Detection of Stator and Rotor Faults in a DFIG Based on the Stator Reactive Power Analysis M. B. Abadi1, 2, S. M. A. Cruz1, A. P. Gonçalves1, P. F. C. Gonçalves2, A. M. S. Mendes1, A. Ribeiro2, F. Silva2 1
Department of Electrical and Computer Engineering, University of Coimbra / IT, Coimbra, Portugal 2 Research & Development Department, ENDIPREV Lda., Mortágua, Portugal
[email protected],
[email protected],
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[email protected] [3], it is stated that 23.5% of the downtime of the wind turbines in 2009 was due to generator failures. Several types of faults may occur in DFIGs, namely ITSC and increased phase resistance (IPR) in the stator and rotor windings, slip ring degradation and bearing faults [4, 5]. Both ITSC and IPR faults create an asymmetry in the stator and rotor windings of the DFIG and are the most common type of failures studied in the literature [6]. An ITSC fault may appear due to the degradation of the insulation system between the individual turns of a coil or between different coils of the windings and occurs due to a combination of different stresses to which the windings are subjected to like electromechanical induced vibrations, high dv/dt imposed by the power electronic converter feeding the rotor windings, thermal overload or contamination, among other factors [7]. For the detection of ITSC fault in electrical motors, several methods have been presented, namely the motor current signature analysis (MCSA), extended Park’s vector approach (EPVA), multiple reference frames (MRFs) theory, wavelet transform (WT) or the flux signature analysis (FSA), among others [8-12]. For the case of DFIGs, similar fault detection methods have also been proposed in the literature. Different techniques to identify IPR faults in the stator and rotor of a DFIG controlled by a stator flux oriented control (SFOC) strategy have been investigated in [13]. These techniques consist in the spectrum analysis of the stator currents, rotor currents, and rotor modulating signals. Two different methods to detect IPR faults in the stator and rotor of a wound rotor induction generator (WRIG) under closed-loop operation have been proposed in [14]. The proposed approaches are based on the discrete WT of the rotor currents and voltage signals. In [15], the capability of two different techniques for the detection of IPR faults in the DFIG stator windings has been evaluated. The authors compared the spectrum analysis of the stator current with the spectrum analysis of the instantaneous stator active power. In [16], the same methodologies were used to detect a IPR fault in the rotor windings. The results presented in [15, 16] led the authors to the conclusion that the instantaneous active power spectrum contains more potential fault-specific information than the stator current spectrum.
Abstract—This paper presents a new online diagnostic approach for the detection of stator and rotor inter-turn short circuit (ITSC) faults in doubly-fed induction generators (DFIGs) for wind power applications. The proposed technique is based on the Fast Fourier Transform (FFT) of the instantaneous stator reactive power. It is shown that stator and rotor ITSC faults in the DFIG can be reliably detected by the identification of spectral components at twice the supply frequency and at two times the slip frequency of the generator, respectively. Experimental results are presented, demonstrating the effectiveness of the proposed diagnostic technique for different generator operating conditions, namely for different values of the active and reactive power injected into the grid, different values of the wind speed, both when the generator operates in subsynchronous and supersynchronous mode. The diagnostic technique here reported was adopted for a remote, online and distributed wind farm condition monitoring system, whose architecture is briefly described at the end of the paper. Keywords—DFIG; inter-turn faults; fault diagnosis; reactive power.
