Optimal MRAS Speed Estimation for Induction Generator in Wind Turbine Application

Optimal MRAS Speed Etimation for Induction Generator in Wind Turbine Application Bori Dumnic #1, Dragan Matic #2, Vladimir Katic #3, Veran Vaic #4, Marko Delimar *5 # Univerity of Novi Sad, Faculty of Technical Science Novi Sad, Serbia 1 dumnic@un.ac.r 2 dmatic@un.ac.r 3 katav@un.ac.r 4 veranv@un.ac.r * Univerity of Zagreb, Faculty of Electrical Engineering and Computing Zagreb, Croatia 5 marko.delimar@fer.hr Abtract Thi paper propoe improved enorle vector control of quiral cage induction generator for variable peed wind energy converion ytem. The rotational peed of the induction generator i etimated with the Model Reference Adaptive Sytem - MRAS oberver. The etimated rotational peed i ued a the feedback of the control loop in the converter control ytem. Proportional integral controller in the MRAS oberver i optimized via Genetic Algorithm, Partical Swarm Optimization and Simulated Annealing. Comparative analie of the optimal peed etimation of induction generator i alo preented. The performance of the enorle controled variable peed wind turbine drive i evaluated through imulation in Matlab/Simulink. Experimental reult are gained via laboratory model baed on dspace DS14 digital control card. I. INTRODUCTION In the lat fifteen year, wind turbine power generation ha noticeably increaed worldwide. One of the implet method of running a wind energy converion ytem i to ue an induction generator connected directly to the power grid. Thi method of operation force the machine to run at a contant frequency and therefore at nearly contant peed. By that power pulation in the wind are almot directly tranferred to the electrical grid. Furthermore there i no control of the active and reactive power, which typically are important control parameter to regulate the frequency and the voltage. Becaue the wind i highly variable, it i, however very deirable to operate a wind turbine at variable peed. Beide thi, a the power range of the wind turbine increae thoe control parameter become more important and it i neceary to introduce power electronic [1] a an interface between the wind turbine and the grid. Variable peed wind turbine improve the dynamic behaviour of the turbine, allow operate at it maximal power producing point for a given wind peed and control of active and reactive power flow. Other advantage of variable peed wind turbine are reduced mechanical tree, reduced torque and power pulation, improved power quality and reduced noie at low wind peed. Thee odd are changing the baic characteritic of the wind turbine from being an energy ource to be an active power ource. Induction generator are widely ued for wind energy converion ytem. The advantage of cage induction generator are well known. Thee machine are relatively inexpenive, robut and require low maintenance. When induction machine operated uing vector control technique, fat dynamic repone and accurate torque control are obtained [2]. Signal of rotor peed of the induction generator i neceary for vector control purpoe. Uually, an encoder or a tacho-generator i ued to meaure the generator peed. Thee peed enor are enitive to heat and vibration, require additional wiring work, maintenance, and thu reduce the robutne of the overall ytem. Therefore, by eliminating the peed enor reliability of wind turbine drive i improved and cot i reduced. Vector control of the induction generator without peed enor require the ignal of the rotor peed that i obtained from meaured tator current and from the voltage at the generator terminal. A variety of different olution for enorle drive were propoed in the pat decade. Thee method could be broadly claified into two main categorie [3]. The firt approach i baed on mathematical model of induction machine, while the econd one conider o called econdary effect uch a the rotor lot harmonic in the air gap field, triple harmonic tator emf caued by aturation of the tator core, etc. Thee econdary effect alo offer a potentiality for peed etimation. Among the approache decribed above, MRAS peed etimator are the mot attractive due to their deign implicity [2], [4]. The ue of a model reference adaptive ytem eem to be one of the mot feaible approache for the implementation of adaptive control ytem. The prime characteritic of an adaptive ytem i the preence of a reference model, which can appear under variou form. In thi paper a variable peed wind turbine driven quirrel cage induction generator with two double ided PWM converter i decribed. The enorle vector control tructure baed on a rotor flux orientation i preented. Speed information, obtained from a MRAS oberver, i ued to control the electrical torque of the induction generator. In order to tune the MRAS ytem an evolutionary optimization algorithm are ued. It will be hown that gained peed etimation depend of applied optimization criteria and ued optimization algorithm. Simulation and experimental reult 978-1-4244-5794-6//$26. IEEE 949

