A PROPOSED ESTIMATOR FOR A DIRECT TORQUE CONTROL OF INDUCTION MOTOR BY USING RADIAL BASIS FUNCTION NEURAL NETWORK TECHNIQUE

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1 Diyala Jounal of Engineeing Sciences ISSN Pinted in Iaq Vol. 04, No. 02, pp , Decembe 2011 A PROPOSED ESTIMATOR FOR A DIRECT TORQUE CONTROL OF INDUCTION MOTOR BY USING RADIAL BASIS FUNCTION NEURAL NETWORK TECHNIQUE D. Mohammed Khalaf Masood Lectue / Diala Education Ministy of Education (Received:20/6/2010 ; Accepted:3/10/2010) D. Essam. M. Abdul-Baki Asst. Pof. / College of Engineeing Univesity of Al-Mustenseia ABSTRACT:- The pupose of this pape is to use Radial Basis Function Neual Netwok (RBFNN) as an estimato fo stato flux and electomagnetic toque in Diect Toque Contol (DTC) systems used as a dive of a 3-phase induction moto, in ode to educe the ipples in the output toque. This pape includes design, constuction and taining fo thee diffeent modes of opeation of RBFNN, in which the spead constant has a diffeent value fo each estimated paamete duing the netwok taining. Then, the netwok, which has independent outputs, gives the best esults choused as an estimato in the poposed DTC system. Matlab/neual netwok toolbox used fo taining the poposed estimato at diffeent load toques. The Simulation esults ae obtained using pogam of Matlab/Simulink. The coincidence of the values of the output data obtained fom the poposed estimato and that fom the conventional one poves the poposed system accuacy. Keywod: Induction Moto, Radial Basis Function Neual Netwok, Diect Toque Contol. 1- INTRODUCTION Diect Toque Contol (DTC) system used as a vaiable speed dive fo induction machines has emeged in the middle of 1980's by Isao Takahshi and M. Depenbock. In this system, the contol of the stato flux and the toque occus diectly by selecting the appopiate invete state. DTC system is the fist technology to contol the "eal" moto contol vaiables of toque and flux [1], [2]. The main featues of DTC system ae summaized as follows: 1- It opeates with closed toque and flux loops without cuent contolles. 2- It needs stato flux and toque estimation. 3- It is a speed sensoless contol method. 4- It has simple and obust contol stuctue; howeve, the pefomance of DTC stongly depends on the quality of the estimation of the flux and toque. In ode to impove DTC pefomance thee was many contol stategies pesented since 1990's, one of these methods was the Atificial Intelligent (AI) [Atificial Neual Netwoks (ANNs), Fuzzy Logic Contol (FLC)], This contol stategy can be used as an estimato to impove the DTC system fed induction moto [3], [4]. This pape uses the adial basis function neual netwoks (RBFNN) to opeate as an estimato fo stato flux and electomagnetic toque instead of conventional estimato to 72

2 impove the estimation pocess of the above paametes, subsequently impoves the pefomance of the DTC system. 2- THE DIRECT TORQUE CONTROL TECHNIQUE Figue(1) shows schematically the block diagam of DTC system that equies an efficient flux and toque estimato. In nomal opeation, two of the moto phase cuents (i a, i b ) and the DC bus voltage (V d.c ) ae simply measued, togethe with the invete s switch positions (V sa, V sb, V sc ). The measued infomation fom the moto fed to the stato flux and toque estimatos (Adaptive Moto Model) [5], [6]. This system depending on the measued values of voltages and cuents fo the induction moto to estimate the stato flux and electomagnet toque and to use them as a feedback signals fo contol. The estimated values ae compaed with the efeence ones; the magnitude of the eo between them compaed by hysteesis compaatos. A switching table (that uses the outputs of the compaatos and the position secto detecto of the stato flux) is achieved to get the optimal voltage vecto needed fo the powe-switching device of the invete. The constuction of DTC model is pefomed using Matlab/Simulink pogam [7]. The equations (1) to (7) efe to the dynamic model of a 3-phase balanced squiel cageinduction moto in a d-q stationay efeence fame, neglecting the satuation effects [9]. d sd Vsd s. isd dt (1) d sq Vsq s. isq dt (2) d d L m q d i sd dt L L (3) d i i q L m d q i (4) sq dt L L sd L m sd (5) d sq L L L s s sq L m (6) q L s L s L whee 2 ( L s L L m ) / L The voltage selection based on a 6-secto d-q plane, with the stato flux magnitude and the toque angle involved. The following equation elates the electomagnetic toque and the stato flux: C i sin (7) e s whee: (C) is a constant; (θ) is the angle existing between the stato flux and the oto flux, (toque angle).[6] In each secto, the stato flux and electomagnetic toque change depend on the voltage vecto selected. Fo example, if in the fist secto a [V 3 (0 1 0)] voltage vecto is selected, the stato flux magnitude deceases; but the electomagnetic toque inceases along with the toque angle. Fo each secto, a specific voltage vecto selection, changes stato flux magnitude and toque chaacteistics in a diffeent way, as it show in Figue(2). [7]. 3- RADIAL BASIS FUNCTION NEURAL NETWORK(RBFNN) MODEL The name Radial Basis Function stems fom the adial symmety with espect to the cente. The RBFNN constucted fom input, hidden layes of nomalized Gaussian activation 73

