MOTOR DRIVES - TURNITIN - DESIGN OF GAME APPLICATION KLUNG YOUR BEAT!!

Wahyu Mulyo Utomo¹, Sy Yi Sim¹

1Electrical Power Department, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.

E-Mail: [email protected], [email protected]

Abstract - Typical DTC IM drive has good efficiencies while operating at its rated condition, i.e., rated speed and rated torque. Nevertheless, most of the time a motor drive operates far from its rated operating point, which impairs the efficiency. Therefore, DTC should be associated with a loss minimization strategy to maximize the drive efficiency for a wider range of operation. Consequently, this paper proposed an online learning Artificial Neural Network (ANN) optimum flux controller with the objective to generate an adaptive flux level to optimize the efficiency of any different operating points, especially at low speed and low torque condition. The proposed controller uses the input power of the drive system as the objective function and minimizes it. The description of neural network control system with the training procedure is given in this paper. The simulation results show the effectiveness of the proposed method.

Index Terms - Loss Minimization Adaptive Flux Controller Direct Torque Control Artificial Neural Network Induction Motor.

I. INTRODUCTION

In recent past, an innovative control method namely DTC has gained the attraction [1]. The use of DTC strategies has become more universal and popular for induction motor drives and seems has a very rapid growth in the development of it. DTC can be considered as a simpler alternative to the FOC technique, where both provide an effective control of the flux and torque but unlike the traditional FOC, DTC enables both quick and precise torque response excluding the inner current regulation loop and complex field-oriented block, less parameter dependence and increase the precision and the dynamic of flux and torque response [2-3]. Hence, DTC is much simpler.

A neural network based speed controller of SVPWM- DTC was implemented to replace the conventional PID controller in order to overcome the common problems of conventional DTC such as overshoot during start up and a poor load disturbance rejection in permanent mode [4]. The ANN is chosen based on its several advantages over the others as ANNs are excellent estimator in nonlinear systems, insensitivity to the distortion of network inexact input data and its

simple architecture[5]. The neural network is trained in the supervised learning mode by using the back propagation algorithm. A better performance of transient response by reduce the overshoot and torque ripple is obtaned.

The increasing emphasis on energy saving is highlighting the importance of attaining higher motor effiviency under all operating conditions especially for the industrial application. Intentionally, a control algorithm that manage to reduce the losses to maximize the efficiency of the IM drives is highly attractive. It is commonly know that the induction motor have good efficiencies while operating at full load. However, at lighter load, which is a condition that many machines experience for a significant portion of their service life, the efficiency decreases to a large extent. It is therefore important to maximize the efficiency of motor drive system while operating in adjustable speed applications. It is well known that the efficiency of an IM drives can be improved by reducing the flux level when it operates under light load conditions. However, the challenge is to be able to predict the extent to which the flux can be reduced, at any operating point over the complete torque and speed range, which will maximize the efficiency.

This paper concern on the objective to determine the most suitable flux value for a DTC fed IM drives for maximization especially under part-load operation.

An ANN based optimum flux predictor is presented.

The speed and load torque are used as inputs for the neural network while the producing optimum flux is taken as neural network output. The proposed control algorithm will be simulated in Matlab/Simulink package to verify the proposed algorithm.

II. DTCCONTROL A. Model of Induction Motor

The induction motor is an a.c. machine where the armature winding on the stator and the field is winding on the rotor. Few assumptions should be pertinent before the mathematical model of IM is design.

• The stator and rotor resistance (Rs,Rr) is used to represent the stator and rotor winding appropriately.

• The stator and rotor inductance (Ls,Lr) is used to represent the flux in stator and rotor winding appropriately.

• The mutual inductance (Lm) is used to represent the interaction between stator and rotor fluxes.

• The air gap is consistently distributed between stator and rotor and hence the windings are sinusoidal distribution.

• The motor is at steady state constant speed, ( ωr=0)

• The motor is initially relaxed.

