- Open Access
- Total Downloads : 19
- Authors : K.Rameshkumar, A.Sakthivel, P.Vijayakumar
- Paper ID : IJERTCONV2IS06010
- Volume & Issue : RTIA – 2014 (Volume 2 – Issue 06)
- Published (First Online): 30-07-2018
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
Predictive Current Control Strategy for Voltage Source Inverter
Predictive Current Control Strategy for Voltage Source Inverter
ameshkumar1, A.Sakthivel2 ,Member, IEEE, A.Senthilkumar4, Member, IEEE
1, 2, 4 Department of Electrical and Electronics Engineering, Dr.Mahalingam College of Engineering and Technology, Pollachi, India
1 email@example.com, firstname.lastname@example.org,
AbstractWhile the classical control techniques for three- phase two-level three-leg inverters are based on pulse width modulation or 3-D space vector modulation, this paper presents a Finite Control Set Model Predictive Control (FCS- MPC) strategy for a two-level three-leg voltage source inverter with resistive- inductive load. The Model Predictive Control method chooses a switching state that minimizes the error between the output currents and their references. Firstly the performance of the proposed predictive control method is compared with pulse width modulation control. Secondly the performance of controller is analyzed with various conditions is carried out. The proposed controller offers excellent reference tracking with less current harmonic distortion for all conditions. The proposed system's performance is investigated using a MATLAB simulation model.
Keywords Model predictive control (MPC), voltage source inverter, Current control, Pulsewidth modulation (PWM)
Voltage source converters have been extensively studied in the last decades in most industrial sectors for many applications. By considering the increasing energy demands and power quality and efficiency, a control and power conversion using power electronics have become an important topic today. Nowadays, MPC control scheme has been applied for current control of Active-Front-End Rectifier,, Distributed Generation Systems, Active Filters and Power Conditioning,,, Non-
3 Department of Electronics and Communication Engineering, Kathir College of Engineering, Coimbatore, India
powerful and robustness of the proposed control method are evaluated through simulations results. This paper is organized as follows. In Section II, the mathematical model of the converter-load system is presented, followed by the explanation of the proposed control strategy in Section III. In Section IV, simulation results are presented. Finally in Section V appropriate conclusions are drawn.
POWER CONVERTER MODEL
Fig. 1. Voltage source inverter power circuit.
A. Voltage Source Inverter Model
The power circuit of the converter considered in this work is shown in Fig. 1. It has been selected for a clear analysis of a predictive control strategy with RL-Load. It is a three leg two level inverter operated by switching S1, S2
,S3, S4, S5and S6.The inverter consisting of two pairs of complementary controlled switches in each leg(S1, S4), (S2, S5) and (S3, S6). The switching states of converter are determined by the gating signals Sa, Sb, and Sc as follows:
Conventional Renewable Energy,, uninterruptible
S 1 if S1 on
S 4 off
power supplies (UPS), drives, and power factor correction . This control scheme predicts the future load
S 4 on
current behavior for each valid switching state of the converter, in terms of the measured load current and predicted load voltages.
The predictions are evaluated with a cost function that minimizes the error between the predicted currents and their
1 if S on
0 if S 2 off
1 if S 3 on
S 5 off
S 5 on
S 6 off
references at the end of each sampling period.This has been applied for the controlling of power converters due to the advantages, like fast dynamic response, easy inclusion of
if S 3
S 6 on
nonlinearities and constraints of the system, and the flexibility to include other system requirements in the
and it can be expressed in vectorial form by
controller ,.The classical current control techniques for a three leg two level VSI use PI controller and a modulation stage (PWM or SVM) to generate the gating signals. In the FCS-MPC takes the advantages of direct application of the control action to the converter without using modulator
where a e j 2 / 3
(Sa aSb a
stage. Compared with the Classic Linear PI-PWM the MPC offers manyadvantages such as good reference tracking and minimum output distortion ,,,.In this paper, the
The output voltages space vectors generated by the
inverter are defined by
Fig.3.Model predictive current control block diagram.
v Ri L di
v (VaN VbN VcN ) 3
where R is the load resistance L is the load inductance, v
where N , N and N are the phase to neutral voltages of the inverter . Then the load voltage vector V can be related to the switching state vector S by
is the voltage generated by the inverter.
MODEL PREDICTIVE CURRENT CONTROL
v Vdc S
v 2 V (Sa aSb a 2
The Control Strategy
The proposed predictive current control scheme is shown in Fig. 3.It uses the system model to predict the future behavior of the variables to be controlled. The quality function or cost function or error between the reference and predicted values is calculated. The switching state that minimizes g is selected and applied during the next sampling
v 2 V (Si [1 a
i 3 dc
a 2 ])
It consisting of five main steps as follows:
is the voltage vector generated by the switching
Measurements:The predictive model requires supply voltage and load currents at instant of k. In this system
states Si with = 0, 7.
By evaluating each of the switching states in (8), eight voltage vectors ( v0 v7 ) can be generated by the inverter resulted in only seven different voltage vectors because v0 and v7 produce the same zero voltage vector , that means a three-phase two-level voltage source converter can deliver
only 7 different voltage vectors, although there are 8
different switching combinations, as it can be seen in Fig. 2.
supply voltage is known and constant, when we go for other applications, supply voltage measurement is needed example APF. For this reason one voltage sensor and three current sensors is needed.
