Adaptive Neural Fuzzy Inference Systems Controller for Hybrid PWM based vector controlled Induction motor drives

Transcription

1 16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, Adaptive Neural Fuzzy Inference Systems Controller for Hybrid PWM based vector controlled Induction motor drives N.Ravi Sankar Reddy #1, T.Brahmananda Reddy #1, J.Amarnath *, D.Subbrayudu #1 # Electriccal and Electronics Department,G.Pulla Reddy Engg.College Nandyal Raod, Kurnol(Dt. )Andhara pradesh,india 1 netapallyravi@gmail.com 3 amarnathjinka@yahoo.com * E.E.E.Department.J.N.T.University,Hyderabad Andhra Praddes,India tbnr@rediffmail.com Abstract This paper presents a hybrid pulse width modulation (HPWM) algorithm to reduce the steady state ripple in current. In the conventional space vector PWM (CSPWM) algorithm, the zero voltage applying time is distributed equally in every sampling interval. In order to modulate the required voltage vector it needs magnitude and angle. In the proposed HPWM algorithm, the effective time is determined using the concept of imaginary times. This PWM algorithm is designed based on the notion of stator flux ripple. Modulation index and duty cycle dependent expression for rms value of the stator flux over a sampling interval is calculated. The rms flux ripple characteristics are graphically illustrated, from which the proposed hybrid PWM algorithm is developed. The PI controllers of an indirect vector control based structure for an induction motor are replaced by fuzzy logic controller, synthesized by adaptive neural fuzzy interference system (ANFIS) method. To validate the proposed algorithm, simulation studies have been carried out and compared with PI controller based vector control of induction motor. Keywords Discontinuous PWM, Imaginary switching times, stator flux ripple, vector control I. INTRODUCTION AC motor drives are used in multitude of industrial and process applications requiring high performances. In high performance drive systems the motor speed should closely follow a specified reference trajectory regardless of any load disturbances and any model uncertainties. In order to achieve high performance, field oriented control of induction motor drive is employed. With vector control theory induction motors can be controlled like a separately excited dc motor. This theory enables the control of field and torque of the induction machine independently by manipulating the corresponding field oriented quantities [1]. The controllability of torque in an induction motor with good transient and steady state responses form the main criteria in the designing of a controller. Though, PI controller is able to achieve these but with certain drawbacks. The gains can not be increased beyond certain limit so as to have an improved response. Moreover, it introduces non linearity into the system making it more complex for analysis. Also it deteriorates the controller performance. With the advent of artificial intelligent techniques, these drawbacks can be mitigated. One such technique is the use of Fuzzy Logic in the design of controller either independently or in hybrid with PI controller[-4]. Fuzzy Logic Controller yields superior and faster control, but main design problem lies in the determination of consistent and complete rule set and shape of the membership functions. A lot of trial and error has to be carried out to obtain the desired response which is time consuming. On the other hand, ANN alone is insufficient if the training data are not enough to take care of all the operating modes. The draw-backs of Fuzzy Logic Control and Artificial Neural Network can be over come by the use of Adaptive Neuro-Fuzzy Inference System The main concept of a neuro-fuzzy network is derived from the human learning process, where an initial knowledge of a function is first setup by fuzzy rules and then the degree of function approximation is iteratively improved by the learning capabilities of the neural network. Hence ANFIS combine the learning power of neural network with knowledge representation of fuzzy logic [5-7]. In [8] an application of Adaptive Neural Fuzzy Inference System (ANFIS) on ector Control using space vectors pulse width modulation (SPWM) has been proposed. SPWM method offers 15% best bus utilization and better suited for digital implementation. In CSPWM algorithm, the voltage reference vector has been approximated by the time averaging over a subcycle of the two adjacent active states and the two zero states. Moreover, it employs equal division of zero voltage vector times within a sampling interval [9]. Then, a novel of voltage modulation technique has been proposed using the concept of effective time to reduce the computational burden involved in CSPWM. Also various discontinuous PWM (DPWM) methods have been proposed by utilizing the freedom of zero state division. The DPWM methods give less harmonic distortion at higher modulation indices compared to CSPWM and less switching losses at all modulation indices [10]. To reduce the current ripple at all modulation indices, a few hybrid PWM algorithms have been