3 functions and output. The RBFNN based on the concept of the locally tuned and ovelapping eceptive field stuctue. RBFNN is widely used as a univesal appoximato in the aea of nonlinea mapping due to its pefomance despite a simple stuctue [10]. Figue (3) shows a adial basis function neual netwok with i th inputs. Notice that the expession fo the net input of a (adbas) neuon is diffeent fom that of neuons in conventional NN. Hee the net input to the (adbas) tansfe function is the vecto distance between its weight vecto (W) and the input vecto (x) multiplied by the bias (b). The ( dist ) box in this figue accepts the input vecto (x) and the single ow input weight matix, and poduces the dot poduct of the two. Figue (4) shows a plot of the (adbas) tansfe function. The tansfe function fo a adial basis neuon (adbas) is: 2 n adbas n e (8) The adial basis function has a maximum of (1) when its input is (0). As the distance ( dist ) between (W) and (x) deceases, the output inceases. Thus, a adial basis neuon acts as a detecto that poduces (1) wheneve the input (x) is identical to its weight vecto (W). The bias (b) allows the adjustment of the sensitivity of the (adbas) neuon. Fo example, if a neuon had a bias of (0.1) its output is (0.5) fo any input vecto (x) at vecto distance of (8.33) [0.833/b] fom its weight vecto (W) (see Figue (4)) [11][12]. 4- DTC BASED ON RBFNN AS AN ESTIMATOR Figue (5) shows the use of RBFNN fo DTC system as an estimato instead of classical estimato. Fom this figue, the input data used in the tained RBF netwoks ae two moto phase cuents (i sa, i sb ), the DC bus voltage (V dc ), and the invete's switch positions (V sabc ), while the output data ae the electomagnetic toque (T e ), stato flux (λ s ), and secto (S k )[flux angle (θ s )]. In ode to achieve a satisfactoy DTC system, the design, constuction and taining of a thee diffeent cases of the RBFNNs opeates as flux and toque estimatos of DTC system by using neual netwok toolbox in Matlab pogam as follow: A- Case No.1: RBFNN has six inputs include (i sa, i sb, V dc, V sa, V sb, V sc ) and one output appose (T e, λ s,s k ) togethe, as it show in Figue (6). B- Case No.2: RBFNN has six inputs include (i sa, i sb, V dc, V sa, V sb, V sc ), two outputs appose (λ s, S k ) togethe and (T e ), as it show in Figue (7). C- Case No.3: RBFNN Netwok has six inputs include (i sa, i sb, V dc, V sa, V sb, V sc ), thee sepaated outputs; (T e ), (λ s ) and (S k ) as it show in Figue (8). 5- SIMULATION RESULTS The simulation and taining fo RBFNN of the thee diffeent cases as estimatos fo stato flux and electomagnetic toque in the DTC dive of induction moto by using NN-tool box and Simulink in Mat lab pogam, gives the ability to compae thei esults at (no-load, half-load, full-load) with these of the classical one. The eos values of the thee paametes fo both estimatos, the electomagnetic toques (T e ), stato fluxes (λ s ) and sectos (S k ) fo the thee case and the thee diffeent loading conditions ae shown in Figues (9, 10, 11). whee (ET e -Net, Eλ s -Net, ES k -Net): Values of the diffeence between Estimated values fom conventional DTC and that of the poposed one fo electomagnetic toque (T e ), stato fluxes (λ s ) and sectos (S k ) espectively fo the thee Nets (Net1, Net2, Net3). 74