References calculation:Based upon on the application the current references are generated. In this system a simple modelling and control the inverter. So that the references are user defined. By changing the reference it can be used for any applications.
Extrapolation:For sufficiently small sampling time,
is less than 20Âµs no extrapolation is needed. In
that case take approximation as
i* (k 1) i* (k)
When sampling time
TS is greater than 20Âµs the
following fourth-order extrapolation can be used:
i* (k 1) 4i* (k) 6i* (k 1) 4i* (k 2) i* (k 3)
0 0 0
PredictiveModel: A discrete-time form of the load current
p>Fig. 2. Voltage vectors generated by the inverter.
for a sampling time
TS can be used to predict the future
In a balanced three-phase load, the current can be defined as a space vector by
value of load current by using measurement of load current and supply voltage at the sampling instant k.
Approximating the derivative by
i 3 (ia aib a
di i(k) i(k 1)
The load current dynamics can be expressed by vector equation
Substituting equation (13) in equation (10) the following expression as
i(k) i(k 1)
B. MPC Algorithm
v Ri L
Then the load current at instant k as
In general, the control algorithm can be summarized to the following steps .
Measure the load currents.
Predict the load currents for the next sampling instant
L [Li(k 1) TS v(k)]
for all the possible switching states.
Evaluate the cost function for each prediction.
Shifting the discrete-time one step forward in the future load current can be determined by
Optimal switching state is selected which minimizes thecost function.
RTS L[Li(k) TS v(k 1)]
Apply the new switching state.
The optimal voltage vector is selected which minimizes the cost function and the switching state associated to the
where R and L are the load resistance and inductance,
respectively is the sampling time, i(k) is the measured load current, and v(k+1) is the inverter predicted voltage is the decision variable to be calculated by the controller.
5) Cost function or quality function optimization:The error between the reference current and the measured load current at the next sampling instant can be expressed as follows
selected voltage vector is set to the gating signals.
In this simulation two types of cases are considered. In the first case the Inverter controlled by the two different current control methods have been carried out, and in second case using Model Predictive Current Control method the simulations are carried out during non-
g i* (k 1) i(k 1)
sinusoidal reference and input frequency variation in order to assess the performance of the proposed predictive
where, i* (k 1) is the reference current vector and i(k 1) is predictive load current vector. Furthermore, (17) can be expressed in stationary frame as follows
method.The Simulations are carried out using MATLAB/Simulink. The fig 4 and 5 denoting the matlab simulink model of the PI-PWM and MPC controller based voltage source inverter
g i (k 1) i
where i (k 1) and
(k i (k 1) i (k (18)
i (k 1) are the real and imaginary
Comparison with PI-PWM Control
A comparison of the proposed predictive current control with Classic Linear PI-PWMcontrol is presented in Figs. 6
parts of the predicted current vector and i * (k 1) ,
i * (k 1) are the real and imaginary parts of the reference
current vector respectively. In this work, the absolute error is used for computational simplicity. Other quality functions such as error squared could also to be used that can be expressed as follows
and 7. Here, the amplitude of reference current is reduced from 13 A to 5.2 A at instant 0.015 (s),while keeping the amplitude current fixed. This is done to assess the decoupling capability of the current control loop. PI with PWM current control, shown in Fig. 6(a), presents slower dynamic response and some noticeable coupling effects between and.In the response of the proposed predictive current control, for the same test, is shown in Fig.7(a).Its
* (k 1) i
(k 1))2 (i
* (k 1) i
dynamic response is as fast than linear PI-PWM and no coupling effects between and .In Fig. 6(b) and Fig. 7(b) denoting the corresponding load voltages.
Finally the corresponding switching state is given to the
Fig . 4. Inverter controlled by PI-PWM controller- Simulink diagram.
Fig. 5.Inverter controlled by MPC controller- Simulink diagram.
Table.1 Simulation Parameters
Supply voltage Resistance Inductance
PWM carrier frequency
Sampling time Reference current
0.5 ohm 10mH
Fig. 6.Classic Linear PI-PWM step on . a) Ref, Load and ) currents. b) load voltage.
Fig. 7. MPC step on . a) Ref, Load ( and .) currents. b) load voltage.
Performance of MPC for Various Conditions
Analysis with input frequency variations
In this analysis the Reference frequency F = (50-20- 70)Hz , Ref current (Ia,Ib,Ic) =13A and load Ra = Rb = Rc
= 0.5ohm ; La = Lb = Lc = 10mH;
Fig.8 Simulation result with input frequency variations
In the fig.8 shows that good reference tracking with frequency variations and fast response is observed.
Analysis with Non sinusoidal reference
In this the seventh harmonic reference with amplitude and frequency are 10 A at 50 Hz, respectively. The loads are the same as of the input frequency variations earlier.