obtained from laboratory prototype will be preented and fully analyed. II. SYSTEM CONFIGURATION The variable peed wind energy converion ytem with three phae quirrel cage induction generator and full-cale power converter i hown in Fig. 1. The induction generator i connected to the propeller wind turbine by way of the peedincreaing gear. The propeller wind turbine ha a pitch angle control ytem. When the wind peed exceed the rated value, the pitch angle control ytem regulate the wind turbine to keep the rotational peed at 1.5 time the value of the rated peed and keep the generator output at the rated output. When the wind peed i le than the rated value, the convertor regulate the generated output of the induction generator in order to keep the converion factor at the maximum value. The AC/DC/AC converter conit of the generator-ide converter and the grid-ide converter. The variable frequency and variable voltage power generated by the induction machine i rectified and pumped to DC link by generator-ide converter. Thi converter upplie lagging excitation current to the induction generator, alo. The gridide converter current are controlled uing a vector control approach leading to an independent control of active and reactive power flow between the upply converter and the grid. The power i therefore injected into the grid with low ditortion current and cloe to unity power factor. A. Vector Control of the Induction Generator Vector control technique i ued to control the machine current current allowing high current, and therefore torque, dynamic and control of the excitation or flux of the machine. In the experimental work preented in thi paper, the electrical torque i controlled according to the control trategy for below rated wind peed operation, which in teady tate, drive the wind energy converion ytem to the point of maximum energy capture [5]. 2 T e = Koptwr (1) In (1), the loe have been neglected and Kopt depend on the blade aerodynamic and wind turbine parameter. The electrical torque of the induction generator i calculated a 3 Lm Te = p ψ rdiq (2) 2 Lr where 3/2 arie from the 3-2 axe caling, i q i the torque producing current, L / L m r i the magnetizing/rotor inductance and ψ rd i the rotor flux d-component. Uing (1) and (2) the reference for the torque current can be obtained a 2 * 2 K opt Lr iq = w (3) 3 pl ψ m rd For enorle control, the etimated rotational peed w from the MRAS oberver i ued in (3). The rotor flux ψ rd and the lip frequency w l are etimated uing a flux model a hown in Fig. 1. Beide thi, the flux model, baed on meaure current, i ued to etimate the angle of orientation θ dq. Fig. 1. Schematic of the overall ytem 9

III. MODEL REFERENCE ADAPTIVE SYSTEM Adaptive control ha emerged a a olution for implementing high performance control ytem, epecially when dynamic characteritic of a drive are unknown, or have large and unpredictable variation [2]. MRAS bae peed etimation technique differ with repect to the quantity that i elected a output of the reference and alo depend on the adaptive model. Thi quantity can take the form of the rotor flux [2], [4] back emf [2] or reactive power [6]. The method dicued in thi paper i the rotor flux baed MRAS oberver. The block diagram of MRAS peed oberver i hown in Fig.2. Reference model i Adaptive model Generator Voltage model Current model w ψˆ ψˆ u ri G ω ( p)( ψˆ ψˆ ) ri Adaptive algorithm Fig. 2. MRAS oberver - block diagram The oberver i baed on two model, a reference (voltage) model and an adjutable (current) model. The etimator that doe not involve the quantity to be etimated i conidered a the reference model and the other etimator which conider the rotor peed i regarded a the adjutable model. The voltage model ued to obtain the rotor flux i defined uing the following equation [7] Lr pψ = [ u ( R + σl p) i ] (4) Lm where σ i the induction machine leakage coefficient, upercript refer to the tationary reference frame and ubcript and r refer to tator and rotor repectively. The rotor flux can be alo calculated from the adjutable (current) model a L m 1 pψ = i rc j w r Tr T ψ (5) r where T r i the rotor time contant. If we know the value of all parameter and rotor peed then the output of both reference and adjutable model hould match. Any mimatch between the peed of the generator and the adjutable model would automatically reult in an error between the output of the two etimator. Thi i the error between the rotor flux reference and the etimated rotor flux that will be ued to adjut the peed of the adaptive model. The error between the etimated quantitie obtained by the two model i ued to drive a uitable adaptation mechanim which generate the etimated rotor peed for the adjutable model. The error i feedback to a proportional-integral controller in which an adaptation algorithm ued to tune the peed o that the error i equal to zero. The adaptive model i adjuted until atifactory performance i achieved. The