Condition Monitoring of Wind Generators

Condition Monitoring of Wind Generators Lucian Mihet Popa1, BirgitteBak-Jensen2, Ewen Ritchie2 and Ion Boldea1

Department of Electrical Machines and Drives, Politehnica University of Timisoara, 1900 Timisoara - Romania1 Institute of Energy Technology, Aalborg University, 9220 Aalborg-Denmark2 Phone: 40-256-204402, E-mail: [email protected], [email protected], [email protected], [email protected]

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 wind generators with further documentation for an advanced condition monitoring system, in order to avoid undesirable operating conditions and to detect and diagnose incipient electrical faults.

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. Many sources show that the majority of machine failures are caused by bearing faults and stator insulation breakdown. For small induction machines, the stator winding insulation degradation is one of the major causes [1, 15]. The induction machine can operate under asymmetrical stator and/or rotor-winding connections as described in [2, 3, 5 and 6]: 1. Inter-turn fault resulting in the opening or shorting of one or more circuits of a stator phase winding; 2. Abnormal connection of the stator winding; 3. Inter-turn fault of a rotor phase winding and / or brushes; 4. Broken rotor bars or cracked rotor end-rings (cage rotor); 5. Static and/or dynamic air-gap irregularities; 6. A rub between the rotor and the stator which can result in serious damage to the stator core and windings; Faults in AC induction machines produce one or more of the following symptoms:  Unbalanced air-gap voltages and line currents  Increased torque pulsation

 Decreased average torque  Increased losses and reduction in efficiency  Excessive heating  Disturbances in the current/voltage/flux etc. waveform Failure surveys have reported that percentage failure by components in induction machines is typically [1, 6, 8]: bearing related (40 %), stator related (38 %), rotor related (10 %), and others (12 %). The objective of this paper is to develop and test methods of condition monitoring, 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, reduce the number of outages and provide an improved background for planning service with relevant, longer, service intervals. II.

DESCRIPTION OF THE EXPERIMENTAL SYSTEM

The experimental system is a model of a wind generator system. The generator is a wound rotor induction generator (DFIG), with slip rings, rated at 11 kW, as can be seen in appendix, provided with a gearbox. 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 kVA-frequency 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 Figure 1.

Freq. Conv.

COND. MONITORING SYSTEM

3~ IM

Gear

Model of Wind Turbine

Back to back Converter

IG

Model of Generator System

Model of Wind Generator System

Fig. 1. Schematic diagram of the laboratory model of a wind generator system.

The most important component of the measurement system is a complete condition monitoring system, which comprises many subsystems like transducers, signal conditioning boxes and data acquisition devices, as can be seen in Fig. 2.

Fig. 2. The block diagram of condition monitoring system.

A. The Condition Monitoring System A complete condition monitoring system could comprise many subsystems, each monitoring a particular part of the wind generator. There will be a degree of overlapping between the functions of the different subsystems. This study includes the design and commissioning of a measuring system comprising the AD Card - ICS 645, signal conditioning boxes and transducers. Transducers and sensors generate signals that must be conditioned to provide the correct input to maximum the DAQ device-ICS 645. The induction generator has been monitored using measurements of stator and rotor currents, stator voltages, rotor speed and temperature in the windings. 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 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 VRMS from ground and up to 1300 V (DC + peak AC) differential. The rotor speed was measured using a shaft incremental encoder-Scancon 2R, which provide 9000 pulses/revolution. The stator winding temperatures were measured by inserting 12 thermocouples-Types J in various winding slots, corresponding to each pole and phase of the induction generator. 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. For example, thermocouples produce very low voltage signals, which required amplification, filtering and linearization. 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. The output signals from incremental encoder must be converted, using a frequency to voltage converter, and then filtered and offsetted. All of these preparation technologies are forms of Signal Conditioning. A2. The Data Acquisition Device Data acquisition devices are general purpose instruments that are well suited for measuring voltage signals. The DAQ ICS-645 is designed for high frequency, high precision, highdensity 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. Based on a new generation of ADC chip that combines Sigma-Delta and flash converter technologies, the ICS-645 offers an exceptional dynamic range over a wide bandwidth. 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 32 768. 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]. Three experimental investigations have been done 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 it is depicted in Figures 3. The stator of three-phase induction generator (DFIG) was connected to the star connected power source, also in star (wyes) connection.