Fig .9 .Simulation results with seventh harmonic injected sinusoidal reference currents for Ts= 20s
The results is indicated in Fig.9 where a good tracking of the load current to its reference is observed, which demonstrates that this control strategy can be applied effectively in a two-level three-leg converter operating as an active lter.
In this paper the FS-MPC for two-level voltage source inverters were studied. The control technique does not need to use modulator. The control algorithm has been evaluated with two different cases through simulation results. It has been noticed that the control algorithm provides very good current tracking behavior. First of all, when the step change in the amplitude of the reference, the simulation results shows that the predictive control method has fast dynamic response with inherent decoupling between iand i. Secondly, the simulation results show the good performances of the current tracking ability in various conditions such as input frequency variations and non- sinusoidal references.
Finally the Simulation results show that FS-MPC strategy gives very good performance under these conditions. In further research on predictive control is to analyze the performance of various condition such as load variations and sampling frequency variations.
S. Kouro, P. Cortes, R. Vargas, U. Ammann, and J. Rodriguez,
Model predictive control-a simple andpowerful method to control power converters, IEEE Trans. Ind. Electron., vol. 56, no. 6, pp. 18261838,Jun.2009
J. Rodriguez, J. Pontt, C. A. Silva, P. Correa, P. Lezana, P. Cortes, and U. Ammann, Predictive current control of a voltage source inverter, IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 495503, Jan. 2007.
R.Kennel and A. Linder, Predictive control of inverter suppliedelectrical drives, in Proc. Conf. RecordPower Electronics Specialists,Galway, Ireland, Jun. 2000, pp. 761766.
P.Cortes, J.Rodriguez, S.Vazquez, and L. and G.Franquelo,Predictive control of a three-phase UPS inverter usingtwo steps prediction horizon, in Proc. IEEE Int. Ind. Technol. (ICIT) Conf., 2010, pp. 12831288.
VenkataYaramasu, Marco Rivera, and Jose Rodriguez, Model Predictive Current Control of wo Level Four-Leg Inverters Part I: Concept, Algorithm,and Simulation Analysis IEEE Trans.
Power Electronics, vol. 28, no. 7, July 2013
Jose Rodriguez, Marian P. Kazmierkowski andChristian A. Rojas,
State of the Art of Finite ControlSetModel PredictiveControl in Power ElectronicsIEEE Trans. Ind. Informatic,vol. 9, no. 2, May 2013
Cortes.P, M.P. Kazmierkowski, R.M. Kennel, D.E. Quevedo and J. Rodriguez Predictive control in power electronics and drives IEEE Trans. Ind. Electron, vol. 55, no. 12 December 2008.
PabloAcuna, Luis Moran, Marco Rivera, Juan Dixon, andJose Rodriguez, Improved Active Power Filter Performance for Renewable Power Generation Systems IEEE Trans. Power Electronics, vol. 29, no. 2, February 2014
P. Mattavelli, G. Spiazzi, and P. Tenti, Predictive digital control of power factor preregulators with input voltage estimation using disturbance observers, IEEE Trans. Power Electronic., vol. 20, no. 1, pp. 140147,Jan. 2005.
J. Rodriguez, J. Pontt, P. Correa, U. Ammann, and P. Cortes, Novel control strategy of an AC/DC/AC converter using power relations, in Proc. Int. Conf. PELINCEC, Warsaw, Poland, Oct. 1619, 2005
J. Rodriguez, J. Pontt, P. Correa, P. Lezana, and P. Cortes, Predictive power control of an AC/DC/AC converter, in Conf. Rec. IEEE IAS Annu.Meeting, Oct. 2005, vol. 2, pp. 934939.
H. Miranda, R. Teodorescu, P. Rodriguez, and L. Helle, Model predictive current control for high-power grid-connected converters with output LCL filter, in Proc. 2009 35th Annu. Conf. of IEEE Ind. Electron.,Nov. 2009, pp. 633638.
J. D. Barros and J. F. Silva, Optimal predictive control of three- phase NPC multilevel converter for power quality applications, IEEE Trans. Ind. Electron ,vol. 55, no. 10, pp. 36703681, Oct. 2008.
F. Defay, A. M. Llor, and M. Fadel, A predictive control with flying capacitor balancing of a multicell active power filter, IEEE Trans.
Ind.Electron., vol. 55, no. 9, pp. 32123220, 2008
Y. Zhang, J. Zhu, and J. Hu, Model predictive direct torque control for grid synchronization of doubly fed induction generator, in Proc.2011 IEEE Int. Electric Machines Drives Conf. (IEMDC), May 2011, pp. 765770
P. E. Kakosimos and A. G. Kladas, Implementation of photovoltaic arraymppt through fixed step predictive control technique, RenewableEnergy, vol. 36, no. 9, pp. 25082514, 2011
J. Rodriguez, R. Kennel, J. Espinoza, M. Trincado, C. Silva, and C. Rojas, High performance control strategies for electrical drives: An experimental assessment, IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 812820, Feb. 2012.
H. Miranda, P. Cortes, J. I. Yuz, and J. Rodriguez, Predictive torque control of induction machines based on state-space models, IEEE Trans. Ind. Electron ,vol. 56, no. 6, pp. 19161924, 2009.