I. INTRODUCTION Currently, one of the most popular configurations of wind turbine generators is based on DFIGs. In European Union countries, 55% of wind turbines are based on the DFIG [1]. One of the advantages of this system is that the back-to-back converter that supplies the rotor windings of the generator is rated for around 30% of the rated power of the generator, thus decreasing significantly its initial cost. At the same time, this configuration allows the generator to operate at variable speed, thus optimizing the operation of the wind turbine. In order to prevent unplanned downtimes and to minimize the maintenance costs associated to wind energy systems, it is vital to use online condition monitoring systems that monitor continuously or in a periodic basis the condition of the generator and the mechanical components associated to it. According to the survey of failures in wind power systems presented in [2], 8.9% of the downtime of a wind turbine is due to generator failures. According to a report about failures in wind turbines located in Germany
k,(((
In [17], the FFT and the EPVA of the stator currents are used in a first stage for the detection of ITSC faults in the stator windings under steady-state operating conditions. For the detection of that specific fault under time-varying load conditions, those authors proposed a technique based on the combination of the EPVA and the WT. In [18], the detection of ITSC faults in the stator windings of DFIGs operating under time-varying operating conditions was done by the use of the rotor current signals in conjunction with a rotormounted search coil voltage. In this case, the installation of the rotor search coil is an invasive procedure, hence limiting the applicability of this diagnostic approach. In [19], the Cumulative Sum algorithm has been proposed for the detection of ITSC faults in the stator windings of DFIGs. In [20], ITSC faults in the rotor windings of DFIGs are detected by the spectrum analysis of the stator currents, while in [21] it is proposed the use of a Luenberguer observer with the same aim, being the faulty phase identified by the analysis of the stator current residuals. Another observer-based approach is proposed in [22] to detect ITSC faults in the stator windings of DFIGs. In [23], an artificial neural network (ANN) has been used to detect and localize ITSC faults in the stator and rotor windings. The spectrum of the rotor currents has also been proposed for the detection of the degradation of slip rings /brushes of DFIGs [24]. In [25], a technique for the detection of eccentricity is also presented. Although a wide range of diagnostic techniques are proposed in the literature, with some advantages and limitations highlighted, the reliability of those techniques was not fully accessed for all the conditions under which a DFIG operates in an actual wind turbine. Furthermore, the definition of severity factors for each type of fault is important. This information allows the maintenance personnel to evaluate the extension/severity of the fault, thus allowing to make a decision about the stoppage of the generator for repair. To overcome some of these limitations, a new technique based on the FFT of the stator instantaneous reactive power for the detection of ITSC faults in the stator and rotor windings of the DFIG has been recently investigated [26]. This paper is a complementary study of [26], where additional experimental results are presented with regard to the operation of a DFIG under different operating conditions, and introduces an automatic condition monitoring system to be installed in wind farms, with remote data collection and centralized data storage, data processing, and decentralized visualization of the working conditions of the wind generators. The paper is presented in six main sections. After Introduction in Section I, the presentation of the new fault diagnostic method is explained in Section II. The details of the test rig are given in Section III. The experimental results are reported in Section IV. The implementation of the automatic diagnostic system is shown in Section V. Finally, section VI presents the major conclusions of this work.
II.
DIAGNOSIS OF FAULTS IN THE DFIG [26]
A. Control System of a DFIG DFIGs are usually controlled with a stator flux or stator voltage oriented vector control system. In any case, the control system comprises two inner rotor current control loops of high bandwidth (hundreds of Hertz) and two outer control loops, one to control the stator active power (injected into the grid) and the other one to control the stator reactive power. The bandwidth of the outer control loops is much lower than the bandwidth of the inner ones to achieve a stable operation of the whole system. The values of the bandwidth of the power control loops are typically in the range of a few Hertz. In the present work, in order to carry out all experimental tests along with the analysis of the generator behavior in healthy and faulty conditions, the control system shown in Fig. 1 was considered [26]. B. Analysis of a Stator Fault Let us consider a DFIG, running in steady-state with a rotor slip s . The stator windings of this generator are directly connected to the grid, whose voltages have a fixed amplitude, with a fundamental frequency of f s . The rotor windings of the DFIG are fed by a back-to-back converter that controls the d and q rotor current components, in a synchronous reference frame. After the appearance a fault in the stator windings of the DFIG, the resulting asymmetry will cause the stator currents to have a positive and a negative sequence component, at frequencies of f s and − f s , respectively. The stator (grid) voltages of the generator and the negative-sequence component of the stator currents will lead to a new spectral component in both the stator active and reactive powers of the DFIG, at a frequency of 2 f s . This frequency is much higher than the bandwidth of the two power control loops, therefore the reference rotor current components idr* and iqr* will be barely influenced by the existence of this fault. On the rotor side of the generator, the negative sequence component of the stator currents will induce rotor voltages at a frequency of
f r− = ( 2 − s ) f s .
(1)
Ps* Ps
iqr
* s
* dr
Q
* vqr
uqr*
iqr*
i Qs
udrc
udr*
dq
θes
idqs dq
* vdr
abc
dq
θm
abc iabcr
uabcs
θes
iabcs
Fig. 1 Control system of a DFIG.
vα* r
vβ* r
θr
idr
udqs
αβ
Converter
Considering that the frequency given by (1) is lower than the bandwidth of the current control loops, the control loops of the rotor currents will be able to impose rotor currents almost without any spectral component at a frequency of f r− .