error in d reference frame i defined a q rc rdc rqv rqc rdv ξ = ψ ψ = ψ ψ ψ ψ (6) Equation (4) to (6) are ued to implement the MRAS oberver. The error calculated uing (6) i driven to zero uing linear PI controller. The output of PI controller i the etimated rotational peed, which i ued later on in calculating the reference for the torque current. K I w = K + ξ p (7) p In experimental realization of the MRAS oberver, reference model can not be implemented uing pure integrator [2]. Intead, the tranfer function 1/(p+1/p) i ued in experimentation preented in thi paper. B. Controller Optimization For optimization of PI controller in MRAS oberver via Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO), Matlab/Simulink model baed on Fig. 2. and equation (1)-(7) ha been deigned. Important part of optimization proce i definition of optimization criteria. Baed on the reult achieved in [8] ome modification to applied criteria have been done. Optimization criteria ued in thi paper mut achieve two main goal. At firt, mut minimize integral abolute error (IAE) to achieve bet tracking performance and econdly, mut minimize parameter of PI controller K p and K i to reduce noie influence on the etimated peed. The optimization criteria i tated a J opt C1 dt + C2K p + C3 = ξ K (8) Coefficient C 1 3 are ued to cale phyically different parameter to relative unit and to normalize dimenion of optimization criteria. Value are determined empirically. Default Matlab value for optimization algorithm parameter are ued (except: GA generation, population ; PSO iteration, particle 5; SA iteration 25). Overview of optimization reult achieved via imulation in Matlab/Simulink i hown in Table I. TABLE I OPTIMIZATION RESULTS Algorithm K p K I J opt GA 525.7 161 36.797 PSO 525 163 36.792 SA 94 16886 34.163 From Table I could be een that a minimal optimization criterion ha been achieved via SA algorithm. Verification of imulation reult i carried out via laboratory model and comprehenive comparative experiment. I 951

IV. EXPERIMENTAL RESULTS A 1.5 kw, 38 V, Hz, four-pole cage induction machine -generator i utilized in the experimental prototype. One laboratory prototype of inverter with the 8 khz witching frequency and one commercial inverter are ued. Laboratory prototype of inverter i realized by the IGBT Semikron module. In the machine ide two line current are meaured via two Hall enor. To avoid noie the meaured current are filtered by two low pa filter with a cut-off frequency of about 2 khz. A peed encoder of 36 ppr i ued to calculate the ytem peed. Thi peed ignal i ued for comparion purpoe. The voltage tranducer i ued for dc link voltage meaure. The generator i driven by a torque controlled 2.2 kw, two-pole induction motor. The experimental etup i hown in Fig. 3. Baed on theoretical background and imulation reult previouly expoed a comprehenive experiment have been conducted. Trigger() oftint Iabc Udc Meaurement of current and DC voltage w,theta DS14SLAVE_PWMINT DS14SLAVE Board PWM-Interrupt Tak Tranition (double buffer write) Iabc Udc w,theta Trigger() w,theta_etimated Encoder Iqref Tak Tranition (double buffer write) Iqref Generator peed and torque regulation Current controller Fig. 4. Induction generator control with dspace 14 Fig. 3. Induction generator tet bench Control algorithm were realized uing the ACE14 developing ytem that i the DS14 control board, baed on a PowerPC 63e proceor. The complete oftware i developed in Matlab/Simulink and Total Development Environment - TDE [9]. The control tructure i programmed a a Simulink graphic model and it i implemented by Real Time Interference - RTI on DS14 R&D Controller Board. A Simulink block diagram repreentation of variable peed enorle control of induction generator i illutrated in Fig. 4. The induction generator control i implemented in three tak containing the I/O and two layer. The meaure tak i directly triggered by the PWM interrupt of the TMS3F2. Thi cloely connect the tak cycle of the TMS3F2 and PowerPC 63e to avoid jitter. It work at a PWM frequency of 8 khz and generate a ynchronouly oftware interrupt with a 4 khz. Thi interrupt i ued to trigger the uperior current control tak. The ubordinated torque control of induction generator work in a third tak and i triggered by the timer interrupt of the PowerPC 63e, which occur with a frequency of 2 khz. For the quirrel cage induction generator enorle control two 16 - bit A/D converter and one incremental encoder channel of the DS14 are ued. In order to teted performance of the control ytem for PI parameter in MRAS oberver gained with each algorithm, comparative experiment have been conducted firtly. Fig. 5-9 give overview of thee reult. Induction generator run from zero to randomly given reference of the drive machine. Fig. 8 and Fig. 9 how magnified ignal of etimated haft peed. It can be noted that i achieved relatively good etimation of haft peed in all three cae. Table II give overview of mean abolute