4 Table (1) shows the aveage values of eos of (T e, λ s, S k ) fo the above thee cases, and then the best case of the RBFNN has been chosen to be simulated. whee eos of (T e, λ s, S k ): ae calculated taking into consideation that the spead constant is not the same fo the diffeent paametes, it has the following values fo the thee vaiable (T e, λ s, S k ) as (1, 0.5, 0.3) espectively. Table (1) leads to a conclusion that Net-3 is the best choice because of its educed eo as compaed with that of Net-1 and Net-2. This is because in this net, the taining occus sepaately and the output of each paamete is independent on the othes, so this featue will leads to easiness in netwok opeation and gives fastest esponse and accuate esults. A simulation was caied out to veify the RBFN estimato whee the squiel cage induction moto used in this case study is a 2.2Kw, 400V, 4pole, 50Hz, (T ef = 14.6Nm).[see appendix-a]. Figues (12), (13) and (14) show pefomance accuacy of taining RBFNN. Also, value of spead constant is equal to (1) when RBFNN tained on data with diffeent values fo electomagnetic toque while value of spead constant of stato flux is equal to (0.5) and the sectos is equal to (0.3), at diffeent values of load toque, this means, the netwok which has thee independent outputs gives ight esults and exact. 6-CONCLUSION The pape pesents a poposed estimato suitable to wok with the DTC system used as a speed contolle fo thee-phase induction moto in ode to educe the toque ipples. The poposed RBFNN shows a good substitute to the conventional one due to its easiness in netwok opeation and gives fastest esponse and accuate esults. These featues will help to educe the toque ipples of the output toque. REFERENCES 1- I.Takashi and T. Noguch,"A new Quick-Response and High-Efficiency Contol Stategy of an Induction Moto", IEEE tansaction on industy applications, vol. IA-22, No.5, sep. /oct M. Depenbok, "Diect Self-Contol (DSC) of Invete-Fed Induction Machine", IEEE tansaction on powe electonics, vol. 4, oct X. d. Gacia, A. Aias, M.l G. Jayne, P.l A. Witting, V. M. Sala, J. L. Romeal," New DTC Contol Scheme fo Induction Motos fed with a Thee-level Invete", AUTOMATIKA, vol. 46, No 1 2, 2005 pp M. ˇZalman, I. Kuic," Diect Toque and Flux Contol of Induction Machine and Fuzzy Contolle", Jounal of Electical Engineeing, vol. 56, No. 9-10, 2005, pp Asea Bown Bovei Ltd (ABB),"Diect Toque Contol- Technical Guide No.1", ABB Automation Goup Ltd, Finland, A. A. Pujol, "Impovements in Diect Toque Contol of Induction Motos," PhD Thesis, Univesity of Politeqnique of Catalunya (UPC), Teasa, P. Ponce, D. M. Aguila and A. Monoy, "Using Atificial Neual Netwoks in the Induction Moto DTC Scheme", 35h Annul IEEE Powe Electonics Specialists Confeence, Aochen, Gemany, S. B. Oztuk, "Modeling, Simulation and Analysis of Low-Cost Diect Toque Contol of PMSM Using Hall-Effect Sensos", MSC Thesis Submitted to Texas A&M Univesity, Decembe B. K. Bose, "Moden Powe Electonics and AC Dives", Pentice-Hall PTR, USA,

5 10- Kyo-Beum Lee, Sung-Hoe Huh, Ji-Yoon Yoo and Fede Blaabjeg, "Pefomance Impovement of DTC fo Induction Moto-Fed by Thee-Level Invete with an Uncetainty Obseve Using RBFN", IEEE tansactions on enegy convesion, Vol. 20, No. 2, June K. M. Passino, S. Yukovich, "Fuzzy Contol", Addison Wesley, Inc H. Demuth, M.Beale, "Neual Netwok Toolbox fo Use with Matlab" Use's Guide, Vesion 4, Fig.(1): Basic diagam of the DTC system [8]. Fig.(2): Voltage space vecto and secto epesentation. 76

6 Fig.(3): Radial Basis Function Neual Netwok Model. Fig.(4): Radial basis function. Fig.(5): Scheme of DTC based on RBFNN. 77

7 Fig.(6): RBFNN estimato fo (Case No.1). Fig.(7): RBFNN estimato fo (Case No.2). Fig.(8): RBFNN estimato fo (Case No.3). 78

8 (a) Toque eo values (ET e ) fo Net1, Net2, Net3. (b) Flux eo values (EF s ) fo Net1, Net2, Net3. (c) Secto eo values (ES k ) fo Net1, Net2, Net3. Fig.(9): No-load eo values fo the thee cases (a, b, c). 79

