Rotor Faults Detection of Induction Generator ...

MARCH 25-28

Rotor Faults Detection of Induction Generator Integrated in Wind Power System Youcef Soufi Electrical Department, Faculty of Science Engineering University of Tebessa, Tebessa, Algeria E.-mail: [email protected] Tahar Bahi Electrical Department, Faculty of Science Engineering University of Annaba, Annaba, Algeria E.-mail: [email protected] Mohamed Faouzi Harkat Electronics Department, Faculty of Science Engineering University of Annaba, Annaba, Algeria E.-mail: [email protected] Issam Atoui Electrical Department, Faculty of Science Engineering University of Annaba, Annaba, Algeria E.-mail: [email protected] Copyright © 2010 MC2D & MITI

Abstract: In this paper, we focus on the detection and diagnosis of rotor faults: broken bars or broken rotor ring, a cage induction generator self excited, installed in a wind channel conversion. It is to extract and use relevant information to detect and diagnose system failures. The adopted methodology focuses on the use of tools of signal processing to develop methods of detection and diagnosis. Knowing that the methods based on using the fast Fourier transform, are not applicable under conditions of variable frequency operation over time. To overcome this problem, we used the tools time - frequency that can perform surveillance for transient and permanent regime. In the first part of this work, we develop analytical models of the turbine and generator, taking into account the multi model for the rotor windings. Then we analyze the behavior of the generator in the healthy case and that of the faulty one that operates in the presence of faults in broken rotor bars or broken rings. In the second part we apply the time-frequency analysis on the stator current generator for the detection of possible defects. By this work, we aim at demonstrating that spectral analysis by fast Fourier transform of the stator current of induction generator wind a string can detect and diagnose a fault rupture bars or breaks rings in the rotor asynchronous generator only when operating in permanent regime. However, the tool time – frequency allows the surveillance during the transitional and permanent regime. . Keywords: Diagnosis, generator, turbine channel, time frequency analysis.

1. Introduction In the field of processing energy, control and diagnosis make up procedures that permit to equip the devices of functionality and maintainability despite changes of the operating context [1]. In fact, electricity has become very strong and a dominant manner, whether in transport, energy storage, health or the environment. It involves the use of various actuators in the broadest sense, providing the energy exchange between a source and a load. This transfer requires a specific conversion topology that is to drive to impose the desired function and meet the specified specifications. Thus, the purpose is achieved by using suitable control laws and is maintained by setting up diagnostic procedures to extend the availability of the function. Indeed, the electrical vector is very present today, whether in transport, energy storage, health or the environment. It involves the use of various actuators in the broadest sense through the exchange of energy between a source and load. This transfer requires a specific conversion topology, it is to drive to impose the desired function and meet the specifications specified. The purpose is achieved by using suitable control laws and is maintained by setting up diagnostic procedures to extend the availability of the [2]. The work done on the study of faults in cage induction generator focuses on the use of tools of signal processing to develop methods of detection and diagnosis. We are particularly interested in monitoring and diagnosis of faults in the asynchronous drive with variable speed. This can be done for certain types of defects by analyzing the stator current of the machine. Most existing methods rely on monitoring frequency components via an estimate of the stator current spectrum [3]. These methods, based on the use of fast Fourier transform, are not applicable under conditions of variable frequency operation over time. To overcome this problem, we employ the time - frequency that can perform surveillance for transitional arrangements (Wigner-Ville) and instantaneous frequency [4]. These methods provide information particularly relevant to the diagnosis since they can even distinguish different types of defects.

Modeling of wind system

2.

The system studied consists of Rp length blades to capture energy from wind, a whole tree - gearbox, a three-phase asynchronous generator, the capacity for self-excited to magnetize the generator and a load Rl. The mechanical power recovered by the turbine can be written as follows [5]:

1 C p  R p2 V 2

Pturbine 

3

(1)

With: ρ: density of air (ρ = 1.3Kg / m); V: wind speed; Cp: power coefficient. The power coefficient is approximated by the following formula [6], [7]:     3    0.0184  3 Cp ,    0.4233sin  15  0.3 

(2) Where λ: specific speed; β: angle of the blades. Then the aerodynamic torque can be written: 2

Taero

P 1 C p R pV  turbine   2 

3

(3)

The multiplier adjusts the speed of the blades slow to fast speed of the machine by the gain G. The mechanical torque Tm and the speed of the machine Ωr are expressed by the following respective expressions: Tm  Taero / G (4)



r

 G

(5)

The dynamic equation is given buy:

J

d r J d r   T m  T em  f  r dt p dt

Where J : moment inertia; Tem : electromagnétic torque ; p : number of pole pairs ; Ωr : rotor angulaire speed; f : viscous friction torque.

(6)

The bootstrap support is the same as empty, except that the equations of excitement take other forms. The charges are connected in a star at the terminals of the generator, the system of equations in the benchmark Clarke reads as follows [8]: The circuits thus formed are magnetically coupled. After transformation and rotation using the Clarke transformation is represented by the following matrix form [9]:

L  d I   V   R I 

nonlinearity. The couple Tt was assumed proportional to the rotational speed of the turbine. The control strategy for speed control is implemented by the following algorithm:    0  2 Si  ref     ref   t   tn     

Si

 t   tn

(9)

(7)

dt

4. Simulation and Interpretation

As Lsc 0 .. .. Msr cosja .. .. 0    0 L .. ..  M sin ja .. .. 0  sc sr  L  . . Lt Mrr Lb Mrr Mrr Mrr Lb  e   Nr   . . Mrr Lb Lt Mrr Lb Mrr Mrr .    3 3  L   Msr coska  Msr sinka .. .. .. .. .. .   2  2  . . .. .. .. .. .. .   L . . Mrr Lb Mrr Mrr Mrr Lb Lt  e  N r  L L   0 0  e .. .. ..  e Le   Nr Nr  

 Rs L  sc 0   . R  .  .  0.   0 

0   t   tn

Lsc Rs

.. .. .. .. 2R 0 Lb0 e 2Rb(Nr1) Rb0 Nr

Msrsinja Msrcosja

.. ..