The induction motor can be derived to difference reference frames with respects to its stator, rotor, or its synchronous speed. Particularly, choosing the difference reference frames yields the different model of IM. All motor parameter such as voltage, current, and flux must be refer to the preferred reference frame for modeling[6]. The difference modeling frames are one of the primary divergences between FOC and DTC. The motor is refer to synchronously reference frame for FOC, ie ω=ωe while DTC is in stationary reference frames, i.e, ω=0.

By considering, the stationary reference frames fixed on the stator, the mathematical equation of the IM can be written as follow[7],[8]:

The voltage equation,

qs s qs e ds

V R i d dt

  

   (1)

ds s ds ds e qs

V R i d dt

  

   (2)

( ) qr

qr r qr e r dr

V R i d dt

   

    (3)

( )

dr r dr dr e r qr

V R i d dt

   

    (4)

Where, V_qr,V_dr0 The flux equation,

( )

qs L il s qs Lm iqs iqr

    (5)

( )

qr L il r qr Lm iqs iqr

    (6)

( )

ds L il s ds Lm ids idr

    (7)

( )

dr L il r dr Lm ids idr

    (8)

The electromagnetic torque equation is

3 ( )

e dr qs qr ds

T PL i i

L  _

 (9)

The q component of rotor field _qr would be zero if the VC control is fulfilled. The

electromagnetic torque will be controlled only by the q-axis stator current and it can be express as follow:

³ ( )

e dr qs

T PL i

L 

 (10)

The parameters for the motor are given as Table-1 Table-1. Motor Parameter

Motor Parameter Value

Frequency, f 50 Hz

Pole, p 2

Stator Resistance, Rs 8.2Ω Rotor Resistances, Rr 5.3667Ω Stator Self Inductances, Ls 0.4934H Rotor Self Inductances, Lr 0.4934H Mutual inductances , Lm 0.4867H

B. DTC Scheme and Principle

DTC was introduced in the middle of 80‘s and consider as an alternative technique to the FOC, which provide a fast and good dynamic torque response. The DTC scheme is very simple in its basic, which consists of DTC controller, torque and flux calculator, and voltage source inverter (VSI).

The control philosophy of DTC is to directly control the inverter state so as to maintain the stator flux and torque within hysteresis band limits which requires no current regulator loops while the similar performance of the FOC can achieve at the same time or much better.

As shown in Figure-1, the conventional DTC scheme for an induction motor consists of two loops, the magnitude of stator flux and the torque respectively. The three level hysteresis comparator take the output error between the estimated torque T and the reference torque Tref while the error from the estimated stator flux Ψ and reference flux magnitude Ψref is feed into the two level hysteresis comparator[9]. The estimated values are calculated by means of the adaptive motor model[10]. Hereafter, the position of the stator flux in sync with the resulting torque error and flux error of the hysteresis

19 block are used as the input for the selection table. The position of the stator flux is divided into six different sections. The decent voltage vector is select based on selection table. The selection table blocks are responsible for proper inverter switching state selection at each sampling time in order to confirm the torque and flux error lays within hysteresis band[7].

Figure-1. Basic DTC scheme with the hysteresis controller.

As a matter of facts, the fundamental concept of DTC is to produce the appetence torque by diametrically maneuver the stator flux vector. The instantaneous value of the stator flux and torque are calculated from the stator variable by utilizing a close loop torque and flux calculator. By select the appropriate inverter state, independent and direct control of stator flux and torque can be achieved.

Meanwhile, the stator voltage and currents are indirectly controlled, therefore, currents feedback loops are unnecessary[11]. The major problem in conventional hysteresis-based DTC was the high torque ripple, stator current distortion in term of low order harmonics and variable switching frequency due to its hysteresis comparators[12].

A variety method has been proposed to overcome the issues as mentioned. The Space Vector Modulation Technique (SVM) was one of the enticing candidates. The space vector depends on the reference torque and flux is use to solve this problem.

