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FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine
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FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine Amel Adouni a,n, Dhia Chariag a, Demba Diallo b, Mouna Ben Hamed a, Lassaâd Sbita a a b
Laboratoire Systèmes Photovoltaïques, Eoliens et Géothermaux, Ecole National d’Ingénieur de Gabes, Univérsité de Gabes, Tunisia Group of Electrical Engineering – Paris (GEEPS), (CNRS, Centrale Supélec, UPMC, Univ. Paris-Sud), 91192 Gif Sur Yvette, France
art ic l e i nf o
a b s t r a c t
Article history: Received 20 January 2016 Received in revised form 1 May 2016 Accepted 16 May 2016 This paper was recommended for publication by Jeff Pieper.
As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs. & 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Keywords: Wind Turbine Low Voltage Ride Through Fault Detection and Identification Electrical grid Artificial Neural Network
1. Introduction The pessimist forecasts of fossil-fuel supplies and the pollution are considered a worldwide warning, especially with the predictable world population growth. Since the energy is the lifeblood of modern societies, the renewable sources (wind, solar, geothermal) were considered the promising sources to fulfill the world energy demand [1]. In the last decades, the conversion energy systems markets continued to emerge in several countries. The Wind Turbine (WT) was no exception. According to the new statistic in 2014, more than 20% of electricity demand is provided by the wind power [2]. The WT as a power source is connected to the grid. The last one is vulnerable to faults as LVRT. The Fault Detection and Identification (FDI) of power sources is considered an important subfield of control engineering [3]. A LVRT occurred in the grid propagates to affect the WT. The Double Fed Induction Generator (DFIG) is very sensitive to external disturbances such dip voltage. This type of fault could occur suddenly in the grid. As result, exceeding urge currents go through the rotor n
Corresponding author. E-mail addresses: [email protected] (A. Adouni), [email protected] (D. Chariag), [email protected] (D. Diallo), [email protected] (M. Ben Hamed), [email protected] (L. Sbita).
terminals. Therefore, the Machine Side Converter will be not able to support this large current and it will get damaged. A part from this, a huge electromagnetic torque pulsations will be generated and hence, an increasing in rotor speed will be detected. This reduces the gearbox life time [4]. Regarding to all these drawbacks, many countries are updated the grid code. An issue was involved in the modern electrical grid rules; WT needs to operate continually even in presence severe grid faults specially the LVRT. To response to that issue, an early and efficiency FDI procedure based on Artificial Neural Network (ANN) has been elaborated. The Artificial Neural Networks have been extensively employed in several fields of engineering application. This technique has been used successfully to model complex nonlinear dynamic systems [5,6]. The ANN does not need the use of complex analytic formulations. It requires a database and the targets to train the ANN until it reaches an acceptable error. A well-trained procedure achieves accuracy even in the presence of noise and uncertain data. ANN is also a good candidate to predict the nonlinear dynamic behaviors for control strategy [7]. However, the success of the ANN is highly dependent on the database that can be limited due to capability of the experimental prototype. Therefore when used for diagnosis, monitoring…[8] of faults in the electrical grid, signal processing and artificial intelligence techniques are used [9] for classification.
http://dx.doi.org/10.1016/j.isatra.2016.05.009 0019-0578/& 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
A. Adouni et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
v
Ωm Ωt
i as
i ts
i bs
i bt
i cs
i ct
Cm Ct
R
icr i br i ar
i f1 ’ i f2 ’ i f3 ’ ired vdc
iond
Lf
i cond
Rf
Cem * Qs *
Vdc *
Qf*
Fig. 1. Topology of the Wind Turbine.
0.9 Cpmax1
0.8
Table 1 Symmetrical dip voltage description.
0° 2° 3° 4°
0.7
Type A
0.6 0.5
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ 3
Phasor representations
ec
vc
va ea
eb
vb
p
C (λ )
Cpmax2
Voltages expressions
0.4
Cpmax3
0.3 0.2
Cpmax4
0.1 0
0
2
4
6
8
10
12
14
16
λ Fig. 2. The evolution of the power coefficient according to the tip speed ratio.
In order to diagnosis a dip voltage fault, many research works are developed and rely on this issue. In [10], the detection problem was resolved using the generalized likelihood ratio test (GLRT). It provides a competitive performance in fault starting instant detection. It is important to have the precise depth of dip voltage because it characterizes the fault severity and it is necessary in order to generate the appropriate maintenance. Therefore, in the paper [11] a detection scheme for voltage swell and low voltage based on using a simple relay and low pass filter is applied to the supply voltage signal. The used technique generates in-phase fixed magnitude reference signal with very low total harmonic distortion. This signal
will compared to the supply signal and any variation in magnitude of the supply could be detected. The dip voltage occurred in the power distribution grid are studied in [12]. In this work, the principal objectives were classifying the fault type and locate the fault position in a given power delivery network. In order to achieve that purpose, a sensitive phase locked loop used to trigger the fault inception. After that, damped sinusoid of arbitrary temporal support is used to decompose the captured signals. In [13], the space vector is used for detection and identification of the voltage dips and swells. This method is based on space vector trajectory in complex plane analysis and in the zero-sequence voltage. In another work [14], using polarization ellipse in 3-D coordinates, unique signatures and parameters of three-phase voltage signals characterized the three phase voltage could be extracted and used to develop a method detect and classify the voltage dips and swells. There are five ellipse parameters could be used azimuthal angle, elevation, tilt, semi-minor axis, and semi-major axis. To distinguish between the different voltage sag, three characteristics (amplitude, harmonics and fault time) could be used. The Hilbert-Huang transform (HHT) is a time frequency representation. It is considered as a powerful technique to extract the voltage dip features. It could detect and classify correctly and concisely the dip voltage caused by line short circuit faults and motor induction start up [15]. In [16], Based on matrix pencil method, a novel technique was elaborated to classify
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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3
Table 2 Asymmetrical dip voltage description. Type B
Sub-Type Phase a
Phase b
Phase c
C
Phase ab
Phase ac
Voltages expressions pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ V 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ V 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ V 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ V 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ V 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ V 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwt αÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt þ α 3 pffiffiffi 2π vc ðtÞ ¼ V 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtþ αÞ pffiffiffi 2π vb ðtÞ ¼ V 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ α 3
Phasor representations
ec
E
Phase ab
Phase ac
Phase bc
pffiffiffi va ðtÞ ¼ V 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt α 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ þ α 3
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ V 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ V 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ V 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt 3
va
eb vc
ec
va