For a stator fault, in a synchronous reference frame with the q-axis aligned with the stator voltages, and considering the stator currents having positive and negative sequence components denoted by the superscripts + and -, respectively, the total active and reactive powers of the stator are obtained by (2) and (3), respectively. Ps = 3 vqs iqs+ + 3 vqs iqs− = Ps{dc} + ΔPs{2 f s } 2 2 + 3 3 Qs = vqs ids + vqs ids− = Qs{dc} + ΔQs{2 f s } 2 2
In this case, for very low values of the rotor slip, the oscillations in the stator active and reactive powers at a frequency of 2sf s , will affect the outputs of the two PI power controllers. Therefore, the power controllers originate oscillations in the two reference rotor currents idr* and iqr* at the same frequency. Thus, the influence of the control system on the behavior of the generator should be considered for any effort to quantify the fault extension in the proximity of the synchronous speed. The effects of the rotor fault depend on the tuning of the PI power controllers and on some DFIG electric and mechanical parameters. An analogous phenomenon has already been observed in the diagnosis of rotor faults in induction motors [27]. This paper will not try to solve completely this issue, as it needs to be treated in detail in a separate and more comprehensive work. The rotor fault can be detected by the identification of a spectral component at a frequency of 2sf s in the total stator reactive power of the DFIG, based on (7). The only exception is when the DFIG operates at exactly the synchronous speed. In that case, the detection of the rotor fault will be compromised and it would need to rely on second-order effects, like the use of space harmonics created by the currents circulating in the stator and rotor windings.
(2) (3)
The firs terms on the second hand of (2)-(3) represent the active and reactive powers exchanged between the stator of the DFIG and the grid, being dc quantities. The second terms in the second hand of (2)-(3) are ac quantities with a frequency of 2 f s , and are a direct consequence of the stator fault, hence their identification in the spectrum of the active or reactive powers can be used for the detection of stator faults in the generator. In theory, in the considered reference frame, both the active and reactive powers could be used for the detection of the stator fault, because the stator currents iqs− and ids− have the same amplitude. Considering that the stator active power changes in a much wider range and in a faster way when compared to the reactive power, in this study, the spectrum analysis of the stator reactive power is used for fault diagnostic purposes.
(4)
D. Definition of Severity Factors In fault diagnosis, it is important not only to detect the faults but also to quantify their extension. For that purpose, severity factors (SFs) are usually defined, giving us a measure of how serious those faults are. For the case of a stator fault, a severity factor SFsf is defined by ΔQs{2 f s } SFsf (%) = ×100% . (8) Qsn
The interaction between this stator current component and the fundamental component of the stator flux will originate torque oscillations in the generator, at a frequency of | 2 sf s | . These torque oscillations will lead to speed oscillations at the same frequency that will cause the appearance of a second spectral component in the stator currents, at a frequency of
where ΔQs{2 f s } is the amplitude of the oscillating component in the stator reactive power at a frequency of 2 f s and Qsn = 3U sn I sn sin ϕ n is the rated stator reactive power of the DFIG. The stator and rotor reactive powers of a DFIG operating in healthy conditions and in steady-state (neglecting the copper losses) are given by (9) [28, 29]:
f usb = (1 + 2s ) f s .
Qr ≈ s ( K − Qs )
C. Analysis of a Rotor Fault After the appearance of a fault in the rotor windings, a primary fault-related spectral component will appear in the stator currents, at a frequency given by f lsb = (1 − 2 s ) f s .
(5)
f sψ , (10) Ls where Qr is the reactive power injected into the rotor of the DFIG, ψ ds is the stator flux linkage and Ls is the selfinductance of the stator windings. Since the stator voltages are imposed by the grid, K will be a constant value. Neglecting the rotor transient rotor inductance, if there is a variation in the rotor reactive power, the following relation will apply K = 3π
These speed oscillations will be reduced, because the drive train associated to a wind generator has a high inertia. Therefore, the amplitude of the current spectral component given by (4) will be much higher than the amplitude of the stator current component given by (5). The stator current components at frequencies given by (4) and (5), in a synchronous reference frame, will appear as spectral components at frequencies of −2sf s ( i lsb ) and 2 sf s ( i usb ), respectively. Hence, in that reference frame, the stator active and reactive powers of the DFIG with a rotor fault will be given by Ps = 3 vqs iqs+ + 3 vqs ( iqsusb + iqslsb ) = Ps{dc} + ΔPs{|2 sf s |} 2 2
dc comp.
dc comp.
ΔQr ≈ − s ΔQs .
