Low Pass Filtering Based Artificial Neural Network Stator Flux Estimator for AC Induction Motors

Transcription

1 Senor & Tranducer, Vol. 161, Iue 12, December 2013, pp Senor & Tranducer 2013 by IFSA Low Pa Filtering Baed Artificial Neural Network Stator Flux Etimator for AC Induction Motor 1, 2 Yuhan Ding, 1 Shaoqing Zhou, 1 Congli Mei, 1 Hui Jiang 1 Jiangu Univerity, Zhenjiang, , China 2 Key Laboratory of Meaurement and Control of CSE (School of Automation, Southeat Univerity), Minitry of Education, Nanjing, China yhding@uj.edu.cn, zqlove2008@hotmail.com Received: 18 September 2013 /Accepted: 22 November 2013 /Publihed: 30 December 2013 Abtract: A novel artificial neutral network tator flux etimator baed on low pa filter for induction motor i propoed in thi paper. Firtly, low pa filter i ued to extract the feature of the tator voltage. Then, the feature ignal are elected a input of an artificial neutral network regreion model to etimate tator flux. The combination tator flux etimation algorithm with low pa filter and artificial neutral network reduce the interference induced by high frequency ignal and ha fine dynamic performance. Simulation how high performance of the propoed tator flux etimator under different torque. Copyright 2013 IFSA. Keyword: Induction motor, Direct torque control, Stator flux etimation, Artificial neutral network, Low pa filter. 1. Introduction Owing to their imple tructure and tability, AC induction motor (IM) have been widely ued in many field [1]. An accurate flux etimation i very important to a high performance induction motor drive, uch a Direct Torque Control (DTC) [2], which ha attracted more and more attention in recent year, due to it imple tructure and ability to achieve fat torque and flux control. A typical IM ha no flux enor built in, and there i no place provided in it to mount uch enor. Flux can be obtained by oberver baed on mathematic model [3, 4] uch a Kalman filter [5], Luenberger oberver [6-8], etc. Mot of the flux etimator are baed on the voltage model, the current model, or a combination of both [3, 10]. The etimator baed on the current model require the knowledge of tator current and rotor peed. In ome indutrial application, the ue of an incremental encoder to get the peed or poition of the rotor i undeirable ince it reduce the robutne and reliability of the drive. It ha been widely known that even though the current model ha managed to eliminate the enitivity to the tator reitance variation. The etimator are enitive to the rotor parameter, epecially at high rotor peed [3, 10, 11]. The voltage model i normally ued at high peed. And ome problem arie at low peed [9-12]. In practice, even a mall DC offet in the back electromotive force (EMF) can caue the integrator to aturate [12, 13]. To overcome thi, a low pa filter () i normally ued in place of a pure integrator [13, 14]. However, compared to the pure integrator, particularly at frequencie cloe to the cut-off, will reult in phae and magnitude error. Attempt have been made to improve the etimated tator flux baed on the a given by [13-16]. In [13], the propoed method create a novel arithmetic between the pure integrator and the. Article number P_

2 Senor & Tranducer, Vol. 161, Iue 12, December 2013, pp The method ue an adaptive control ytem, which i baed on the fact that the back EMF i orthogonal to the tator flux. The compenator i adapted for thi condition. However, implementation of the propoed ytem require large proceor reource and ignificantly increaed the complexity of the control ytem. In [14], the error between and pure integrator were computed and compenated. But the compenation i baed on the teady-tate condition and only can be ued under teady-tate condition. In [15], two different cut-off frequencie were ued in back EMF equation. But it will degrade the ability of driving to ome extent. A new type of tator flux integration ha been propoed in [16]. Output of the flux i ued for feedback baed on firt-order. Control effect of the algorithm i remarkable. But it i more complex. By analyzing the error of the baed etimator, a imple compenation method ha been propoed [17]. Thi method i mainly applied to olve the teady-tate ituation problem. And it i largely ineffective to improve the flux etimation accuracy at low peed. The voltage model ha been ued in the high peed area and current model in the low peed area [18]. The diadvantage of thi hybrid model i that it i very difficult to witch quickly and moothly between the two model. In thi paper, to overcome the problem of the integration for tator flux etimation, a novel artificial neutral network tator flux etimate method baed on low pa filter i propoed. The propoed method i very imple. And it i a Wiener model in nature, which conit of a linear dynamic () and a nonlinear tatic (ANN), yet it can improve the dynamic performance of tator flux etimation. Simulation how the high performance of the method. 2. Stator Flux Etimation 2.1. Principle of Flux Etimation Baed on The tator flux calculation baed on the tator voltage equation i given by [19]: It frequency expreion i: u Ri dt (1) u Ri, (2) je where i the tator flux, R i the tator reitance, and u and i are the meaured terminal voltage and current, repectively. e i the motor tator ignal when teady-tate operation of angular. The integration of (1) by pure integrator uffer the drift and the aturation problem. To olve the problem, traditional trategy i to replace the pure integrator by a. The tructure of the tator flux etimation ytem i howed in Fig. 1. Logical Caculation Part Fig. 1. Structure of the tator flux etimation ytem. Then the Equation (1) can be written a u Ri ' je c, (3) where c i the cut-off frequency of the. By comparing (2) with (3), ' where ' c ' je c 1 e tan. 2 c, (4) Actually thi method will degrade the performance of the ytem. From the above equation, the magnitude of the etimated tator flux i alway le than the actual one which can reult in magnetic flux aturation in low peed region. On the other hand the phae error will alo lead to incorrect voltage vector election. When the etimated flux enter a new ector, the actual flux i till in the previou ector, o the voltage vector will be elected incorrectly Cacaded Baed Flux Etimation A programmable cacaded i propoed to olve the drift problem and to etimate exactly tator flux [9, 20]. The principle of the cacaded method of integration can be explained a follow. Since the drive ha to operate in a wide frequency range, a ingle-tage integrator ha to be deigned with a very large time contant. Thi caue the problem of DC offet and it very low decay, a dictated by the time contant. If a ingle-tage integrator i reolved into a number of cacaded 220