The reference voltage vector is then realized using a voltage vector modulator. In conventional hysteresis based DTC systems, the next switching condition of the VSI is generated directly from the torque error and flux error. Nonetheless, the SVM-DTC import a constant switching frequency signal to the VSI by the stator reference voltage vectors which produce from the flux and torque error [13]. By implementing the SVM technique, it is not only maintain the merits of the DTC but also make it more reliable and enhance the performance of the drive system for all speed range[14].

III. ADAPTIVEFLUXCONTROLLER ALGORITHM

Basically, this method uses the input power of drive system as the objective function and minimizes it. Figure-2 shows the proposed optimum

flux controller imposed on the SVPWM DTC scheme. To apply the ANN based optimum flux predictor, the input power of motor has been calculated by equation (11).

P_inV_dI_dV_qI_q (11)

Figure-2. Block diagram of the proposed ANN based optimum flux DTC

Among various kinds of ANNs that available, the most prevalent configuration appears as the feedforward network trained by the error back propagation algorithm for the engineering application. The feedforward neural networks indicate that a set of input data is supplied to the input units and the information is transferred to the output units through the hidden layers in a forward direction without fed back to earlier layers. The feedforward neural network are basically layers of neurons connected in cascade where the internal structure of a neuron consist of a summer to summing its weighted input signal with the bias and an activation function as shown in Figure-3. The neuron output is calculated as:

^a^^{f wp}



^^b



(12)

Figure-3. The neuron structure.

Where p is the input, w is the corresponding weight while b is the bias. The summer output n, often refer as the net input that feed to a transfer function f, which produces the scalar neuron output, a.

∑ a

Inputs General Neuron

p w Hysteresis

Comparator Hysteresis Comparator

Voltage Source Inverter

(VSI)

Stator Flux and Torque Estimator T

T_err

θ T_ref*

Ѱ_ref

Ѱ Ѱ_err

S_a

Voltage IM vector Selector

V S_b S_c

0 0

Torque Controller

Flux Controller

SVPWM

IM Flux and

Torque Estimator T

Terr

ωref

ωerr Tref

Ѱ Ѱerr

VDC

V_ds

V_qs

2ϕ 3ϕ

I_abc I_ds

I_qs ANN

Optimum Flux Controller Pin_ref

Pin Pin_err

Voltage Source Inverter Speed

controller Torque

Controller Flux Controller

SVPWM

Flux and Torque Estimator T

T_err ω_ref

ω ω_err

T_ref

Ѱ Ѱ_err

V_DC V_ds

V_qs

2ϕ 3ϕ

I_abc I_ds

I_qs ANN

Optimum Flux Controller P_{in_ref}

P_in P_{in_err}

Voltage Source Inverter

(VSI) Speed

controller

20 The feedforward network is composed by the input layer, output layer, and the hidden layers.

The hidden layers helps to associate the input and output layers, it can be one or several layers depending on the system complexity. Although a single hidden layer is sufficient to solve any function approximation problems, but a more complex problem will be easier to solve by a net with two or more hidden layers. In a multilayer neural network with two hidden layers, the first hidden layers often serves to partition the input space into regions and the units in the second hidden layer represent a cluster of points. According to the property of the multilayer feedforward network, this paper utilizes the three layers feed forward network. Basically, the numbers of input and output neuron at each layer are equal to the number of input and output signals of the system respectively. A multilayer perceptron network is implemented in this paper for the speed controller.

The structure of the proposed ANN DTC is shown in Figure-4.

ai m

wij ^m

f f

a Ʃ

Ʃ Ʃ Ʃ Ʃ

Figure-4. The architecture of proposed back propogation ANN Flux controller.

The controller consists of input layer, hidden layer and output layer. Based on number of the neuron in the layers, the ANN DTC is defined as a 1- 5-1 network structure. The first neuron of the output layer is used as a torque reference signal (a²1=mf).