ea eb ec
vb vc
va ea
eb
vb
ec
vc ea
eb v b ec vc
va va ea
eb Phase bc
vc
vb
ec va
vc vb
ea
eb
ec
eb ec
vc va
ea
vb vc va
ea
eb v b
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Table 2 (continued ) Type
Sub-Type
Voltages expressions
Phasor representations
pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ 3
ec vc
va
ea
vb eb
Table 3 Electrical grid indicators. Amplitude
Angle
Healthy function
Ama ¼V, Amb ¼ V, Amc ¼ V
Fault type A
Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ (1 d)V
Fault type B
Fault type C
Fault type E
Phase a
Ama ¼(1 d)V, Amb ¼ V, Amc ¼V
Phase b
Ama ¼V, Amb ¼ (1 d)V, Amc ¼V
Phase c
Ama ¼V, Amb ¼ V, Amc ¼ (1 d)V
Phase ab
Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ V
Phase ac
Ama ¼(1 d)V, Amb ¼ V, Amc ¼(1 d)V
Phase bc
Ama ¼V, Amb ¼ (1 d)V, Amc ¼(1 d)V
Phase ab
Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ V
Phase ac
Ama ¼(1 d)V, Amb ¼ V, Amc ¼(1-d)E
Phase bc
Ama ¼V, Amb ¼ (1 d)V, Amc ¼(1 d)V
the three phase voltage dips. It was improved with an ellipse fitting algorithm. Another work focus in voltage disturbances detection and classification was developed in [17]. The intelligent systems and signal processing techniques are used for a voltage disturbances detection and classification. The decision is made using four modules: the first dedicated to operation state description, the second module used multi-resolution analysis, the discrete wavelet transform and entropy norm concepts to extract the features. The signal signature is processed via standardization and codification in the third module. Using a Fuzzy-ARTMAP neural network, the fourth module classifies the type of disorder. The improper protection of WT during a voltage dip produces the damage of the system. Classifying multistage and real voltage dips when the measurement data are recorded in transient stage is present in [18]. For this purpose the voltage-space vector is applied. The proposed technique is tested in wind farms located in the Spanish region of Castilla-La Mancha. In [19], the authors used a data-driven. Three features extracted from the current vector in the Concordia stationary reference frame. Then, they are evaluated using Principal Component Analysis. In other work [20], a non-parametric method was suggested to diagnosis a dip voltage in WT. It based on the current trajectory analysis. In the cited papers, the authors focus in a special type of dip voltage [20]. They are limited in one type (dip in one phase of the electrical grid, or symmetrical dip voltage…). The GLRT detector is competitive, but the main problem is the decision threshold [6]. The simple relay and low-pass filter is a hardware solution that does not provide an efficient decision since it also depends on the threshold [7]. The Principal Component Analysis (PCA) is a datadriven technique suitable for systems with high dimensions and if an accurate analytical model is unavailable. Moreover, the fault detection time duration when using PCA may be long and the method is not robust against the disturbances [15]. Even if the Kernel PCA (KPCA) handles the noise, occlusion and missing data, this method does not allow fast fault detection. In addition, in [16]
2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ α; φb ¼ 2π 3 þ α; φc ¼ 3 2π φa ¼ α; φb ¼ 2π 3 ; φc ¼ 3 α 2π φa ¼ 0; φb ¼ 2π 3 α; φc ¼ 3 þ α 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
ANN1
ANN2 =0 healthy case
va (t) ANN3 ANN7
D
vb (t)
=1 Fault type A =2 Fault type B =3 Fault type C
ANN4
=4 Fault type E vc (t) ANN5
ANN6
Voltage indicators generation step
Voltage indicators analysis or Detection and identification step
Fig. 3. Dip voltage fault diagnosis.
the method is load sensitive as it is based on the measured the currents. Comparing to the aforementioned works, the proposed FDI exhibits different advantages such as:
it does not require the setting of a threshold, it can be implemented in open or closed loop as it only requires the voltages measurements,
it is not load dependent.
Since, there are many dip voltage types could affect the electrical grid, the study of all these faults are required in order to take the accurate monitoring tools. Hence, it is important to identify all of them. In this paper, the targeted goals are firstly to detect
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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rapidly the mode function (healthy or faulty) and secondly to improve the identification step in order to minimize the false alarm caused by the noise and the unknown inputs. As a solution, a new ANN architecture is proposed. The Artificial Neural Network is a supervised method which efficiently depends on the training step that should be done with a rich database, which contains as much information as possible including not only the healthy and faulty cases but also the noise and the unknown inputs. Therefore, during the learning procedure, the outputs are built to be robust to false alarms (due to noise and unknown inputs) and sensitive to fault occurrence. The paper is structured in 5 sections. Section 2 is a short description of WT model. Section 3, deals with the proposed FDI
5
scheme and Section 4 shows the simulation and experimental results and discusses the effectiveness of this procedure. The final section concludes this current research work.
2. Wind Turbine model The topology of the WT is illustrated in Fig. 1. Mainly, there are four elements: the Turbine, the Gearbox, the DFIG and the back-toback converter. 2.1. Turbine model The blades are set in rotation due to the wind speed. The kinetic power wind is equal to [21] P c ¼ 0:5ρSV 3w
va(t) Si vb(t)
ð1Þ
where V w is the wind speed, ρ is the air density and S is the surface swept by the blades. A percentage of the kinetic power will be captured by the turbine and converted to the aerodynamic power. This percentage represents the power coefficient so [22] P aer ¼ C p ðλ; β ÞP c ¼ 0:5ρπ R2 V 3w C p ðλ; βÞ
ð2Þ
Where, R is the blade length and C p is a nonlinear function and depends of the Tip Speed Ratio (TSR) λ and pitch angle β . It defined as [21]
vc(t)
C p ðλ; β Þ ¼ ð0:5 0:167ð β 2ÞÞ sin
Fig. 4. Artificial Neural Network for detection.
π ðλ þ 0:1Þ 0:00184ðλ 3Þð β 2Þ 18:5 0:3ð β 2Þ ð3Þ
ϕa
=0 healthy case
The TSR is defined as the ratio between the linear blade tip speed Ωt and the wind speed. It is expressed as
Am a
=1 Fault type A
λ¼
ϕb
=2 Fault type B
ANN7
=3 Fault type C
Am b
ϕc Am c
Décision D
RΩt Vw
ð4Þ
The aerodynamic torque is expressed as [22] C aer ¼
P aer
Ωt
¼
1 πρR3 V 2w C p ðλ; βÞ 2λ
ð5Þ
=4 Fault type E 2.2. Double Fed Induction Generator model The model of the DFIG is described in the synchronous reference frame. The stator and rotor voltages along the d-axis and q-axis are
Fig. 5. Voltage indicators analysis step.
Table 4 Voltages indicators and corresponding decision D. Indicators
Decision: D
Normal function
Ama ¼V, Amb ¼ V, Amc ¼ Vφa ¼ 0; φb ¼
Fault type A
2π Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ (1 d)Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
1
Phase a
2π Ama ¼(1 d)V, Amb ¼ V, Amc ¼Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
2
Phase b
2π Ama ¼V, Amb ¼ (1 d)V, Amc ¼Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
2
Phase c
2π Ama ¼V, Amb ¼ V, Amc ¼ (1 d)Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
2
Phase ab
2π Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ Vφa ¼ α; φb ¼ 2π 3 þ α; φc ¼ 3
3
Phase ac
2π Ama ¼(1 d)V, Amb ¼ V, Amc ¼(1 d)Vφa ¼ α; φb ¼ 2π 3 ; φc ¼ 3 α
3
Fault type B
Fault type C
Fault type E
2π 3 ; φc
¼
2π 3
0
Phase bc
2π Ama ¼V, Amb ¼ (1 d)V, Amc ¼(1 d)Vφa ¼ 0; φb ¼ 2π 3 α; φc ¼ 3 þ α
3
Phase ab
2π Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
4
Phase ac
2π Ama ¼(1 d)V, Amb ¼ V, Amc ¼(1 d)Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
4
Phase bc
2π Ama ¼V, Amb ¼ (1 d)V, Amc ¼(1 d)Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
4
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 6. (a) Electrical grid in case of dip voltage type B. (b) Amplitudes outputs detection step. (c) Angles outputs detection step. (d) Decision signal D.