(11)
For the case of a rotor fault, and inspired in the relation (11), the severity factor SFrf is defined by
(6)
SFrf (%) =
frequency of 2 sf s
Qs = 3 vqs ids+ + 3 vqs ( idsusb + idslsb ) = Qs{dc} + ΔQs{|2 sf s |} . 2 2
(9)
2 ds
(7)
ΔQs{|2 sf s |} ×100% s × Qsn
(12)
where ΔQs{|2 sf s |} is the oscillating component in the stator reactive power at a frequency of 2sf s .
frequency of 2 sf s
III.
TEST RIG
IV.
Fig. 2 shows the experimental setup used to perform the experimental tests documented in the next section. A 4 kW, 4-pole DFIG is mechanically coupled to an induction motor drive that emulates the wind turbine [26]. The rotor windings of the DFIG are fed by a typical back-to-back converter, which consists in a rotor-side converter and a grid-side converter, thus allowing the DFIG to operate at either subsynchronous and supersynchronous speeds [28]. To safeguard the insulation system of the rotor windings, a filter is added between the rotor-side converter and the rotor windings of the DFIG. For the case of the grid-side converter, a second-order LC filter was used. An incremental encoder with 2048 ppr mounted on the shaft of the DFIG measures its rotor position. A stator-flux oriented control system was chosen to control the DFIG [29]. The control system follows closely the diagram shown in Fig.1 and was implemented in a dSPACE 1103 platform. The reference values of the active power ( Ps* ) and reactive power ( Qs* ) injected into the grid by the DFIG (negative values mean that the power is flowing from the DFIG to the grid) could be adjusted through a graphical interface [26]. To emulate a ITSC fault, a variable resistor is connected in parallel with one of the phase windings of the DFIG [17]. Fig. 3 shows the emulation of an ITSC fault in the stator and rotor windings (the stator windings are delta-connected and the rotor windings are star-connected). Due to an ITSC fault, the impedance of the faulty phase windings is decreased. The number of shorted coils/turns can be adjusted by changing the value of the variable resistor.
EXPERIMENTAL RESULTS
Experimental tests were carried out for the DFIG operating in three different conditions: healthy state; a fault in one of its stator windings; a fault in one of its rotor windings. For all situations, several tests were conducted, at different values of the active and reactive power injected into the grid and different values of the rotor speed of the DFIG (subsynchronous and supersynchronous modes).
A. Healthy Operation Fig. 4 shows the stator current waveforms of the DFIG operating in healthy conditions, with a rotor speed of 1150 rpm, and when injecting 2000 W into the grid. As can be observed in this figure, the stator currents of the DFIG are perfectly balanced in healthy operating conditions. The time waveform and the spectrum of the stator reactive power of the DFIG, for the same operating conditions, are presented in Fig. 5. The spectrum of the stator reactive power presented in Fig. 5(b) clearly shows that in healthy conditions, the spectral components of the stator reactive power at the frequencies 2 f s (100 Hz) and 2sf s (23.2 Hz) are negligible. The obtained results are in agreement with the fact that the DFIG has no faults. For other operating speeds and different values of the active and reactive power injected into the grid, similar results were obtained. B. Stator Fault Subsequently, a fault was introduced in phase A of the stator windings, using a resistor. The stator current waveforms and the stator reactive power of the DFIG, when operating with a stator fault, for two different values of the active power injected into the grid (0 W and 2000 W) are shown in Fig. 6-8. In both cases, the rotor speed was 1150 rpm and the reactive power was set to 0 VAr. When the reference value of the active power was set to 0 W, the stator currents of the DFIG are balanced, although they constitute a negative sequence component. This component is a direct consequence of the stator fault and in terms of stator reactive power, will originate a spectral component at a frequency of 100 Hz, as can be seen in Fig. 7. This result is important because it shows that any diagnostic method based on the measurement of the unbalance of the stator currents for the detection of this type of fault will fail, and will not give a correct indication about the condition of the generator. As can be observed in Fig. 6(b), after increasing the injected active power to 2000 W, the currents have positive and negative sequence components and, therefore, they are clearly unbalanced.
Ωm
θ enc
Ps* Qs*
Stator currents [A]
Fig. 2 Experimental setup.
(a) (b) Fig. 3 Emulation of the ITSC fault in: (a) stator and (b) rotor windings.
8
iA
S
4
iB
S
iC
S
0 -4 -8 1
1.005
1.01
1.015
1.02
1.025
1.03
1.035
1.04
1.045
1.05
Time [sec]
Fig. 4 Stator currents of the DFIG, operating in healthy conditions (n=1150, Ps* = −2000 W , Qs* = 0 VAr ).