The connections weight parameter between j^th and i^th neuron at m^th layer is given by w^mij, while bias parameter of this layer at i^th neuron is given by b^mi. Transfer function of the network at i^th neuron in m^th layer is defined by:

 







1  1

1 Sm

m i m j m ij m

i w a b

(13) The output function of neuron at m^th layer is given by:

) ( _i^m

m m

i f n

a 

(14) Where f is the activation function of the neuron. In this design the activation function of the output layer is unity and for the hidden layer is a tangent hyperbolic function given by:

1 1

) 2

( 2 

 

 n_i^m m

i m

e n

(15) Updating of the connection weight and bias parameters are given by:

m ij m

ij m

ij w

k k F

w k

w 

 



 ( )

) ( )

( 

(16)

m i m

i m

i b

k k F

b k

b 

 



 ( )

) ( ) 1

( 

(17) where k is sampling time, α is learning rate, and F performance index function of the network. The next section will be explaining the details of offline and online learning NN that used for the simulation testing.

The process to select and update the appropriate weights of the network in order to achieve the desired input or output relationship is known as training the network. The ability to learn by the training data to improve and achieve the require system performance is one of the most important features in ANN. Typically, the weight updating can be done by two primary ways, namely the online and offline learning. The general approach for both learning schemes are the same, the difference is that, for offline learning, the weight are updated after summing all training example, while in online training, the parameter are updated every time after a new training example is done[15]. In this paper, back propagation learning algorithm is implemented where it is also the most famous learning algorithm in the supervised learning mode.

The back-propogation looks for the minimum of the error function difference between target output and actual output for all given training patterns by adjust the weight in all connecting links and thresholds in the nodes using the method of gradient descent. The combination of weights which minimizes the error function is considered to be a solution of learning problem. The backpropagation algorithm should adjust the network parameter in order to minimize the mean square error as calculated by:

 

iei k k

F ( )

2 ) 1

( ² (18)

21 )

( ) ( )

(k t k a k

e_i  _i  _i (19) where ti is target signal and ai output signal on last layer.

By implementing the chain rule, the gradient descent of the performance index against to the connection weight is given by:

m i

m m m

ij i ij

F F n

w n w



  

   (20)

The sensitivity parameter of the network is defined as:

i m

s F n

 

 (21)

m i

i m m

i i

a s F

a n

  

  (22) Gradient the transfer function again to the connection weight parameter is given by:

1 m

m i

m i ij

n a

  

 (23)

Finally, by substitution equation (21) and (23) into (16) the updating connection parameter for weights and bias is given by:

) ( ) ( ) ( ) 1

( ¹

1 k w k s k a k

w_ij^m^   _i^m^ⁱ _i^m _i^m^ (24)

1( 1) ( ) ( )

m m i m

i i i

b ^ k b ^ k s k (25)

IV.RESULTSANDDISCUSSION

The proposed model has been developed by Matlab/Simulink. The simulation block diagram for the proposed IM drives is shown in Figure-5.

Figure-5. The Simulink block diagram of the proposed LM SVM-DTC.

The proposed adaptive flux is adopted before the DTC system in order to generate the appropriate flux reference according to a different speed and torque level instead of the conventional constant flux value. In order to verify the validity of the proposed ANNEO DTC, the efficiency is obtained with and without the ANNEO controller for a variety of speed and torque. Different operating speed is tested, which is 150 rad/s, 120 rad/s, 90 rad/s and 60 rad/s. Corresponding to each speed, different torque value will be tested, which is 0.2 Nm, 0.4 Nm, 0.7 Nm and 0.9 Nm, where the rated torque is 1 Nm for this testing three phase's induction motor.

To verify the simulation result in real time, the proposed control method has been applied to an experiment setup for real time testing. The experiment setup, shown in Figure 6 consists a three phases insulated gate bipolar transistor (IGBT) based inverter to fed the induction motor and controlled through the dSPACE DS1103 Real-time Digital Signal Controller. The entired control system is programmed in C language by the Matlab 2013 and interfaced by the ControlDesk 5.1. The digital control unit and current sensor is used to measure the feedback signal.