The pictures below (Figs. 4, 5) show the rotor and stator currents of the generator measured under a resistive unbalance (Rv=1.2 Ω) in one rotor phase of the same value as the rotor phase resistance, corresponding to a stator power of 2 kW. A very small difference exist in both stator and rotor currents and can only be due to the unbalance provoked.

LV=(15 & 50) mH A

A

Switch on / off

Switch on / off B

B DFIG

DFIG

C

a1).

C

a2).

LV=15 & 50 mH

b)

c)

Fig. 3.a1) One stator phase inductive unbalance; a2) One stator phase resistive unbalance; b) One rotor phase resistive unbalance c) Turn-to-turn fault.

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, 3a2 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 balance case. Placing an inductance and/or a resistance in parallel on 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. The early detection of various fault mechanisms could prevent catastrophic failure from occurring and would also allow for carefully planned repair actions [1, 2]. To achieve this goal, on-line or off-line strategies that measure current may be utilized. IV.

Fig. 4. The rotor currents of DFIG acquired by TDS 540 under the resistive unbalance of 1.2 Ω in one rotor phase at 2 kW. Scaling factor is 4 mV/A.

EXPERIMENTAL RESULTS

The measurements were acquired by a Tektronix Oscilloscope-TDS 540 and by the DAQ device – ICS 645 via Matlab software. The DFIG was operating, during the measurements, at a rotor speed of 1475 rpm corresponding to a stator active power of 2 kW.

Fig. 5. The rotor speed and stator currents of DFIG acquired by ICS-645 under the resistive unbalance of 1..2 Ω in one rotor phase at 2kW.

Comparisons of Figures 6 and 7 show that the degree of unbalance provoked in one stator phase, through introducing the inductive unbalance of 50 mH (10 times more than stator phase inductance), is reflected very clearly in one stator current (Is1 in Fig. 6 on channel 2).

Fig.6. The stator currents acquired by TDS-540 under inductive unbalanceLv=50 mH at P=2 kW. The unbalance stator current is on channel 2. Scaling factor is 4 [mV / A].

Fig. 8 shows that increasing significantly with a level of the resistive unbalance in one stator phase (R v=10 Ω) produce a reduction of the stator current in that phase (ch. 2). It should also be evident that the stator current decreasing on one phase would provoke an increasing of others stator currents.

Fig. 8. Stator currents acquired by TDS-540 under one stator phase resistive unbalance (Rv=10 ) at P=2kW and n=1475 rpm. The unbalance stator current is configured on channel 2. Scaling factor is 4 [mV / A].

Fig.7. Speed and stator currents acquired by ICS-645 under inductive unbalance-Lv=50 mH, at P=2 kW & n=1475 rpm. Is2: instantaneous stator current of the unbalance phase.

Fig. 9. Stator currents acquired by TDS-540 under one stator phase resistive unbalance (Rv=1 ) at P=2kW and n=1475 rpm. The unbalance stator current is configured on channel 2. Scaling factor is 4 [mV / A].

The stator currents in Figs. 6, 8 and 9 were acquired by a Tektronix Oscilloscope. The measurement results show the efficiency of line currents in identifying the presence of a resistive and an inductive unbalance in one stator phase. Figs. 8 and 9 show the stator currents of the induction generator under two different levels of resistive unbalance (Rv=10  and Rv=1 ) in one stator phase. A comparison of these Figs. point out that increasing significantly with a level of the resistive unbalance in one stator phase (10 times larger than phase resistance), produce a decreases of the stator current of 14 times in that phase. Anyway, a clear difference between balance and unbalance currents exist in both situations very well. In Fig. 7, the stator currents and rotor speed of DFIG were acquired by the DAQ-ICS 645, for the same conditions as in Fig. 6, with the sampling frequency of 2.5 MHz. Is 1, Is2 and Is3 denote instantaneous values of the line currents where Is2 represents the instantaneous stator current of unbalanced phase. Figs. 10-11 show the stator and rotor currents of the generator that corresponds to stator power of 2 kW, under turn-to-turn fault developed in one stator phase, and acquired by a Tektronix Oscilloscope. Comparison of these Figs. illustrate that the rotor currents (rms values) have almost the same value while the imbalance created into the stator currents is more clear. Hence, the stator current could be a good indicator in detection of this type of fault.

Fig. 11. The rotor currents acquired by TDS-540 under turn-to-turn fault in one stator phase (Lv=50 mH in parallel - Fig. 3c) at P=2 kW and n=1475 rpm. Scaling factor is 4 [mV / A].