2 16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, developed. In these algorithms, expressions for rms stator flux ripple for each sequence have been derived in [11]. Whereas [1] uses a single expression using the conventional space vector approach, which increases the complexity of the algorithm. To avoid the requirement of reference voltage vector, sector identification and angle determination, the effective time is determined using the concept of imaginary switching times in [13]. This paper presents the Adaptive Neural Fuzzy Inference Systems (ANFIS) controller and an imaginary switching times based hybrid PWM algorithm for reduced current ripple in vector control induction motor drives II. PROPOSED HYBRID PWM METHOD In the CSPWM, the reference voltage vector situated in the appropriate sector is approximated by the time averaging over a sampling interval of the two adjacent active voltage vectors and two zero voltage vectors. The switching turn-on times of the two active states and two zero states are utilized to determine the duty cycle information to program the active switching gate signals. When the inverter is operating in the linear modulation region, the sum of the times the two active states are utilized is less than the duration of the subcycle, in which case the remaining time is occupied by using the two zero states. A. Proposed Switching Sequences In the proposed algorithm, the switching times can be calculated by using the concept of imaginary switching times which uses instantaneous values of the reference voltages of a, b and c phases. This method does not depend on the magnitude of the reference voltage space vector and its relative angle with respect to the reference axis. The imaginary switching time periods proportional to the instantaneous values of the reference phase voltages are defined as [10, 13] Tan an dc Tbn bn dc (1) Tcn cn dc Here, T s is the sampling time dc is dc link voltage. When the instantaneous reference voltages are negative, then the corresponding switching times also become negative. Hence these times are called as imaginary switching times. In every sampling time, the maximum, minimum and medium values of imaginary switching times are calculated as T Max = Max( Tan, Tbn, Tcn) () T Min = Min( Tan ) (3) T Mid = Mid( Tan ) (4) Where Max, Min and Mid are the nominal values used during the sampling interval. The function Max ( Tan ) selects the maximum value among T an and Tcn. Similarly Min ( Tan ) selects the minimum value and Mid ( Tan ) selects the middle value. Finally, the active state times T 1 and T may be expressed as T1 = TMax TMid and T = TMid TMin (5) The zero voltage vector time can be calculated by using equation (6) TZ = T 1 T (6) By utilizing the freedom of zero state division, various DPWM methods can be generated. In the proposed method the zero state time will be shared between two zero states as T 0 for 0 and T 7 7 respectively, and can be expressed as T 0 = k o T z and T 7 = ( 1- ko ) Tz (7) By substituting the value of k o between 0 and 1 in (7), a number of PWM algorithms can be obtained. The CSPWM algorithm is obtained by substituting k o = 0.5.When k o = 0 any one of the phases is clamped to positive dc bus for 10 degrees over a fundamental interval and then DPWMMAX is obtained. When k o = 1 any one of the phases is clamped to negative dc bus for 10 degrees over a fundamental interval and then DPWM is obtained. Thus, in the first sector, CSPWM uses sequence, DPWMMAX uses sequence and DPWM uses sequence. Therefore, in the DPWM methods, the switching loss can be reduced by clamping one of the phases to either positive or negative dc bus at a total of 10 o over a fundamental cycle. Hence, the switching frequency of DPWM algorithms is reduced by 33% compared with CSPWM. Hence a switching frequency coefficient is introduced as defined in (8). f swcspwm k sw = (8) f swdpwm B. Analysis of Flux Ripple in a Sampling Interval The states of the inverter are switched at appropriate instants to generate the required fundamental voltage sample in average sense and not in an instantaneous fashion. The difference between applied voltage vector and reference voltage vector is the ripple voltage vector, which depends on space and modulation index. 0, 7 rz d ref r r1 Fig. 1 oltage ripple vectors and trajectory of the stator flux ripple 1 λ 0r λ 1r λ 7r q λ r