Induction Motor sv pwm

SV PWM rated flux

0.9

load

Speed Controller Speed Controller Reference

Speed (rad/s)

Loss Minimization Proposed

ANN Optimum Flux

Controller

Induction Motor [Pin _ref]

[Pin ]

DTC System DTC

Flux,Torque Estimator

International Conference on Vocational Education and Electrical Engineering (ICVEE) 2015

22 Figure 6: Hardware prototype of the proposed of the proposed EO SVPWM-DTC.

The ANNEO controller is expected to be adaptive and produce the optimal flux level corresponding to variance speed under difference torque in order to attain its maximum efficiency, specifically at low speed and low torque operation.

The efficiency for each speed, resultant by different torque condition is shown in Figure-7, while the efficiency for each torque corresponding with the different speed operation is shown in Figure-8. The resulting adaptive flux level from the ANNEO for each testing operation condition is in Table-2, instead of the constant 0.9 as the reference flux.

A significant amount of efficiency improvement has been achieved by the optimal flux level determined by the ANNEO, compared with the constant rated flux value. As show in Figure-7 and Figure-8, a reasonable amount or efficiency has been increased specially at light load. At the low load operation, in both high speed and low speed, the efficiency varies from 52% to 65 % and 30 % to 42%

accordingly when the suitable flux value implemented as a reference value yields by the proposed ANNEO controller.

Figure-7. The efficiency for constant flux value and optimal flux value for the speed of 150 rad/s, 120 rad/s, 90 rad/s and 60 rad/s corresponding with different torque value.

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 150rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 120rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 90rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 60rad/s optimum flux

rated flux

optimum flux rated flux

optimum flux rated flux optimum flux rated flux

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 150rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 120rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 90rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 60rad/s optimum flux

rated flux

optimum flux rated flux

optimum flux rated flux optimum flux rated flux

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 150rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 120rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 90rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 60rad/s optimum flux

rated flux

optimum flux rated flux

optimum flux rated flux optimum flux rated flux

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60

Torque (Nm)

Efficiency (%)

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60

Torque (Nm)

Efficiency (%)

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 90rad/s

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

30 40 50 60 70 80

Torque (Nm)

Efficiency (%)

Speed = 60rad/s optimum flux

rated flux

optimum flux rated flux

optimum flux rated flux optimum flux rated flux

60 70 80 90 100 110 120 130 140 150

30 40 50 60 70 80

Torque = 0.2 Nm

Speed (rad/s)

Efficiency (%)

60 70 80 90 100 110 120 130 140 150

30 40 50 60 70 80

Torque = 0.4 Nm

Speed (rad/s)

Efficiency (%)

60 70 80 90 100 110 120 130 140 150

30 40 50 60 70 80

Torque = 0.7 Nm

Speed (rad/s)

Efficiency (%)

60 70 80 90 100 110 120 130 140 150

30 40 50 60 70 80

Torque = 0.9 Nm

Speed (rad/s)

Efficiency (%)

optimum flux rated flux

roptimum flux rated flux

optimum flux rated flux

60 70 80 90 100 110 120 130 140 150

30 40 50 60 70 80

Torque = 0.2 Nm

Speed (rad/s)

Efficiency (%)

60 70 80 90 100 110 120 130 140 150

30 40 50 60 70 80

Torque = 0.4 Nm

Speed (rad/s)

Efficiency (%)

60 70 80 90 100 110 120 130 140 150

30 40 50 60 70 80

Torque = 0.7 Nm

Speed (rad/s)

Efficiency (%)

60 70 80 90 100 110 120 130 140 150

30 40 50 60 70 80

Torque = 0.9 Nm

Speed (rad/s)

Efficiency (%)

optimum flux rated flux

roptimum flux rated flux

optimum flux rated flux

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