and n=3), 325 Hz (k=1 and n=11) and 375 Hz (k=1 and n=13) as well.

4 incipient rotor windings unbalance. While, under the resistive and inductive unbalance in one stator phase and under turn-to-turn fault in one stator phase, the stator line current spectrum offers more information. Fig 5. Stator currents spectrum under resistive ( and inductive unbalance ( in one stator phase at P G =2 kw. The data were recorded and processed by Matlab software and acquired by ICS-645. C. MCSA to Diagnose Rotor Winding Unbalance Figures 6 show the stator and rotor line currents spectrum under rotor unbalance in one rotor phase. A clear difference in the spectrum of the stator currents appear at 75 Hz, as can be seen in Fig. 6. Another new component appears at 375 Hz. The rotor current spectrum exhibits new faulty components around 375 Hz (k=1 and n=13) and around 125 Hz (k=1 and n=3) too. Sometimes components appear at 325 Hz (k=1 and n=11), as can be seen in Fig. 6. It can be concluded that the rotor line current spectrum, under rotor unbalance in one rotor phase, offers more information s about occurrence of this fault than the stator line current spectrum. Anyway, both of them may be used as an indicator in detection of Fig. 6. Frequency Spectrum of the stator currents ( and rotor currents ( under rotor unbalance in one rotor phase at P G =2kW. The data were recorded and processed by Matlab software and acquired by ICS-645. V. INSTANTANEOUS POWER AS DIAGNOSIS MEDIA 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 machine signature analysis, namely the instantaneous power, is used. It has been shown that the amount of information carried by the instantaneous power, which is the product of the supply voltage and the motor current,

5 as shown by (2), is higher than that deducible from the current alone [15, 16]. P AB (t)=p AB,0 (t)+(m/2)i L V LL {2cos(φ+π/6)cos(ω 0 t)+cos[(2ω + ω 0 )t-φ-π/6]+cos[(2 ω- ω 0 )t-φ-π/6]} (2) where p AB,0 (t) is the instantaneous power, m is the modulation index, V LL is the rms value of the line-to-line voltage, and I L is the line current, while ω and φ denote the supply radian frequency and machine load angle, and ω 0 is the radian oscillation frequency. The diagnostic media include the following: line current i A, in phase A of the supply line; Partial input power p AB, calculated as a product of the line-to-line voltage v AB and line current i A. Total power p ABC (t), a sum of p AB and p CB. Recorded data were processed using the MATLAB software package. The Fast Fourier Transform (FFT) was computed and the power spectra were plotted. For each case, voltage and current measurements were taken for the generator operating. For each variable, 2 16 values were recorded, with the sampling frequency of 2.5 MHz, simultaneously on each channel. c) d) Fig.7. Frequency spectrum of partial and total instantaneous power and stator current of DFIG for healthy machine ( and under stator unbalance (, rotor unbalance (c) and turn-to-turn fault (d) at 2 kw. FFT was computed and power and current spectrum were plotted using MATLAB software and getting via ICS 645. Spectra of diagnostic media with and without fault are shown in Figs. 7. The spectrum of instantaneous power and line current is also presented. The frequency spectrum of partial and total power under stator unbalance (Fig. 7 exhibits faulty components around 425 Hz, while the frequency spectrum of stator current offers new faulty components around 125 Hz and 375 Hz, too. In the instantaneous power spectrum under rotor unbalance (Fig. 7c) appears additional components around 125 Hz and 425 Hz while in the frequency spectrum of stator current appears new faulty components

6 around 75 Hz and 375 Hz, respectively. The frequency spectrum of partial and total power under turn-to-turn fault gives new faulty components around 425 Hz. The stator current spectrum exhibits new components around 125 Hz, as can be seen in Fig. 7c). It is concluded that the instantaneous power spectra has not bring important improvement under stator unbalance. Therefore, the stator current should be maintained as the main medium for the machine signature analysis, while, when an abnormality such as rotor unbalance is developed the stator current seems to offer no advantage over the instantaneous power. VI. CONCLUSION The objective of this paper was to develop different type of faults and to test detection methods of condition monitoring, previously reported in the literature, and suitable adapted for implementation in wind generator systems. The results presented have shown that the objective was achieved. An induction machine condition monitoring system has also been developed to tests these methods. The software, which controls both the acquisition and analysis of the signals, is written in MATLAB and has been developed as a sub-part of the monitoring system. The experimental results show the efficiency of line stator and rotor currents monitoring in identifying the presence of an unbalance in one stator and rotor phase and confirm the presence of harmonics as described by (1). The experimental results have clearly demonstrated that MCSA to be used to diagnose turn-to-turn faults. MCSA can also diagnose other problems in induction generators such as inductive and resistive unbalance in one stator and rotor phase. Due to the fact that the study was limited to the simulating turn-to-turn fault, placing an inductance in parallel on one phase, instead the effect of real short circuits, more research is certainly needed. The instantaneous power seems to be a valid alternative diagnostic medium for the machine signature analysis. Hopefully, the results presented will form a basis for diagnosis methods in wind generator systems. APPENDIX: TABLE. I. NAMEPLATE DATA FOR LEROY SOMER 11 kw WOUND ROTOR SLIP RING GENERATOR. LEROY SOMER, MOT~FLSB 180 M4 B3, No HG01, kg: 220 IP55 IK 1 cl. F 40 C S1 V Hz 1/min KW cosϕ A 690Y I S =13 V R =1700 I R = 6.1 DE cm H 50/60 Hz NDE cm H 50/60 Hz REFERENCES [1] A.H.Bonnet and G.C.Soukup, Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors, IEEE Trans. Ind. Applicat., vol. 28, pp , July/August [2] P.Vas, Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines, Clarendon Press, Oxford, [3] S.Nandi and H.A.Toliyat, Fault Diagnosis of Electrical Machines A Review, IEEE Industry Applications Conference, Thirty-Fourth IAS Annual Meeting. 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