expressed as following [23]:
to the generator speed. The gearbox is characterized by a ratio G:
8 dφ vsd ¼ Rs U isd þ dtsd ωs U φsq > > > > > > < vsq ¼ Rs U isq þ dφsq þ ωs U φsd dt
Ωt ¼
dφ > vrd ¼ Rr Uird þ dtrd ωr U φrq > > > > > d : v ¼ R U i þ φrq þ ω U φ rq r rq r rd dt
The stator and rotor fluxes are expressed by 8 φ ¼ Ls isd þMird > > > sd > < φsq ¼ Ls isq þ Mirq
φrd ¼ Lr ird þ Misd > > > > : φrq ¼ Lr irq þ Misq
ð6Þ
Ωm G
C aer ¼ GC m
ð8Þ ð9Þ
where Ωm is DFIG shaft speed and C m is the mechanical torque. 2.4. Wind Turbine control
ð7Þ
where Rs ; Rr are the resistance of stator's and rotor's winding. Ls ; Lr are stator's and rotor's self inductance, M is mutual inductance, ωr and ωs are respectively the rotor's angular speed and grid angular frequency, isd , isq , ird , irq are the stator's and rotor's currents. 2.3. Gearbox model The turbine and the DFIG are connected via the Gearbox. It multiples the turbine shaft low speed in order to accommodates it
As shown in Fig. 1. three main controls are necessary for the proper functioning of the system. The first is the Maximum Power Point Tracking called MPPT. The second is the Machine Side Converter (MSC) control and the third is the Grid Side Converter (GSC) control. Fig. 2 represents the relation between the power coefficient and TSR according to pitch angle. It is observed that the maximum of power coefficient is obtained in the optimal TSR. In order to maximize the aerodynamic power, the WT should operate in the optimal operating point. The DFIG mechanical speed Ωm depends on pitch angle and electromagnetic torque C em . When the pitch angle is fixed, the C em should attends the optimal value in order to achieve the optimal power. Therefore, the generator shaft should rotate in the mechanical
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 7. (a) Electrical grid in case of dip voltage type B, (b) amplitudes outputs detection step, (c) angles outputs detection step, and (d) decision signal D.
principal equations
speed which verifies the following relation: C em ¼ K Ωm 2
ð10Þ
if d ¼
The MSC control is accomplished using the stator-flux oriented control (SFOC). It consists to align the stator flux vector with the daxis position. Therefore, the q-axis stator flux is set to 0 and the electromagnetic torque is expressed as following:
if q ¼
where K is a constant equal to K ¼
C em ¼ pφsd isq
C p_
max
ρπ R
M irq Ls
vsq φsd vsq M Qs ¼ i Ls rd Ls
ð14Þ
5
2G3 λopt 3
ð11Þ
Using the stator and rotor flux expressions, the active and reactive stator power are given P s ¼ vsq
Q f vsq
ð12Þ
ð13Þ
The C em becomes C em ¼ pM Ls φsd irq . Since the C em and P s depend on q-axis rotor current. The MSC should deliver the desired reactive power Q s and the C em . The DFIG is connected directly to the grid via the stator and through the GSC. The control of this converter has two roles: maintain the DC bus voltage constant and keep a unity power factor at the point of connection to electrical grid. The control of the GSC is based on the d–q axis filter control using these
P f vsq
ð15Þ
where P f is the filter active power and Q f is the filter reactive power (set to 0). More details about the adopted strategy are given in [21].
3. Fault Detection and Identification procedure 3.1. Faults description To describe the dip voltage, the ABC classification is used. It is the most frequently classification cited in literature. That because it is suitable for many applications. The temporal voltage expressions of the three phases under symmetrical and asymmetrical faulty function are given respectively in Tables 1 and 2. The magnitude of the pre-fault phase voltage is denoted by e and in the faulty case it is indicated by v [24]. 3.2. Fault detection and isolation procedure Table 2 shows the expressions of the grid voltages for the four fault types. Comparing them to those in healthy cases, it has been noticed that the dip voltage affects the amplitude and/or the
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 8. (a) Electrical grid in case of dip voltage type C, (b) amplitudes outputs detection step, (c) angles outputs detection step, and (d) decision signal D.
phase, which will be the features for fault detection. Since the electrical grid has three phases we have therefore six features. One ANN generates each feature so we have six ANNs. These six indicators are useful to describe the each phase state: – – – – – –
Ama amplitude phase a, Amb amplitude phase b, Amc amplitude phase c, φa angle phase a, φb angle phase b, φc angle phase c.
Depending on the fault type the amplitude and the angle phase are deflected. Referring to Table 2, all the indicators are extracted and summarized in Table 3. The interpretation of the table proves that the analysis of these two indicators is sufficient to detect rapidly and identify accurately the fault. For that purpose the ANN was used. This technique guaranties the immunities to the unknown inputs and noise and as consequence robustness to the false alarms generated by these signals. Where – d is the dip voltage magnitude, – α the angle deviation. The proposed algorithm (Fig. 3) is divided into two steps: the first is dedicated to the voltage indicators generation and the second to the fault indicators analysis it allows the detection and identification in the same time. The first step is composed of six ANNs: ANN1, ANN2, ANN3, ANN4, ANN5, ANN6. Fig. 4 gives the internal detail for each ANN. Fig. 5 represents the second step.
3.2.1. Voltage indicators generation step The aim of this step is to generate the voltage indicators. So, six ANNs are dedicated to the six indicators. The first neural network has three inputs va(t), vb(t), vc(t) and one output Ama which is the magnitude of the voltage in phase a. The five others are devoted respectively to Amb, Amc, φa , φa , φa . Each of these ANN is composed of three layers: the first has 7 neurons with tang-sigmoid hidden neuron function and the second has two neurons with tangsigmoid function activation. Each ANN is represented by Fig. 4. The learning procedure based on Table 3 is based on the Levenberg–Marquardt algorithm. This algorithm is used to solve nonlinear least squares problems when fitting a parameterized function by searching the suitable parameters to reach the optimal minimum error between the data points and the function. For example, in the first ANN1, the inputs are va(t), vb(t), vc(t), the output is y¼Ama. So, y¼Ama ¼ f(va(t), vb(t), vc(t), pi). Where pi is the neuron coefficient. The desired output is Ama_d. Hence, the squared error is (y Ama_d)2. There are other algorithms such as the gradient descent algorithm and the Gauss–Newton algorithm. The Levenberg–Marquardt interpolates the Gauss–Newton algorithm and the gradient algorithm. In the gradient descent algorithm, the parameters are updated by reducing the sum of the squared errors in the steepest-descent direction. In the case of Gauss–Newton algorithm, this sum is minimized by assuming that the least squares function is locally quadratic, and finding the minimum of the quadratic. Despite the Levenberg–Marquardt algorithm is slower than the Gauss–Newton algorithm, it is more robust than the Gauss–Newton and can find the desired settings even if the initial ones are very far from the final minimum. Where Si could be Ama, Amb, Amc, φa , φb , φc .
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 9. (a) Electrical grid in case of dip voltage type E, (b) amplitudes outputs detection step, (c) angles outputs detection step, and (d) decision signal D.
Grid connecting and dip voltage block
Back-to-back converter
Servo-motor
DFIG
Grid side connexion
Wind turbine side connexion Parameters dip voltage
Voltage and current sensors
Three phases dip voltage injection
Fig. 11. Grid connection block and dip voltage faults injection.
oscilloscope
Fig. 10. Experimental prototype.
3.2.2. Voltage indicators analysis step Voltage indicators analysis step is the second step. It has been showed in Fig. 5. The goal of this step is to identify the fault type. It looks for the mode function (healthy or faulty) and in the case of dysfunction it looks for which type of fault was occurred. This step
is also based on ANN. It is very important because, it allows making the proper decision and calculating the accurate order to keep the system in safety operation. According to Table 3. that is deduced from Table 2. each fault is described by the vector [Ama, Amb, Amc, φa , φb , φc ]. As it observe that each fault is different to the others by at least one indicator. If a signal that depends on all the features and deviates from 0 (healthy case) to the corresponding value according to the features changes is generated, the accurate
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Fig. 12. Graphical interface.
decision is guaranteed. For that purpose, the ANN dedicated to the second step has 1 output which is the decision signal D. The ANN is composed by 2 layers: 7 neurons in hidden layer with tangsigmoid function and 1 neuron in output layer with linear function. The signal inputs are φa , Ama, φb , Amb, φc and Amc generated by the first step. The network is trained in such a way that a unique value of the decision signal corresponds to the adequate combinations of this vector. So there is a unique solution for each fault. If the fault subtypes were to be detected, other values of the decision signal should be included for fault discrimination. Table 4 proves that the decision is guaranteed.