3 16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, Fig. q-axis and d-axis components of the stator flux ripple vectors Fig 1 and Fig illustrates the ripple voltage vectors and trajectory of the stator flux ripple, the corresponding d-axis and q-axis components of the stator flux ripple respectively. From figures it can be observed that the application of a zero voltage vector results in a variation of the q-axis component of the flux ripple and the application of any active voltage vector results in variation of the both the d-axis and q-axis components. Over a sampling interval, the mean square stator flux ripple can be calculated as λ q-axis ripple (rms) 1 = 3 + T0 λq 0 + [ λ + ( λ + λ ) + λ ( λ + λ )] [ ( λ + λ ) + λ ( λ + λ ) + λ ] q1 T7 + λq 7 + λd q 0 q 7 ( ) T1 + T q 0 q1 q 1 T q 7 (9) By using the above formula, the mean square flux ripple can be easily computed and graphically represented for various PWM methods C. Proposed HPWM Algorithm The mean square flux ripple characteristics can be obtained from (9) for various PWM algorithms. From fig.3, it can be observed that, at higher modulation indices DPWM algorithms give less harmonic distortion compared to CSPWM algorithm. RMS stator flux ripple d-axis ripple λ DPWM SPWM λ + λ q1 λ d λ q T 0 T 1 T T 7 DPWMMAX Alpha (degrees) Fig.3.RMS stator flux ripple of various PWM methods for k sw =1 and M i = 0.4 q1 T1 Hence, in this paper a new HPWM is proposed to reduce the current ripple at all modulation indices. The proposed HPWM algorithm consists of a set of PWM algorithms and employs the best algorithm, which gives less ripple for given modulation index. D. HPWM Based ector Control of Induction motor using ANFIS controller Fig.4 shows the block diagram of HPWM based indirect vector controlled induction motor drive with ANFIS controller. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is a class of adaptive networks that are functionally equivalent to fuzzy interference system. Field weak en ANFIS controll * i ds * i qs Slip & Angle i ds i qs P p * ds * qs -Φ To 3-Φ * ds 3-ф to i qs - Fig.4.Block diagram of proposed indirect vector control Method The ANFIS neuro-fuzzy system [6] has been used to implement the proposed model. First, it uses the training data set to build the fuzzy system in which, membership functions are adjusted using the backpropagation algorithm, allowing that the system learns with the data that it is modeling. Fig. 6 shows the network structure of the ANFIS that maps the inputs by the membership functions and their associated parameters, and so through the output membership functions and corresponding associated parameters. These will be the synaptic weights and bias, and are associated to the membership functions that are adjusted during the learning process. The computational work to obtain the parameters and their adjustments is helped by the gradient descendent technique. It is important to note that the system has a good modeling if the training set is enough representative, i.e., it has a good data distribution to make possible to interpolated all necessary values to the system's operation. * ANFIS Controller Fig.5.Logic of ANFIS controller 3- Phase Invert er As shown in fig.5 the designed ANFIS which has two inputs i.e., the actual speed and reference speed while the output is θ e H P W M S a S b S c i a i b T e I M dc

4 16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, Layer 1 Layer Layer 3 Layer 4 Layer 5 1 X N O R M A L I Z A T I O N Σ f Y Fig.6.Network structure of the ANFIS controller the torque, used to generate current i qs *. In order to generate fuzzy system fifty thousand have been considered as training data and remaining data has been considered as testing data. The currents i ds, i qs in the vector control are compared with command currents i ds * and iqs * to generate errors. From these * * errors, the voltage command signals ds, qs can be generated through PI controllers. These voltage commands are then converted into stationary frame and given to HPWM based inverter. As shown in fig. 6 the layers are defined as follows: Layer 1: Every node in this layer contains membership functions Layer : This layer chooses the minimum value of two input weights Layer 3: Every node of these layers calculates the weight, which is normalized. Layer 4: This layer includes linear functions, which are functions of the input signals. Layer 5: This layer sums all the incoming signals III. SIMULATION RESULTS MATLAB/Simulink based simulation studies are carried out to predict the performance of the proposed HPWM based vector control of induction motor drive. The motor is a squirrel-cage motor with power 1.5kW, stator resistor 4.1 Ω,stator leakage inductance 0.545mH, rotor resistance.5 Ω,rotor leakage inductance 0.54mH, mutual inductance 0.510mH, inertia 0.04 Kg.m, pair of poles is, dc voltage 780.The simulation results are shown in Fig 7-Fig 15. From the simulation results it can be observed that the ripples in currents and torque are less in proposed HPWM algorithm based drive compared to that of CSPWM based drive. Also, it can be observed that the proposed HPWM algorithm gives less harmonic distortion compared with CSPWM algorithm at a given modulation indices. I. CONCLUSIONS In this paper, a new controller for Hybrid PWM based vector controlled induction motor drives using ANFIS has been introduced. This method has a backpropagation based neurofuzzy structure with five layers. This model was simulated