Time-domain analysis using characteristic values to determine changes by trend setting and for extracting the amplitude information from current signals has been used in this section as first evaluation tool. The acquisition of data by Oscilloscope was done to comparison with AD Card acquisition and to verify the accuracy and sensitivity of signal conditioning box.

V.

Fig. 10. The stator currents acquired by TDS-540 under turn-to-turn fault in one stator phase (Lv=50 mH in parallel - Fig. 3c) at P=2 kW and n=1475 rpm. Scaling factor is 4 [mV / A].

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-toturn fault [6, 7, 15], broken rotor bars [5, 6, 9, 11, 13], static or / and dynamic eccentricity [2, 8, 12, 13, 14]. Due to its powerful technique merits in diagnosis and detection of faults the MCSA monitoring technique was chosen as a fault detection method. 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, Fig. 12. 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.

Transducer

Signal Conditioning

ADC ICS-645 Data Acquisition

PC

Data Analysis

MATLAB

Fig. 12. Single channel acquisition system.

Figure 12 shows a diagram of a single channel of hardware required to obtain the indicators for analysis purposes. 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

Fig. 13. Spectrum of stator currents under turn- to-turn fault in one stator phase at PG=2kW. The data were recorded and processed by Matlab software package and acquired via ICS-645.

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, 5, 6, 7 and 15]: n  f st  f1  1  s   k  ………………………….(1) p   fst = stator frequency components that are a function of shorted turns, f1=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 13 and 14 give the line current spectra for all stator phases under turn-to-turn fault (Fig 13) and with no stator fault (Fig. 14). The obvious change, in Figure 13, 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). In addition the Figs. 13 and 14 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 where a new faulty component exist as well.

Fig. 14. FFT for a healthy machine of DFIG at PG=2kW, s=-0.016, VS=390 V, IS=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

C. MCSA to Diagnose Rotor Winding Unbalance

a) 175 Hz

175 Hz

a)

125 Hz

375 Hz

425 Hz

375 Hz 175 Hz

425 Hz

125 Hz

b) Fig 15. Stator currents spectrum under resistive (a) and inductive unbalance (b) in one stator phase at PG=2 kW. The data were recorded and processed by Matlab software and acquired by ICS-645.

Figs. 15 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 at 175 Hz (k=1 and n=5) and at 425 Hz (k=1 and n=15) while the resistive unbalance provokes the occurrence of new components in the stator current spectrum at 125 Hz (k=1 and n=3), 325 Hz (k=1 and n=11) and 375 Hz (k=1 and n=13) as well.

375 Hz

b) Fig. 16. Frequency Spectrum of the stator currents (a) and rotor currents (b) under rotor unbalance in one rotor phase at PG=2kW. The data were recorded and processed by Matlab software and acquired by ICS-645.

Figures 16 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. 16a. Another new component appears at 375 Hz. The rotor current spectrum offers new faulty components at 375 Hz (k=1 and n=13) and at 125 Hz (k=1 and n=3) as well and sometimes at 325 Hz (k=1 and n=11), as can be seen in Fig. 16b).

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 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’s. V.

CONCLUSIONS

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 article has shown that the detection of these faults is also possible by time and frequency domain analysis. The frequency spectrum of the stator and rotor line currents was found to give the best results. Placing an inductance and / or a resistance in series on one phase, between the grid and stator phase terminals on one phase, an unbalance in one stator phase was simulated. Inserting a resistance in series on one rotor phase we also simulated an unbalance in one rotor phase. Placing an inductance in parallel on one phase, the deterioration of the turn-to-turn insulation in one stator phase of the induction generator was simulated. In this way we simulated a turn-toturn fault in one stator phase without destroying the machine or building an additional construction such as placing taps on the wires of the stator windings and then shorting these taps. 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 equation (1). The experimental results have clearly demonstrated that MCSA can 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. REFERENCES

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APPENDIX: TABLE. I. NAMEPLATE DATA FOR LEROY SOMER 11 kW WOUND ROTOR SLIP RING GENERATOR. LEROY SOMER, MOT~FLSB 180 M4 B3, No. 14618900HG01, kg: 220 IP55 IK 1 cl. F S1 40 C V Hz 1/min KW A cos 690Y 50 1460 11 0.81 IS=13 VR=1700 DE

[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. 921-937, July/August 1992. [2] P.Vas, ”Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines”, Clarendon Press, Oxford, 1993. [3] 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.

NDE

IR= 6.1 6310 6310

3

15cm

3

15cm

11000 50/60 Hz 11000 50/60 Hz

H H