4.1.1. Case fault type A A dip voltage is injected between 0.4 s and 0.85 s (Fig. 6 (a)). Fig. 6(b) and (c) shows voltage indicators behaviors. The three amplitudes signals decrease during the same time interval. For angles signals (φa, φb and φc) are equal respectively to 0, 2π/3, 2π/3. The decision (Fig. 6(d)) is equal to 1 which corresponds to a fault type A.
4.1. Simulation results
4.1.2. Case fault type B The fault type B is a dip voltage in one phase (a or b or c) and the two others still in healthy function. The first case is shown in Fig. 7(a) between 0.1 s and 0.3 s. It is a 30% dip voltage affecting the line a. The voltage amplitude signal Ama is the only signal deviate between 0.1 s and 0.3 s whereas the all other signals remain unchanged (Fig. 7(b) and (c)). According to Table 3 all the indicators corresponds to fault type B. The signal D (Fig. 7(d)) proves that it is the fault type B. In fact, it equal to 2 between 0.1 s and 0.3 s. Between 0.4 s and 0.6 s a dip voltage in phase b is injected (Fig. 7(a)). Therefore, the signals Ama and Amc (Fig. 7(b)) are unvarying, but the one Amb generated by the ANN2 deviates from the original value between 0.40 and 0.6 s. This means that the fault started and disappeared 0.40–0.6 s. The other signals (Fig. 7(c)) remain constant. The decision D changes from 0 to 2 confirming that this is the fault type B. The third case is a dip voltage in phase c as shown in Fig. 7(a). It occurred between 0.7 s and 0.9 s. As a consequence, only the signal Amc deviates in same range of time. This type of fault corresponds to type B, which corresponds to a Decision D equal to 2.
In order to evaluate the efficiency of the method, the algorithm is implemented in Matlab Simulinks. A dip voltage in the electrical grid is injected at different times.
4.1.3. Case fault type C In this case, the dip voltage affects the line a and b (Fig. 8(a)) during the period [0.30 s, 0.75 s]. As a result, the signals Ama, Amb,
3.2.3. Contribution and novelties The proposed scheme based on the use of a new Artificial Neural Network architecture and the voltage grid measurements can detect and identify the four fault types (A, B, C, E). All the modes are included, from healthy to faulty cases. Thanks to an efficient learning step, the method is robust against false alarms due the unknown inputs and environmental noise. One of the main advantages of the method is that it does not require the current's measurements. Therefore it's independent from the load variations. The detection time duration is also short as the signal decision spots quickly the modifications of the voltage indicators.
4. Results and discussion
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 13. (a) Electrical grid in case of dip voltage type B, (b) amplitudes outputs detection step, (c) angles outputs detection step, and (d) decision signal D.
φa and φb (Fig. 8(b) and (c)) deviate, the signals Amc and φa keep unchangeable during the same period. The signal decision D changes from 0 to 3 confirming that this is the fault type C. The second case is a dip voltage occurred between 1.25 s and 1.7 s. It affects the line a and c (Fig. 8(a)). In that period, only the signal Amb and the phase φb (Fig. 8(b)) remains constant. The others signals (Ama, Amc, φa and φc) generated by the ANN1 and ANN3 deviate from the original value during the same range of time. The decision D becomes equal to 3. During the period 2.35 s and 2.75 s, a dip fault is injected in line b and c (Fig. 8(a)). Thus, the signals Ama and the phase φa (Fig. 8(b)) remain unchanging. The others signals deviate from the original value. For that reason, the decision D becomes equal to 3. 4.1.4. Case fault type E In fault type E (Fig. 9(a)) there are three possible cases. The first case is a fault in lines a and b. It begins at 0.1 s and ends at 0.3 s. As a consequence the signals Ama and Amb (Fig. 9(b)) deviate whereas the rest signals keep constant. The angles (φa, φb and φc) (Fig. 9(c)) still equal to 0, 2π/3, 2π/3.
Between 0.4 s and 0.6 s, a dip voltage in lines a and c is injected. As a results, only Ama and Amc shall be reduced. All the signals indicated the angles are constant. The third case showed in Fig. 9(a) between 0.7 s and 0.9 s. It consists in a dip voltage in lines b and c. only the signals Amb and Amc deviates. All these indicators correspond to a fault type E. This is affirmed by the signal D which is equal to 4. 4.2. Experimental results 4.2.1. Experimental prototype description For the experimental validation, the proposed algorithm is tested with an experimental prototype of low power. In fact, it is important to mention that the detection and identification fault does not depend on operating point or system power. The experimental setup is shown in Fig. 10. The system includes a doubly fed induction machine 0.8 kW. Furthermore, it is equipped with a servo motor (1.1 kW) which allows the training of the DFIG so that it emulates the behavior of the mechanical part. The prototype is composed of a block representing the converters AC/DC,
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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DC/DC converter, DC bus, a DC/AC converter. The DC bus voltage is equal to 280 V. The wind turbine generator is equipped with a block (Fig. 11) for the connection to the electrical grid and also to inject the dip voltage fault. The system is controlled by a personal computer. The currents and the voltage measurements are measured with LEM sensors (LA25NP for current sensors, LV25P for voltage sensors). The computer software has a graphical interface (Fig. 12) which allows selecting the wind speed and also illustrating the operating point of the system. 4.2.2. Experimental results In order to validate the new proposed architecture of the Artificial Neural Network, a dip voltage is injected in different period of time. Fig. 13(a). illustrates the electrical grid under a dip voltage. The outputs of the first step are shown in Fig. 13(b) and (c). The analysis of these figures concludes that – A sag in the signal Ama is detected between 0.25 s and 0.5 s, while the two other signals remain constant, then only Amb deflects during 0.75 s and 0.95 s. in the third range of time only the signal Amc decreases. – All the angle phases are invariant. – The decision becomes equal to2 which correspond to dip voltage type B.
5. Conclusion A conventional WT based on DFIG system connected to a grid under LVRT was considered. Four type faults are imagined to take place at Point Connection Common suddenly for any reasons. To detect and identify them, an efficient scheme has been proposed. It is based on a new ANN architecture. Two steps are constructed. Firstly, six ANNs are investigated to generate the six voltages indicators (Ama, Amb, Amc, φa, φb, φc). The second is the indicators analysis which consist to generate a decision signal called D that could in the same time detect and identify the different faults. They are classified to symmetric (fault type A) and asymmetric dip voltage (fault type B, C, E). A promising simulation and experimental results prove the efficiency of the suggested procedure. In fact, the decision signal deviates early, since it depends only on the indicators modifications. Many performances are achieved both in healthy and faulty cases. Investigated the voltage indicators make the FDI more flexible since it does not depend on current measurements which are related to the load. The use of the ANN added a greater robustness to the unknown inputs and the noise which are sources of the false alarms. The inclusion of ANN in FDI second step provided high ability and accuracy, which are essential characteristics to the decision making process. This process includes the detection and identification faults in one step. Hence, the proposed procedure can be implemented in simply and easily with little investments. This work further can be extended to the following topics could be studied:
Detection of the fault subtypes. Evaluate the method with smaller dip voltages for preventive maintenance.
Evaluate the hardware implementation of the solution with low
Analyze the fault impact on the generator side and proceed to the reconfiguration of the drive.
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cost electronic circuit.