error between etimated and meaured haft peed. n 1 MAE = ei, ei = w( i) _ etimated w( i) _ meaured (9) n i= 1 TABLE II MEAN ABSOLUTE ERROR (MAE) VALUES Algorithm 25[rad/] [rad/] [rad/] GA 4.53% 1.11%.58% PSO 4.56% 1.3%.56% SA 4.53%.93%.34% Bet performance i achieved via SA algorithm, a hown in Table II. In thi cae, the MRAS oberver tracking the real peed during the whole teted period with very mall error. The error between the etimated peed from the MRAS oberver and the real peed from the encoder i almot negligible. Increaing of peed etimation error at lower peed i obviouly. The experimental reult hown in Fig. are obtained uing the etimated peed a the feedback on the induction generator torque and peed regulation ubytem. The peed ignal obtained from the encoder i ued only to check the reult. 952

Meaured peed [rad/] Etimated peed [rad/] 8 6 - - 2 4 6 8 12 14 16 18 8 6 - - 2 4 6 8 12 14 16 18 Eimated peed [rad/] Eimated peed [rad/] 52 51 49 48 3 3.5 4 4.5 Partical warm 52 51 49 Genethic algorithm 48 3 3.5 4 4.5 Simulated annealing 52 Etimation error [%] Eimated peed [rad/] 51 49 2 4 6 8 12 14 16 18 48 3 3.5 4 4.5 Fig. 5. Meaured veru etimated peed for PI parameter gained via GA Fig. 8. Etimated peed for [rad/] Meaured peed [rad/] Etimated peed [rad/] Etimation error [%] 8 6 - - 2 4 6 8 12 14 16 18 8 6 - - 2 4 6 8 12 14 16 18 2 4 6 8 12 14 16 18 Fig. 6. Meaured veru etimated peed for PI parameter gained via PSO Eimated peed [rad/] Eimated peed [rad/] Eimated peed [rad/] 72 71 69 68.5 11 11.5 Partical warm 72 71 69 Genethic algorithm 68.5 11 11.5 Simulated annealing 72 71 69 68.5 11 11.5 Fig. 9. Etimated peed for [rad/] Meaured peed [rad/] 8 6 - - 2 4 6 8 12 14 16 18 Etimated and meaured haft peed [rad/] Etimated peed [rad/] Etimation error [%] 8 6 - - 2 4 6 8 12 14 16 18 Stator current, id [A] ωˆ ω Stator current, iq [A] 2 4 6 8 12 14 16 18 Fig. 7. Meaured veru etimated peed for PI parameter gained via SA Fig.. Meaured peed and etimated peed, flux current and torque current with w etimated feedback 953

From to 8 econd the machine wa working with no load. After that, the load wa incrementally increaed. It can be noted that at the very beginning there i a light mimatch between actual and etimated peed, but after that the etimated peed almot perfectly matche the actual rotor peed of the induction generator. The wind turbine enorle vector controlled drive i completely table from zero. V. CONCLUSION Thi paper ha preented a enorle vector control trategy for an induction generator in a variable peed wind energy converion ytem uing a MRAS oberver to etimate the rotational peed of the induction generator. In the enorle ytem, the application of artificial intelligence optimization algorithm ha been dicued. It i hown that gained reult depend on developed optimization criteria, ued optimization algorithm and it parameter. The dynamic performance of MRAS oberver with developed optimization i very good and can be ued to obtain an accurate etimation of the rotational peed not only in teady tate but alo when fat input change a wind tep are applied to the wind energy converion ytem. In thi paper influence of the machine parameter variation on the peed etimation i not taken into account. APPENDIX A. Induction machine - generator: Type: ZK 9 L4, P = 1.5 kw, ω (rated) = 15 r/min, R = 5.25 Ω, R r = 3.17 Ω, L γ =,242 H, L γr =,1 H, L =,2992 H, L r =,285 H, L m =,275 H, T r = 9 m, J =,332 kgm 2, i d (rated) = 2,83 A, i q (rated) = 4,6 A. B. Induction machine - drive machine: Type: ZK 8 L2, P = 2.2 kw, ω (rated) = 2885 r/min, I (rated) = 5,2 A, V (rated) = 38 V. C. Converter: Switching frequency: 8 khz, V max = V, I max = A, Dead time: 3,25 μ. REFERENCES [1] N. Mohan, T.M. Undeland, W.P.Robbin, Power Electronic Converter Application and Deign. 1 t edition, John Wiley and Son, 1998. [2] P. Va, Senorle Vector and Direct Torque Control, Oxford Univerity Pre, New York 1998. [3] J. Holtz, Perpective of Senorle AC Drive Technology, 27 th International PCIM Europe, Nurenberg, 5, pp. 8-87, Jun. 5. [4] C. Schauder, Adaptive peed identification for vector control of induction motor without rotational tranducer, IEEE Tranaction on Indutry Application, No. 5, pp. 54-61, 1992. [5] S.M.B. Wilmhurt, Control trategie for wind turbine, Wind Energy, Vol. 12, No. 4, pp. 236-249, 6. [6] Z. Peng, T. Fukao, Robut peed identification for peed enorle vector control of induction motor, IEEE Tranaction on Indutry Application, No. 5, pp. 1234-12, 1994. [7] V. Vaic, S. Vukoavic, E. Levi, A Stator Reitance Etimation Scheme for Speed Senorle Rotor Flux Oriented Induction Motor Drive, IEEE Tranaction on Energy Converion, Vol. 18, No. 4, pp. 476-483, 3. [8] K.J. Atrom, Feedback Sytem, Princeton Univerity Pre, ISBN-13: 978--691-13576-2, 8. [9] dspace, Solution for Control, DS14 R&D Controller Board Paderborn, 4. [] V. Vaic, Control of Induction Motor Without Speed Senor, Univerity of Beograd, Faculty of Electrical Engineering, Beograd, 1. 954