5 16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, using set of data obtained by normal simulations. The generated data are reference speed and actual speed pattern which are considered as inputs and their corresponding torque as output of the ANFIS model. The results obtained by this ANFIS model shown in this paper are more accurate and this approach has solved the problem of the complexity. From the simulation results it can be seen that the proposed controller has been implemented successfully. The ripples in current and torque are high for CSPWM. Hence to overcome this problem, in this paper, switching sequences are developed based on the imaginary times which do not require sector and angle information and then the expression for rms ripple over a subcycle are expressed as function of reference voltage vector, imaginary switching times and sampling time based on the notion of stator flux ripple, which gives less distortion and is applied in every sampling interval Fig. 9 Starting transients of proposed ANFIS controller based HPWM based vector controlled induction motor drive Fig.7 Steady state plot of proposed ANFIS controller SPWM based vector controlled induction motor Fig. 10 Steady state plot of proposed ANFIS controller HPWM based vector controlled induction motor Fig. 8 Currents i qs, i ds during Steady state for proposed ANFIS controller SPWM based vector controlled drive Fig. 11 Currents i qs, i ds during Steady state for proposed ANFIS controller HPWM based vector controlled drive

6 16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, Fig. 1 Transients during step change in load for proposed ANFIS Controller HPWM based vector controlled induction motor Fig. 13 Currents i qs, i ds during step change in load for proposed HPWM based vector controlled induction motor Fig. 14 Transients during speed reversal +100 r.p.m. to -100r.p.m for proposed ANFIS Controller HPWM based vector controlled induction motor Fig. 15 Transients during speed reversal-100 r.p.m. to +100r.p.m for proposed ANFIS Controller HPWM based vector controlled induction motor REFERENCES [1] F. Blaschke The principle of field orientation as applied to the new transvector closed loop control system for rotating-field machines," Siemens Review, 197, pp [] M. Nasir Uddin,, Tawfik S. Radwan,, and M. Azizur Rahman Performances of Fuzzy- Logic-Based Indirect ector Control for Induction Motor Drive IEEE Transactions on Industry applications,vol.38,no.5,september/october, 00 [3] Satean Tunyasrirut, Tianchai Suksri, and Sompong Srilad Fuzzy Logic Control for a Speed Control of Induction Motor using Space ector Pulse Width Modulation World Academy of Science, Engineering and Technology 5 007S. [4] M.Strefezza and Y.Dote, "Fuzzy and neural networks controller", in Proc. IEEE- IECON, pp , 1991 [5] G. Liu, Z. Hu, Y. Shen, H. Zhou and C. Teng, Estimation of induction motor speed based on artificial neural networks inversion system, IEEE Int. Conf. on Neural Networks & Signal Processing, China, June 8-10, 008, pp [6] J. Jang. ANFIS: Adaptive-Network-Based Fuzzy Inference System leee Trans. Man and Cybernetics Systems. ol.3, No.3 pp , May/June1993 [7] R. A. Gupta, Rajesh Kumar, Rajesh S. Surjuse, ANFIS Based Intelligent Control of ector Controlled Induction Motor Drive Second International Conference on Emerging Trends in Engineering and Technology, ICETET-09 [8] Pedro Ponce Cmz, Joel Morulrtes Aquino, Maya Reyes EIizondo ector Control using ANFIS Controller with Space ector Modulation Universities Power EngineeringConference 004, 39 International,pp ,vol [9] Heinz Willi ander Broeck, Hnas-Christoph Skudelny and Georg iktor Stanke, Analysis and realization of a pulsewidth modulator based on voltage space vectors IEEE Trans. Ind. Applicat., vol. 4, no. 1, Jan/Feb 1988, pp [10] Dae-Woong Chung, Joohn-Sheok Kim and Seung-Ki Sul, Unified voltage modulation technique for real-time three-phase power conversion IEEE Trans. Ind. Applicat., vol. 34, no., Mar/Apr 1998, pp [11] G.Narayanan, Di Zhao, H. Krishnamurthy and Rajapandian Ayyanar, Space vector based hybrid techniques for reduced current ripple IEEE Trans. Ind. Applic., ol.55, No.4, pp , April 008. [1] Ahmet M. Hava, Russel J. Kerkman and Thomas A. Lipo, Simple analytical and graphical methods for carrier-based PWM-SI drives IEEE Trans. Power Electron. vol. 14, no. 1, Jan 1999, pp [13] T. Brahmananda Reddy, J. Amarnath and D. Subbarayudu, Improvement of DTC performance by using hybrid space vector Pulsewidth modulation algorithm International Review of Electrical Engineering, ol.4, no., pp , Jul-Aug, 007