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine Amel Adouni a,n, Dhia Chariag a, Demba Diallo b, Mouna Ben Hamed a, Lassaâd Sbita a a b
Laboratoire Systèmes Photovoltaïques, Eoliens et Géothermaux, Ecole National d’Ingénieur de Gabes, Univérsité de Gabes, Tunisia Group of Electrical Engineering – Paris (GEEPS), (CNRS, Centrale Supélec, UPMC, Univ. Paris-Sud), 91192 Gif Sur Yvette, France
art ic l e i nf o
a b s t r a c t
Article history: Received 20 January 2016 Received in revised form 1 May 2016 Accepted 16 May 2016 This paper was recommended for publication by Jeff Pieper.
As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs. & 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Keywords: Wind Turbine Low Voltage Ride Through Fault Detection and Identification Electrical grid Artificial Neural Network
1. Introduction The pessimist forecasts of fossil-fuel supplies and the pollution are considered a worldwide warning, especially with the predictable world population growth. Since the energy is the lifeblood of modern societies, the renewable sources (wind, solar, geothermal) were considered the promising sources to fulfill the world energy demand [1]. In the last decades, the conversion energy systems markets continued to emerge in several countries. The Wind Turbine (WT) was no exception. According to the new statistic in 2014, more than 20% of electricity demand is provided by the wind power [2]. The WT as a power source is connected to the grid. The last one is vulnerable to faults as LVRT. The Fault Detection and Identification (FDI) of power sources is considered an important subfield of control engineering [3]. A LVRT occurred in the grid propagates to affect the WT. The Double Fed Induction Generator (DFIG) is very sensitive to external disturbances such dip voltage. This type of fault could occur suddenly in the grid. As result, exceeding urge currents go through the rotor n
Corresponding author. E-mail addresses: [email protected] (A. Adouni), [email protected] (D. Chariag), [email protected] (D. Diallo), [email protected] (M. Ben Hamed), [email protected] (L. Sbita).
terminals. Therefore, the Machine Side Converter will be not able to support this large current and it will get damaged. A part from this, a huge electromagnetic torque pulsations will be generated and hence, an increasing in rotor speed will be detected. This reduces the gearbox life time [4]. Regarding to all these drawbacks, many countries are updated the grid code. An issue was involved in the modern electrical grid rules; WT needs to operate continually even in presence severe grid faults specially the LVRT. To response to that issue, an early and efficiency FDI procedure based on Artificial Neural Network (ANN) has been elaborated. The Artificial Neural Networks have been extensively employed in several fields of engineering application. This technique has been used successfully to model complex nonlinear dynamic systems [5,6]. The ANN does not need the use of complex analytic formulations. It requires a database and the targets to train the ANN until it reaches an acceptable error. A well-trained procedure achieves accuracy even in the presence of noise and uncertain data. ANN is also a good candidate to predict the nonlinear dynamic behaviors for control strategy [7]. However, the success of the ANN is highly dependent on the database that can be limited due to capability of the experimental prototype. Therefore when used for diagnosis, monitoring…[8] of faults in the electrical grid, signal processing and artificial intelligence techniques are used [9] for classification.
http://dx.doi.org/10.1016/j.isatra.2016.05.009 0019-0578/& 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
A. Adouni et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
v
Ωm Ωt
i as
i ts
i bs
i bt
i cs
i ct
Cm Ct
R
icr i br i ar
i f1 ’ i f2 ’ i f3 ’ ired vdc
iond
Lf
i cond
Rf
Cem * Qs *
Vdc *
Qf*
Fig. 1. Topology of the Wind Turbine.
0.9 Cpmax1
0.8
Table 1 Symmetrical dip voltage description.
0° 2° 3° 4°
0.7
Type A
0.6 0.5
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ 3
Phasor representations
ec
vc
va ea
eb
vb
p
C (λ )
Cpmax2
Voltages expressions
0.4
Cpmax3
0.3 0.2
Cpmax4
0.1 0
0
2
4
6
8
10
12
14
16
λ Fig. 2. The evolution of the power coefficient according to the tip speed ratio.
In order to diagnosis a dip voltage fault, many research works are developed and rely on this issue. In [10], the detection problem was resolved using the generalized likelihood ratio test (GLRT). It provides a competitive performance in fault starting instant detection. It is important to have the precise depth of dip voltage because it characterizes the fault severity and it is necessary in order to generate the appropriate maintenance. Therefore, in the paper [11] a detection scheme for voltage swell and low voltage based on using a simple relay and low pass filter is applied to the supply voltage signal. The used technique generates in-phase fixed magnitude reference signal with very low total harmonic distortion. This signal
will compared to the supply signal and any variation in magnitude of the supply could be detected. The dip voltage occurred in the power distribution grid are studied in [12]. In this work, the principal objectives were classifying the fault type and locate the fault position in a given power delivery network. In order to achieve that purpose, a sensitive phase locked loop used to trigger the fault inception. After that, damped sinusoid of arbitrary temporal support is used to decompose the captured signals. In [13], the space vector is used for detection and identification of the voltage dips and swells. This method is based on space vector trajectory in complex plane analysis and in the zero-sequence voltage. In another work [14], using polarization ellipse in 3-D coordinates, unique signatures and parameters of three-phase voltage signals characterized the three phase voltage could be extracted and used to develop a method detect and classify the voltage dips and swells. There are five ellipse parameters could be used azimuthal angle, elevation, tilt, semi-minor axis, and semi-major axis. To distinguish between the different voltage sag, three characteristics (amplitude, harmonics and fault time) could be used. The Hilbert-Huang transform (HHT) is a time frequency representation. It is considered as a powerful technique to extract the voltage dip features. It could detect and classify correctly and concisely the dip voltage caused by line short circuit faults and motor induction start up [15]. In [16], Based on matrix pencil method, a novel technique was elaborated to classify
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Table 2 Asymmetrical dip voltage description. Type B
Sub-Type Phase a
Phase b
Phase c
C
Phase ab
Phase ac
Voltages expressions pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ V 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ V 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ V 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ V 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ V 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ V 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwt αÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt þ α 3 pffiffiffi 2π vc ðtÞ ¼ V 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtþ αÞ pffiffiffi 2π vb ðtÞ ¼ V 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ α 3
Phasor representations
ec
E
Phase ab
Phase ac
Phase bc
pffiffiffi va ðtÞ ¼ V 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt α 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ þ α 3
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ V 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ ð1 dÞV 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ V 2 sin wt 3 pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ 3
pffiffiffi va ðtÞ ¼ V 2 sin ðwtÞ pffiffiffi 2π vb ðtÞ ¼ ð1 dÞV 2 sin wt 3
va
eb vc
ec
va
ea eb ec
vb vc
va ea
eb
vb
ec
vc ea
eb v b ec vc
va va ea
eb Phase bc
vc
vb
ec va
vc vb
ea
eb
ec