QS [VAr]
1500 1000 500 0 -500 -1000 -1500 1
Q
The stator fault can be identified by observing the spectral component at 100 Hz as presented in Fig. 8(b). Furthermore, the comparison of the amplitudes of this spectral component, presented in Fig. 7(b) and Fig. 8(b), shows that this value did not change significantly for the two aforementioned conditions. Results obtained for other operating conditions, namely different values of the injected active and reactive powers and different values of the rotor speed, demonstrate the independence of this fault indicator with regard to the working conditions of the DFIG. Fig. 9 shows the evolution of the severity factor defined by (8), as a function of different operating conditions of the DFIG. These results clearly show that this severity factor is a good indicator about the degree of asymmetry of the generator or the extension of the fault, because it is highly independent of all operating conditions of the DFIG.
S
(a) 1.005
1.01
1.015
1.02
1.025
1.03
1.035
1.04
1.045
1.05
Time [sec]
1000
Q [VAr]
800 600
S
400
(b)
200
X=100 Y=33
X=23.2 Y=18
0 0
20
40
60
80
100
120
Frequency [Hz]
Stator currents [A] Stator currents [A]
Fig. 5 Stator reactive power of the DFIG in healthy conditions (n=1150 rpm, Ps* = −2000 W , Qs* = 0 VAr ): (a) time waveform, (b) spectrum. 8
i
A
4
i S
B
S
i
C
C. Rotor Fault The results shown in this subsection were obtained for the operation of the DFIG with a rotor fault, which was emulated by connecting a resistor in parallel with phase a of the rotor windings. The time waveform and spectrum of the stator reactive power when the DFIG operates with a rotor fault and with n=1150 rpm, Ps* = −2000 W and Qs* = 0 VAr are shown in Fig. 10. As can be observed clearly in Fig. 10(b), The spectrum of the stator reactive power has a spectral component at a frequency of |2sf| (23.2 Hz), related to the existence of the rotor fault. The behavior of the severity factor defined in (12), as a function of different generator operating conditions, namely rotor speed, active and reactive powers injected into the grid, is shown in Fig. 11. As can be seen, this severity factor is almost independent of the values of active and reactive power injected into the grid. However, as stated before, the rotor speed influences the obtained results.
S
(a)
0
-4 -8
8
iA
iB
S
4
S
iC
S
0
(b)
-4 -8 1
1.005
1.01
1.015
1.02
1.025
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1.035
1.04
1.045
1.05
Time [sec]
Q S [VAr]
Fig. 6 Stator currents of the DFIG with a stator fault (n=1150 rpm, Qs* = 0 VAr ): (a) Ps* = 0 W , (b) Ps* = −2000 W . 1500 1000 500 0 -500 -1000 -1500 1
Q
(a) 1.005
1.01
1.015
1.02
1.025
1.03
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Time [sec]
1000
X=100 Y=914
800 600
S
Q [VAr]
S
400
(a)
(b)
200 0 0
20
40
60
80
100
120
Frequency [Hz]
QS [VAr]
Fig. 7 Stator reactive power of the DFIG with a stator fault (n=1150 rpm, Ps* = 0 W , Qs* = 0 VAr ): (a) time waveform, (b) spectrum. 1500 1000 500 0 -500 -1000 -1500 1
(b) Q
S
(a) 1.005
1.01
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1.05
Time [sec] 1000
S
Q [VAr]
(c)
X=100 Y=887
800 600 400
(b)
200
Fig. 9 Evolution of the severity factor, defined for the stator fault, with: (a) rotor speed ( Ps* = −2000 W , Qs* = 0 VAr ), (b) injected active power (n=1150 rpm, Qs* = 0 VAr ), (c) injected reactive power (n=1150 rpm, Ps* = −1000 W ).