3 Senor & Tranducer, Vol. 161, Iue 12, December 2013, pp with a hort time contant, the problem of DC offet decay time can be harply attenuated [9]. The tructure of the tator flux etimation ytem i howed in Fig. 2. ANN Fig. 3. Structure of the ANN baed tator flux etimation ytem Baed Artificial Neural Network Stator Flux Etimation Fig. 2. Structure of Cacaded Baed flux etimation ytem. The programmable cacaded perform back EMF integration. The algorithm decribed in [9] doe not introduce acceptable etimation when a direction of a tator field rotation i changing. And the cheme alo ha a drawback in that the time contant of the will be very large at time [9] Artificial Neural Network Baed Flux Etimation Artificial neural network (ANN) are uitable for AC motor tate etimation, becaue of their known advantage, uch a the ability to approximate any nonlinear function to a deired degree of accuracy, learning and generalization, fat parallel computation, robutne to input harmonic ripple, and fault tolerance [21, 22]. Thee apect are important in the cae of nonlinear ytem, like converter-fed AC drive, where linear control theory cannot be directly applied. Additionally, highefficiency power electronic converter ued for ac motor operate in witch mode, which reult in very noiy ignal. For thee reaon, ANN are attractive for ignal proceing and control of AC drive. The uual ANN model i the multilayer feedforward network uing the error back propagation algorithm (BP) [23]. The artificial neural network can be ued directly to deign a new oberver of the tator flux. The idea i to model flux directly uing tator current and voltage a input intead of back EMF. The tructure of the ANN baed tator flux etimation ytem i howed in Fig. 3. Depite many advantage, the ANN etimator ha eriou limitation inherited. It require that initially elected ampling time i applied to the data in learning. If we want the etimated flux to be mooth, hort ampling time hould be conidered, which can reult in large training et. And the ANN model i a tatic model without fine dynamic performance. Inpired by the above method, we preent the idea of combining and ANN a a new tator flux etimator. Fig. 4 how the baed ANN tator flux etimator tructure. ANN Fig. 4. The tructure of the tator flux etimation ytem. The cut-off frequency ω c of the influence the error of the flux etimator greatly. In practice, the cut-off frequency can t be et too mall ince the tability of the control ytem will be degraded when the cut-off frequency i too mall. According to [24], we et the ω c at 30 rad/. 3. Simulation and Experiment reult In order to get the data, a converter-fed DTC ytem i carried out. The training pattern are prepared by numerical imulation of the induction motor model in the tationary reference frame ( - ) with help of MATLAB and SIMULINK. In imulation the nominal data of 3.7 kw induction motor i ued. Table 1 how the parameter of induction motor. The deign and training of a neural network for atifactory performance require very time conuming iterative procedure with large training data table. At lat we choe the data in the condition of the motor at 1000 rpm and under the rated load torque TL 25 Nm. By le than 600 time training, the ultimate error i le than 1.00e

4 Senor & Tranducer, Vol. 161, Iue 12, December 2013, pp Table 1. Parameter of induction motor. Parameter Value Nominal power 3730 W Pole pair 2 Nominal flux 1.5 Wb Stator reitance Rotor reitance Nominal peed 1420 r/min Mutual inductance H Inertia 0.02 kg.m 2 Fig. 10 to Fig. 12 how the reult when load torque exceed the training range (from ( ) TL). The preented reult were obtained by the baed ANN etimator in teady-tate under the TL at 1000 r/m in Fig. 5. From Fig. 5, it can be concluded that the new etimator how high performance. Fig. 6 to Fig. 12 how the robutne performance of the propoed method under different load torque. Fig. 5. The ANN etimation reult in teady-tate under the TL at 1000 r/m compared with the real value. Fig. 6. Flux (up) and flux (down) etimation error at 1000 r/m under 0.2 TL. Fig. 7. Flux (up) and flux (down) etimation error at 1000 r/m under 0.4 TL. Fig. 8. Flux (up) and flux (down) etimation error at 1000 r/m under 0.6 TL. 222

5 Senor & Tranducer, Vol. 161, Iue 12, December 2013, pp Fig. 9. Flux (up) and flux (down) etimation error at 1000 r/m under 0.8 TL. Fig. 10. Flux (up) and flux (down) etimation error at 1000 r/m under 1.2 TL. Fig. 11. Flux (up) and flux (down) etimation error at 1000 r/m under 1.4 TL. Fig. 12. Flux (up) and flux (down) etimation error at 1000 r/m under 1.6 TL. 6. Concluion A novel baed ANN tator flux etimator i propoed in the paper. Simulation how the baed ANN etimator perform good performance of robutne to torque change. Thi method ha ome certain propect in the etimation of tator flux of AC induction motor. Acknowledgement Thi work i upported by the Priority Academic Program Development of Jiangu Higher Education Intitution under Grant PAPD [2011] 6, China Potdoctoral Science Foundation under Grant , Jiangu Potdoctoral Sutentation Fund, China under Grant C, and the open 223

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