eb ec
vc va
ea
vb vc va
ea
eb v b
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Table 2 (continued ) Type
Sub-Type
Voltages expressions
Phasor representations
pffiffiffi 2π vc ðtÞ ¼ ð1 dÞV 2 sin wt þ 3
ec vc
va
ea
vb eb
Table 3 Electrical grid indicators. Amplitude
Angle
Healthy function
Ama ¼V, Amb ¼ V, Amc ¼ V
Fault type A
Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ (1 d)V
Fault type B
Fault type C
Fault type E
Phase a
Ama ¼(1 d)V, Amb ¼ V, Amc ¼V
Phase b
Ama ¼V, Amb ¼ (1 d)V, Amc ¼V
Phase c
Ama ¼V, Amb ¼ V, Amc ¼ (1 d)V
Phase ab
Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ V
Phase ac
Ama ¼(1 d)V, Amb ¼ V, Amc ¼(1 d)V
Phase bc
Ama ¼V, Amb ¼ (1 d)V, Amc ¼(1 d)V
Phase ab
Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ V
Phase ac
Ama ¼(1 d)V, Amb ¼ V, Amc ¼(1-d)E
Phase bc
Ama ¼V, Amb ¼ (1 d)V, Amc ¼(1 d)V
the three phase voltage dips. It was improved with an ellipse fitting algorithm. Another work focus in voltage disturbances detection and classification was developed in [17]. The intelligent systems and signal processing techniques are used for a voltage disturbances detection and classification. The decision is made using four modules: the first dedicated to operation state description, the second module used multi-resolution analysis, the discrete wavelet transform and entropy norm concepts to extract the features. The signal signature is processed via standardization and codification in the third module. Using a Fuzzy-ARTMAP neural network, the fourth module classifies the type of disorder. The improper protection of WT during a voltage dip produces the damage of the system. Classifying multistage and real voltage dips when the measurement data are recorded in transient stage is present in [18]. For this purpose the voltage-space vector is applied. The proposed technique is tested in wind farms located in the Spanish region of Castilla-La Mancha. In [19], the authors used a data-driven. Three features extracted from the current vector in the Concordia stationary reference frame. Then, they are evaluated using Principal Component Analysis. In other work [20], a non-parametric method was suggested to diagnosis a dip voltage in WT. It based on the current trajectory analysis. In the cited papers, the authors focus in a special type of dip voltage [20]. They are limited in one type (dip in one phase of the electrical grid, or symmetrical dip voltage…). The GLRT detector is competitive, but the main problem is the decision threshold [6]. The simple relay and low-pass filter is a hardware solution that does not provide an efficient decision since it also depends on the threshold [7]. The Principal Component Analysis (PCA) is a datadriven technique suitable for systems with high dimensions and if an accurate analytical model is unavailable. Moreover, the fault detection time duration when using PCA may be long and the method is not robust against the disturbances [15]. Even if the Kernel PCA (KPCA) handles the noise, occlusion and missing data, this method does not allow fast fault detection. In addition, in [16]
2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ α; φb ¼ 2π 3 þ α; φc ¼ 3 2π φa ¼ α; φb ¼ 2π 3 ; φc ¼ 3 α 2π φa ¼ 0; φb ¼ 2π 3 α; φc ¼ 3 þ α 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3 2π φa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
ANN1
ANN2 =0 healthy case
va (t) ANN3 ANN7
D
vb (t)
=1 Fault type A =2 Fault type B =3 Fault type C
ANN4
=4 Fault type E vc (t) ANN5
ANN6
Voltage indicators generation step
Voltage indicators analysis or Detection and identification step
Fig. 3. Dip voltage fault diagnosis.
the method is load sensitive as it is based on the measured the currents. Comparing to the aforementioned works, the proposed FDI exhibits different advantages such as:
it does not require the setting of a threshold, it can be implemented in open or closed loop as it only requires the voltages measurements,
it is not load dependent.
Since, there are many dip voltage types could affect the electrical grid, the study of all these faults are required in order to take the accurate monitoring tools. Hence, it is important to identify all of them. In this paper, the targeted goals are firstly to detect
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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rapidly the mode function (healthy or faulty) and secondly to improve the identification step in order to minimize the false alarm caused by the noise and the unknown inputs. As a solution, a new ANN architecture is proposed. The Artificial Neural Network is a supervised method which efficiently depends on the training step that should be done with a rich database, which contains as much information as possible including not only the healthy and faulty cases but also the noise and the unknown inputs. Therefore, during the learning procedure, the outputs are built to be robust to false alarms (due to noise and unknown inputs) and sensitive to fault occurrence. The paper is structured in 5 sections. Section 2 is a short description of WT model. Section 3, deals with the proposed FDI
5
scheme and Section 4 shows the simulation and experimental results and discusses the effectiveness of this procedure. The final section concludes this current research work.
2. Wind Turbine model The topology of the WT is illustrated in Fig. 1. Mainly, there are four elements: the Turbine, the Gearbox, the DFIG and the back-toback converter. 2.1. Turbine model The blades are set in rotation due to the wind speed. The kinetic power wind is equal to [21] P c ¼ 0:5ρSV 3w
va(t) Si vb(t)
ð1Þ
where V w is the wind speed, ρ is the air density and S is the surface swept by the blades. A percentage of the kinetic power will be captured by the turbine and converted to the aerodynamic power. This percentage represents the power coefficient so [22] P aer ¼ C p ðλ; β ÞP c ¼ 0:5ρπ R2 V 3w C p ðλ; βÞ
ð2Þ
Where, R is the blade length and C p is a nonlinear function and depends of the Tip Speed Ratio (TSR) λ and pitch angle β . It defined as [21]
vc(t)
C p ðλ; β Þ ¼ ð0:5 0:167ð β 2ÞÞ sin
Fig. 4. Artificial Neural Network for detection.
π ðλ þ 0:1Þ 0:00184ðλ 3Þð β 2Þ 18:5 0:3ð β 2Þ ð3Þ
ϕa
=0 healthy case
The TSR is defined as the ratio between the linear blade tip speed Ωt and the wind speed. It is expressed as
Am a
=1 Fault type A
λ¼
ϕb
=2 Fault type B
ANN7
=3 Fault type C
Am b
ϕc Am c
Décision D
RΩt Vw
ð4Þ
The aerodynamic torque is expressed as [22] C aer ¼
P aer
Ωt
¼
1 πρR3 V 2w C p ðλ; βÞ 2λ
ð5Þ
=4 Fault type E 2.2. Double Fed Induction Generator model The model of the DFIG is described in the synchronous reference frame. The stator and rotor voltages along the d-axis and q-axis are
Fig. 5. Voltage indicators analysis step.
Table 4 Voltages indicators and corresponding decision D. Indicators
Decision: D
Normal function
Ama ¼V, Amb ¼ V, Amc ¼ Vφa ¼ 0; φb ¼
Fault type A
2π Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ (1 d)Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
1
Phase a
2π Ama ¼(1 d)V, Amb ¼ V, Amc ¼Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
2
Phase b
2π Ama ¼V, Amb ¼ (1 d)V, Amc ¼Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
2
Phase c
2π Ama ¼V, Amb ¼ V, Amc ¼ (1 d)Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
2
Phase ab
2π Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ Vφa ¼ α; φb ¼ 2π 3 þ α; φc ¼ 3
3
Phase ac
2π Ama ¼(1 d)V, Amb ¼ V, Amc ¼(1 d)Vφa ¼ α; φb ¼ 2π 3 ; φc ¼ 3 α
3
Fault type B
Fault type C
Fault type E
2π 3 ; φc
¼
2π 3
0
Phase bc
2π Ama ¼V, Amb ¼ (1 d)V, Amc ¼(1 d)Vφa ¼ 0; φb ¼ 2π 3 α; φc ¼ 3 þ α
3
Phase ab
2π Ama ¼(1 d)V, Amb ¼ (1 d)V, Amc ¼ Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
4
Phase ac
2π Ama ¼(1 d)V, Amb ¼ V, Amc ¼(1 d)Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
4
Phase bc
2π Ama ¼V, Amb ¼ (1 d)V, Amc ¼(1 d)Vφa ¼ 0; φb ¼ 2π 3 ; φc ¼ 3
4
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 6. (a) Electrical grid in case of dip voltage type B. (b) Amplitudes outputs detection step. (c) Angles outputs detection step. (d) Decision signal D.