0 0
20
40
60
80
100
120
Frequency [Hz]
Fig. 8 DFIG stator reactive power for a stator fault (n=1150 rpm, Ps* = −2000 W , Qs* = 0 VAr ): (a) waveform, (b) FFT spectrum
QS [VAr]
1500 1000 500 0 -500 -1000 -1500 1
A general overview of the diagnostic system architecture is shown in Fig. 12, where are visible the interactions across the internet between the different elements and layers of the whole system. In each wind generator, located at the wind farm, a remote data acquisition system, based on a CompactRIO (cRIO) platform by National Instruments, is installed. This system measures the raw data corresponding to two stator currents and the grid voltages to which the stator of the DFIG is connected to, as the diagnosis algorithm proposed in this system relies on the generator reactive power to detect faults. One of the most important features to include in this project is the possibility of direct communication between the system administrator and the remote cRIO platforms in order to deploy new software builds, update firmware and perform any other maintenance tasks. To make this possible, a virtual private network (VPN) with OpenVPN software was established, which allows direct and secure communications between the administrator and any on-site device and from the devices to the public internet. As cRIO does not include any VPN client, a router was installed at the generator site, to run a VPN client and stay permanently connected to the VPN server. The files with the acquired raw data are uploaded from the cRIO to the FTP server. Then, the diagnostic software, which is running on the FTP server, detects the arrival of a new data file and runs the diagnostic routine upon it. When finished, the diagnostic software communicates with the Kisense web server, uploading the diagnostic results to be stored in the Kisense database. Finally, the Kisense platform can be accessed by the system users or administrators to show the current or past information about the generators being monitored.
QS
(a) 1.02
1.04
1.06
1.08
1.1
1.12
1.14
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Time [sec] 1000
S
Q [VAr]
800 600 X=23.2 Y=324
400
(b)
200 0 0
20
40
60
80
100
120
Frequency [Hz]
Fig. 10 Stator reactive power of the DFIG operating with a rotor fault (n=1150 rpm, Ps* = −2000 W , Qs* = 0 VAr ): (a) time waveform, (b) spectrum.
(a)
(b)
Final user
(c)
Fig. 11 Severity factor for the operation of the DFIG with a rotor fault, as a function of: (a) rotor speed ( Ps* = −2000 W , Qs* = 0 VAr ), (b) injected active power (n=1150 rpm, Qs* = 0 VAr ), (c) injected reactive power (n=1150 rpm, Ps* = −1000 W ).
An alternative is to use the space harmonics associated to the stator and rotor windings but this requires a more detailed knowledge of the DFIG which is being monitored, which may be neither practical nor easy in some circumstances. V.
User
Public internet
ISA
Kisense database
Kisense web server
AUTOMATIC DIAGNOSTIC SYSTEM FOR WIND FARMS
An automatic diagnostic system for wind generators is being developed under the scope of an ongoing research project, whose main aim is to monitor the condition of DFIGs located in different wind farms spread all over the world. The diagnostic technique presented in this paper was adopted and is being implemented in a remote online wind farm condition monitoring system with central data storage and visualization capabilities. This project is being carried out by Endiprev Lda, a specialized predictive maintenance company which is responsible for the remote data acquisition, raw data storage and generator fault diagnosis, in partnership with ISA – Intelligent Sensing Anywhere S.A, which is in charge of the web user interface and presentation of the generator diagnostic results to the final user.
Endiprev
Public internet Administrator
VPN server
Public internet
FTP server
Machine site
Public internet CompactRIO
Router
Fig. 12 Architecture of the online monitoring system being developed for wind farms.
As the diagnosis algorithm proposed in this system relies on the machine reactive power to detect the faults, only stator currents and voltages need to be measured. This is a great advantage of the diagnostic system, since at the generator site, the stator of the generator is easily accessible. On the other hand, as the data processing is centralized, the diagnostic algorithm can be performed online with daily scheduled data acquisitions for instance, or at an user request when needed. VI.
[9]
[10]
[11]
CONCLUSION
This paper proposes a new diagnostic approach based on the spectrum analysis of the stator instantaneous reactive power for the detection of stator and rotor faults in DFIGs. For the case of a stator fault, the detection of a spectral component at two times the grid frequency is an indicator about the presence of this type of fault, while a rotor fault can be detected by the identification of a spectral component at a frequency of 2sf s . Severity factors were defined for both types of faults which proved their reliability and independence with regard to several generator operating conditions, namely active and reactive power injected into the grid and rotor speed. The exception is the detection of the rotor fault in the vicinity of the synchronous speed which is not possible by using this approach. In that case, the fault may remain unnoticed until the generator operates at a speed different from this value. Further research is currently ongoing in order to improve the applicability of this diagnostic approach, being scheduled the implementation of a pilot project in an actual wind farm based on DFIGs.
[12]
ACKNOWLEDGMENT This research is supported by ENDIPREV Lda. (www.endiprev.com) and partially financed by the European Regional Development Fund (ERDF) under project number CENTRO-07-0202-FEDER-030277.
[20]
[13]
[14]
[15] [16] [17] [18]
[19]
[21] [22]
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