.. ..

0

0

Rb(Nr1)

2R Rb(k1) Lb(k2)  e 2Rb(k1) Rbk Nr .. .. .. .. .. ..

.

0

. .

.. ..

0

Rb(Nr1)

0

0

Rb(Nr2)

0

R e Nr

..

..

..

0 .. .. 2R Lb(Nr2)  e 2Rb(Nr1) Nr R e Nr

For this application, we consider this data : Wind speed (V) 13 m / s; Excitation(C) 80µF; Load resistance (Rl) 300 _ and Inductance load (Ll) 500mH. A Generator asynchronous healthy

0 0  R  e Nr  .  .  . Re  Nr  Re  

With

Lt  Lrp 

2Le  2Lb Nr

I   I ds I qs I r 0 ....I rj ...I r ( N 1) I e T r

V    Vds Vqs 0....0...0T The electromagnetic torque is obtained by derivation of the co-energy. Nr 1  Nr 1  3 Cem  pMsr Ids Irk sinka Iqs Irk coska 2 k 0  k0 

(8) 3. Control principle of the generator The structure of command for maximum control of the speed of rotation or by picking magnetization direction (varying β) blades [10] [11].To control the speed of rotation speed of the correction is proportional with a

Figure.3: Simulations for healthy operation

B Induction generator with default of broken rotor bar. The simulation results, shown in Fig. 4, show the behavior of the same chain of converting wind energy with a break of two rotor bars at t = 0s. The same operating conditions, a bootload followed by an application of the load from t = 2s.

C Induction generator with fault ruptures rings. With the same chain conversion wind and the same operating conditions with two broken rotor rings at t = 3s. The results are shown in Figure 5 This shows the same characteristics as before.

vitesse [rd/sec] 350

300

250

200

150

100

50

0

0

0.5

1

1.5

2

2.5

3

Temps[s]

Figure. 5: Simulations disruptions rings Figure. 4: Broken bars Simulations

3.5

5.

Conclusion

Following this work, we demonstrated that spectral analysis of stator current of induction generator wind a string can detect and diagnose a fault rupture bars or breaks rings in the generator rotor asynchronous steady. Tool time- frequency allows for monitoring during the transitional regime (instantaneous frequency). The frequencies that appear in the presence of such defects are symmetrical with respect to the fundamental frequencies are those based on the slip generator and depend on the variation of magnetizing current. Therefore, a reliable diagnostic method adopted, the system must be analyzed regulation of magnetizing current very powerful.

References

[7] E.S. Abdin and W. Xu ‘Control Design and Dynamic Performance Analysis of a Wind Turbine Induction Generator Unit’, IEEE Trans. Energy Conversion, Vol. 15, N°1, pp. 91–96, 2000. [8] E. Merabet, R. Abdessemed, H. Amimeur, F. Hamoudi, L.Abdelhamid, "Influence de la Charge sur une Génératrice Asynchrone Double Etoile (GASDE)", International Conference on Renewable Energy ICRE'07, University of Bejaia, , pp. 63-68 (CDROM), 25-27 Nov. 2007. [9] A.Menacer, M.Said,N.Said ”Stator Current Analysis Of Incipient Fault Into Asynchronous Motor Rotor Bars Using Fourier Fast Transform” Journal of Electrical Engineering, Vol. 55, n0. 5-6, 2004, 122-130

[1] H. Benattia, B. Dagues, I. Slama Belkhodj, "A contribution to the detection and the localisation of the failure of a mechanical load driven by an induction machine", ICEM02, Belgique,2002.

[10] L.Leclercq "Apport du stockage inertiel associé à des éoliennes dans un réseau électrique en vue d’assurer des services systèmes", Thèse de Doctorat, Ecole Centrale de Lille, Décembre 2004.

[2] S. Nandi, H. Toliyat, and X. Li, “Condition monitoring and fault diagnosis of electrical motors: A review,” IEEE Transactions on Energy Conversion, vol. 20, no. 4, pp. 719– 729, Dec. 2005.

[11] J.Slootweg,S.De Haan,H.polider et W.Kling "General model for representing variable speed wind turbine in power system dynamics simulation ",IEEE Transactions on Power Systems vol.18 , no.1 ,2003.

[3] W.T. Thomson and R. J. Gilmore, "Motor current signature analysis to detect faults in induction motor drives—fundamentals, data interpretation, and industrial case histories" Proceedings of the thirty-second turbo machinery symposium, pp.145-156, 2003. [4] M. Blodt, M. Chabert, J. Faucher and B. Dagues, "Mechanical load fault detection in induction motors by stator current timefrequency analysis", IEMDC05, San Antonio, Texas, 15-18 MAI 2005. [5] S. L.M. Hansen, P.H. Madsen, F. Blaabjerg, H.C. Christensen, U. Lindhard, and K. Eskildsen, "Generators and power electronics technology for wind turbines", Record of IEEE-IECON-2001, 2000–2005. [6] L.Krichen, B. François et A.Ouali, ”Modélisation, commande et interaction de deux éoliennes à vitesse variable”Revue des Energies Renouvelables Vol. 10 N°2 , 225 – 230, 2007.