expressed as following [23]:
to the generator speed. The gearbox is characterized by a ratio G:
8 dφ vsd ¼ Rs U isd þ dtsd ωs U φsq > > > > > > < vsq ¼ Rs U isq þ dφsq þ ωs U φsd dt
Ωt ¼
dφ > vrd ¼ Rr Uird þ dtrd ωr U φrq > > > > > d : v ¼ R U i þ φrq þ ω U φ rq r rq r rd dt
The stator and rotor fluxes are expressed by 8 φ ¼ Ls isd þMird > > > sd > < φsq ¼ Ls isq þ Mirq
φrd ¼ Lr ird þ Misd > > > > : φrq ¼ Lr irq þ Misq
ð6Þ
Ωm G
C aer ¼ GC m
ð8Þ ð9Þ
where Ωm is DFIG shaft speed and C m is the mechanical torque. 2.4. Wind Turbine control
ð7Þ
where Rs ; Rr are the resistance of stator's and rotor's winding. Ls ; Lr are stator's and rotor's self inductance, M is mutual inductance, ωr and ωs are respectively the rotor's angular speed and grid angular frequency, isd , isq , ird , irq are the stator's and rotor's currents. 2.3. Gearbox model The turbine and the DFIG are connected via the Gearbox. It multiples the turbine shaft low speed in order to accommodates it
As shown in Fig. 1. three main controls are necessary for the proper functioning of the system. The first is the Maximum Power Point Tracking called MPPT. The second is the Machine Side Converter (MSC) control and the third is the Grid Side Converter (GSC) control. Fig. 2 represents the relation between the power coefficient and TSR according to pitch angle. It is observed that the maximum of power coefficient is obtained in the optimal TSR. In order to maximize the aerodynamic power, the WT should operate in the optimal operating point. The DFIG mechanical speed Ωm depends on pitch angle and electromagnetic torque C em . When the pitch angle is fixed, the C em should attends the optimal value in order to achieve the optimal power. Therefore, the generator shaft should rotate in the mechanical
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 7. (a) Electrical grid in case of dip voltage type B, (b) amplitudes outputs detection step, (c) angles outputs detection step, and (d) decision signal D.
principal equations
speed which verifies the following relation: C em ¼ K Ωm 2
ð10Þ
if d ¼
The MSC control is accomplished using the stator-flux oriented control (SFOC). It consists to align the stator flux vector with the daxis position. Therefore, the q-axis stator flux is set to 0 and the electromagnetic torque is expressed as following:
if q ¼
where K is a constant equal to K ¼
C em ¼ pφsd isq
C p_
max
ρπ R
M irq Ls
vsq φsd vsq M Qs ¼ i Ls rd Ls
ð14Þ
5
2G3 λopt 3
ð11Þ
Using the stator and rotor flux expressions, the active and reactive stator power are given P s ¼ vsq
Q f vsq
ð12Þ
ð13Þ
The C em becomes C em ¼ pM Ls φsd irq . Since the C em and P s depend on q-axis rotor current. The MSC should deliver the desired reactive power Q s and the C em . The DFIG is connected directly to the grid via the stator and through the GSC. The control of this converter has two roles: maintain the DC bus voltage constant and keep a unity power factor at the point of connection to electrical grid. The control of the GSC is based on the d–q axis filter control using these
P f vsq
ð15Þ
where P f is the filter active power and Q f is the filter reactive power (set to 0). More details about the adopted strategy are given in [21].
3. Fault Detection and Identification procedure 3.1. Faults description To describe the dip voltage, the ABC classification is used. It is the most frequently classification cited in literature. That because it is suitable for many applications. The temporal voltage expressions of the three phases under symmetrical and asymmetrical faulty function are given respectively in Tables 1 and 2. The magnitude of the pre-fault phase voltage is denoted by e and in the faulty case it is indicated by v [24]. 3.2. Fault detection and isolation procedure Table 2 shows the expressions of the grid voltages for the four fault types. Comparing them to those in healthy cases, it has been noticed that the dip voltage affects the amplitude and/or the
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 8. (a) Electrical grid in case of dip voltage type C, (b) amplitudes outputs detection step, (c) angles outputs detection step, and (d) decision signal D.
phase, which will be the features for fault detection. Since the electrical grid has three phases we have therefore six features. One ANN generates each feature so we have six ANNs. These six indicators are useful to describe the each phase state: – – – – – –
Ama amplitude phase a, Amb amplitude phase b, Amc amplitude phase c, φa angle phase a, φb angle phase b, φc angle phase c.
Depending on the fault type the amplitude and the angle phase are deflected. Referring to Table 2, all the indicators are extracted and summarized in Table 3. The interpretation of the table proves that the analysis of these two indicators is sufficient to detect rapidly and identify accurately the fault. For that purpose the ANN was used. This technique guaranties the immunities to the unknown inputs and noise and as consequence robustness to the false alarms generated by these signals. Where – d is the dip voltage magnitude, – α the angle deviation. The proposed algorithm (Fig. 3) is divided into two steps: the first is dedicated to the voltage indicators generation and the second to the fault indicators analysis it allows the detection and identification in the same time. The first step is composed of six ANNs: ANN1, ANN2, ANN3, ANN4, ANN5, ANN6. Fig. 4 gives the internal detail for each ANN. Fig. 5 represents the second step.
3.2.1. Voltage indicators generation step The aim of this step is to generate the voltage indicators. So, six ANNs are dedicated to the six indicators. The first neural network has three inputs va(t), vb(t), vc(t) and one output Ama which is the magnitude of the voltage in phase a. The five others are devoted respectively to Amb, Amc, φa , φa , φa . Each of these ANN is composed of three layers: the first has 7 neurons with tang-sigmoid hidden neuron function and the second has two neurons with tangsigmoid function activation. Each ANN is represented by Fig. 4. The learning procedure based on Table 3 is based on the Levenberg–Marquardt algorithm. This algorithm is used to solve nonlinear least squares problems when fitting a parameterized function by searching the suitable parameters to reach the optimal minimum error between the data points and the function. For example, in the first ANN1, the inputs are va(t), vb(t), vc(t), the output is y¼Ama. So, y¼Ama ¼ f(va(t), vb(t), vc(t), pi). Where pi is the neuron coefficient. The desired output is Ama_d. Hence, the squared error is (y Ama_d)2. There are other algorithms such as the gradient descent algorithm and the Gauss–Newton algorithm. The Levenberg–Marquardt interpolates the Gauss–Newton algorithm and the gradient algorithm. In the gradient descent algorithm, the parameters are updated by reducing the sum of the squared errors in the steepest-descent direction. In the case of Gauss–Newton algorithm, this sum is minimized by assuming that the least squares function is locally quadratic, and finding the minimum of the quadratic. Despite the Levenberg–Marquardt algorithm is slower than the Gauss–Newton algorithm, it is more robust than the Gauss–Newton and can find the desired settings even if the initial ones are very far from the final minimum. Where Si could be Ama, Amb, Amc, φa , φb , φc .
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 9. (a) Electrical grid in case of dip voltage type E, (b) amplitudes outputs detection step, (c) angles outputs detection step, and (d) decision signal D.
Grid connecting and dip voltage block
Back-to-back converter
Servo-motor
DFIG
Grid side connexion
Wind turbine side connexion Parameters dip voltage
Voltage and current sensors
Three phases dip voltage injection
Fig. 11. Grid connection block and dip voltage faults injection.
oscilloscope
Fig. 10. Experimental prototype.
3.2.2. Voltage indicators analysis step Voltage indicators analysis step is the second step. It has been showed in Fig. 5. The goal of this step is to identify the fault type. It looks for the mode function (healthy or faulty) and in the case of dysfunction it looks for which type of fault was occurred. This step
is also based on ANN. It is very important because, it allows making the proper decision and calculating the accurate order to keep the system in safety operation. According to Table 3. that is deduced from Table 2. each fault is described by the vector [Ama, Amb, Amc, φa , φb , φc ]. As it observe that each fault is different to the others by at least one indicator. If a signal that depends on all the features and deviates from 0 (healthy case) to the corresponding value according to the features changes is generated, the accurate
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 12. Graphical interface.
decision is guaranteed. For that purpose, the ANN dedicated to the second step has 1 output which is the decision signal D. The ANN is composed by 2 layers: 7 neurons in hidden layer with tangsigmoid function and 1 neuron in output layer with linear function. The signal inputs are φa , Ama, φb , Amb, φc and Amc generated by the first step. The network is trained in such a way that a unique value of the decision signal corresponds to the adequate combinations of this vector. So there is a unique solution for each fault. If the fault subtypes were to be detected, other values of the decision signal should be included for fault discrimination. Table 4 proves that the decision is guaranteed.