9 (a)toque eo values (ET e ) fo Net1, Net2, Net3. (b)flux eo values (EF s ) fo Net1, Net2, Net3. (c) Secto eo values (ES k ) fo Net1, Net2, Net3. Fig.(10): Half-load eo values fo the thee cases (a, b, c). 80

10 (a) Toque eo values (ET e ) fo Net1, Net2, Net3. (b) Flux eo values (EF s ) fo Net1, Net2, Net3. (c) Secto eo values (ES k ) fo Net1, Net2, Net3. Fig.(11):Full-load eo values fo the thee cases (a, b, c). 81

11 Table (1) Aveage Eo No load Half load Full load T e -case T e - case T e - case λ s case λ s case λ s case S k case S k case S k case p o p o s e d e s t i m a t e d s t a t o f l u x ( W e b e ) Fs conv. Fs FNN c o n v e n t i o n a l e s t i m a t e d s t a t o f l u x ( W e b e ) p o p o s e d e s t i m a t e d t o q u e ( N m ) Te conv. Te FNN c o n v e n t i o n a l e s t i m a t e d t o q u e ( N m ) (a) Estimated stato flux. (b) Estimated electomagnetic toque. Fig.(12): Pefomance accuacy of taining RBFNN at (T L =14 Nm). 82

12 (a) Estimated stato flux (b) Estimated electomagnetic toque Fig.(13): Pefomance accuacy of taining RBFNN at (T L =10 Nm) p o p o s e d e s t i m a t e d s t a t o f l u x ( W e b e ) Fs conv. Fs FNN c o n v e n t i o n a l e s t i m a t e d s t a t o f l u x ( W e b e ) p o p o s e d e s t i m a t e d t o q u e ( N m ) Te conv. Te FNN c o n v e n t i o n a l e s t i m a t e d t o q u e ( N m ) (a) Estimated stato flux. (b) Estimated electomagnetic toque. Fig.(14): Pefomance accuacy of taining RBFNN at (T L =6 Nm). 83

13 APPENDIX-A The 3-phase Squiel cage induction moto paametes ae, 400 volt, 50 Hz, 2.2k watt shown in the table (A-1).. Table (A-1): the moto paametes. Paametes Symbol Value Stato Resistance s 367 Ω Roto Resistance 2.10 Ω Stato Leakage Inductance L ls H Roto Leakage Inductance L l H Magnetizing Inductance L m H Numbe of Pole p 4 Moment of Inetia J kg.m 2 Viscous Fiction Coefficient β N.m.s Rated Speed ω 1430 /min. Rated Toque T ef 14.6 N.m Rated Cuent pe Phase I at 5.0Amp. Rated Powe Facto cosφ

14 مخمن مقترح للسيطرة المباشرة على العزم لمحرك حثي باستخدام الا ساس الشعاعي العصبية شبكة دالة تقنية د.محمد خلف مسعود م./تربية ديالى وزارة التربية د.عصام محمود عبد الباقي ا.م./ كلية الهندسة الجامعة المستنصرية الخلاصة الغرض من هذا البحث هو استخدام مقترح لشبكة دالة الاساس الاشعاعي العصبية (RBFNN) كمخمن لقيم العزم الكهرومغناطيسي والفيض في نظام السيطرة المباشرة على العزم (DTC) الذي يستخدم كمسوق لمحرك حثي ثلاثي الطور و ذلك لتقليل التموجات الحاصلة في العزم المتولد. يتضمن هذا البحث تصميم وبناء و ت دري ب ثلاث حالات مختلفة لشبكة دالة الاساس الاشعاعي العصبية ح ثي ا ن ثابت الانتشارالمعتمد عند تدريب الشبكة كان بقيم مختلفة لكل من العزم وفيض الساكن وموضع زاوية الفيض. تم اختيار الشبكة ذات المخارج المستقلة لانها تعطي افضل النتاي ج واستعملت في النظام المقترح للسيطرة المباشرة على العزم ا ن الشبكة المختارة تدربت لقيم مختلفة من عزم الحمل باستخدام برنامج ا ل( Matlab/NN-toolbox ). تم الحصول على نتاي ج المحاكاة باستخدام برنامج ال( Matlab/Simulink ). دقة اداء النظام المقترح استنتجت من التطابق في قيم خرج الشبكة المقترحة كمخ من مع قيم خرج المخم ن التقليدي. 85