4.1.1. Case fault type A A dip voltage is injected between 0.4 s and 0.85 s (Fig. 6 (a)). Fig. 6(b) and (c) shows voltage indicators behaviors. The three amplitudes signals decrease during the same time interval. For angles signals (φa, φb and φc) are equal respectively to 0, 2π/3, 2π/3. The decision (Fig. 6(d)) is equal to 1 which corresponds to a fault type A.
4.1. Simulation results
4.1.2. Case fault type B The fault type B is a dip voltage in one phase (a or b or c) and the two others still in healthy function. The first case is shown in Fig. 7(a) between 0.1 s and 0.3 s. It is a 30% dip voltage affecting the line a. The voltage amplitude signal Ama is the only signal deviate between 0.1 s and 0.3 s whereas the all other signals remain unchanged (Fig. 7(b) and (c)). According to Table 3 all the indicators corresponds to fault type B. The signal D (Fig. 7(d)) proves that it is the fault type B. In fact, it equal to 2 between 0.1 s and 0.3 s. Between 0.4 s and 0.6 s a dip voltage in phase b is injected (Fig. 7(a)). Therefore, the signals Ama and Amc (Fig. 7(b)) are unvarying, but the one Amb generated by the ANN2 deviates from the original value between 0.40 and 0.6 s. This means that the fault started and disappeared 0.40–0.6 s. The other signals (Fig. 7(c)) remain constant. The decision D changes from 0 to 2 confirming that this is the fault type B. The third case is a dip voltage in phase c as shown in Fig. 7(a). It occurred between 0.7 s and 0.9 s. As a consequence, only the signal Amc deviates in same range of time. This type of fault corresponds to type B, which corresponds to a Decision D equal to 2.
In order to evaluate the efficiency of the method, the algorithm is implemented in Matlab Simulinks. A dip voltage in the electrical grid is injected at different times.
4.1.3. Case fault type C In this case, the dip voltage affects the line a and b (Fig. 8(a)) during the period [0.30 s, 0.75 s]. As a result, the signals Ama, Amb,
3.2.3. Contribution and novelties The proposed scheme based on the use of a new Artificial Neural Network architecture and the voltage grid measurements can detect and identify the four fault types (A, B, C, E). All the modes are included, from healthy to faulty cases. Thanks to an efficient learning step, the method is robust against false alarms due the unknown inputs and environmental noise. One of the main advantages of the method is that it does not require the current's measurements. Therefore it's independent from the load variations. The detection time duration is also short as the signal decision spots quickly the modifications of the voltage indicators.
4. Results and discussion
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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Fig. 13. (a) Electrical grid in case of dip voltage type B, (b) amplitudes outputs detection step, (c) angles outputs detection step, and (d) decision signal D.
φa and φb (Fig. 8(b) and (c)) deviate, the signals Amc and φa keep unchangeable during the same period. The signal decision D changes from 0 to 3 confirming that this is the fault type C. The second case is a dip voltage occurred between 1.25 s and 1.7 s. It affects the line a and c (Fig. 8(a)). In that period, only the signal Amb and the phase φb (Fig. 8(b)) remains constant. The others signals (Ama, Amc, φa and φc) generated by the ANN1 and ANN3 deviate from the original value during the same range of time. The decision D becomes equal to 3. During the period 2.35 s and 2.75 s, a dip fault is injected in line b and c (Fig. 8(a)). Thus, the signals Ama and the phase φa (Fig. 8(b)) remain unchanging. The others signals deviate from the original value. For that reason, the decision D becomes equal to 3. 4.1.4. Case fault type E In fault type E (Fig. 9(a)) there are three possible cases. The first case is a fault in lines a and b. It begins at 0.1 s and ends at 0.3 s. As a consequence the signals Ama and Amb (Fig. 9(b)) deviate whereas the rest signals keep constant. The angles (φa, φb and φc) (Fig. 9(c)) still equal to 0, 2π/3, 2π/3.
Between 0.4 s and 0.6 s, a dip voltage in lines a and c is injected. As a results, only Ama and Amc shall be reduced. All the signals indicated the angles are constant. The third case showed in Fig. 9(a) between 0.7 s and 0.9 s. It consists in a dip voltage in lines b and c. only the signals Amb and Amc deviates. All these indicators correspond to a fault type E. This is affirmed by the signal D which is equal to 4. 4.2. Experimental results 4.2.1. Experimental prototype description For the experimental validation, the proposed algorithm is tested with an experimental prototype of low power. In fact, it is important to mention that the detection and identification fault does not depend on operating point or system power. The experimental setup is shown in Fig. 10. The system includes a doubly fed induction machine 0.8 kW. Furthermore, it is equipped with a servo motor (1.1 kW) which allows the training of the DFIG so that it emulates the behavior of the mechanical part. The prototype is composed of a block representing the converters AC/DC,
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i
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DC/DC converter, DC bus, a DC/AC converter. The DC bus voltage is equal to 280 V. The wind turbine generator is equipped with a block (Fig. 11) for the connection to the electrical grid and also to inject the dip voltage fault. The system is controlled by a personal computer. The currents and the voltage measurements are measured with LEM sensors (LA25NP for current sensors, LV25P for voltage sensors). The computer software has a graphical interface (Fig. 12) which allows selecting the wind speed and also illustrating the operating point of the system. 4.2.2. Experimental results In order to validate the new proposed architecture of the Artificial Neural Network, a dip voltage is injected in different period of time. Fig. 13(a). illustrates the electrical grid under a dip voltage. The outputs of the first step are shown in Fig. 13(b) and (c). The analysis of these figures concludes that – A sag in the signal Ama is detected between 0.25 s and 0.5 s, while the two other signals remain constant, then only Amb deflects during 0.75 s and 0.95 s. in the third range of time only the signal Amc decreases. – All the angle phases are invariant. – The decision becomes equal to2 which correspond to dip voltage type B.
5. Conclusion A conventional WT based on DFIG system connected to a grid under LVRT was considered. Four type faults are imagined to take place at Point Connection Common suddenly for any reasons. To detect and identify them, an efficient scheme has been proposed. It is based on a new ANN architecture. Two steps are constructed. Firstly, six ANNs are investigated to generate the six voltages indicators (Ama, Amb, Amc, φa, φb, φc). The second is the indicators analysis which consist to generate a decision signal called D that could in the same time detect and identify the different faults. They are classified to symmetric (fault type A) and asymmetric dip voltage (fault type B, C, E). A promising simulation and experimental results prove the efficiency of the suggested procedure. In fact, the decision signal deviates early, since it depends only on the indicators modifications. Many performances are achieved both in healthy and faulty cases. Investigated the voltage indicators make the FDI more flexible since it does not depend on current measurements which are related to the load. The use of the ANN added a greater robustness to the unknown inputs and the noise which are sources of the false alarms. The inclusion of ANN in FDI second step provided high ability and accuracy, which are essential characteristics to the decision making process. This process includes the detection and identification faults in one step. Hence, the proposed procedure can be implemented in simply and easily with little investments. This work further can be extended to the following topics could be studied:
Detection of the fault subtypes. Evaluate the method with smaller dip voltages for preventive maintenance.
Evaluate the hardware implementation of the solution with low
Analyze the fault impact on the generator side and proceed to the reconfiguration of the drive.
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cost electronic circuit.
Please cite this article as: Adouni A, et al. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions (2016), http://dx.doi.org/10.1016/j.isatra.2016.05.009i