Predictive Direct Power Control (PDPC) of Grid-connected Dual-active Bridge Multilevel Inverter (DABMI)

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 8, No. 4, December 2017, pp. 1524~1533
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v8i4.pp1524-1533  1524
Journal homepage: http://iaesjournal.com/online/index.php/IJPEDS
Predictive Direct Power Control (PDPC) of Grid-connected
Dual-active Bridge Multilevel Inverter (DABMI)
H.H. Goh1
, Azuwien Aida2
, S.S. Lee3
, S.Y. Sim4
, K.C. Goh5
1,2,4,5
Department of Electrical Power Engineering, Universiti Tun Hussien Onn Malaysia
3
School of Electronics and Computer Science, University of Southampton Malaysia Campus, Malaysia
Article Info ABSTRACT
Article history:
Received Sep 10, 2017
Revised Nov 12, 2017
Accepted Nov 29, 2017
This paper deals with controlling a grid-connected dual-active bridge
multilevel inverter for renewable energy integration. The concept of direct
power control is integrated with model predictive control algorithm, which is
termed as predictive direct power control, to control the real and reactive
power injected into the power grid. The proposed multilevel inverter allows
more options of feasible voltage vectors for switching vector selections in
order to generate multilevel outputs, and thereby obtaining high power
quality in the power grid. By using the predictive direct power control,
simulation results show that the proposed multilevel inverter produces lower
power ripple and manage to achieve currents with low total harmonic
distortion which are well within the IEEE standard. The modeling and
simulation of the system are implemented and validated by MATLAB
Simulink software.
Keyword:
Grid connected inverter
Multilevel inverter
Predictive direct power control
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
H. H. Goh
Department of Electrical Power Engineering,
University Tun Hussein Onn Malaysia,
86400 Parit Raja, Batu Pahat, Johor, Malaysia.
Email: hhgoh@uthm.edu.my
1. INTRODUCTION
Developments in renewable energy (RE) integration are getting to be distinctly essential as
worldwide need affordable, reliable, and clean energy. In recent years, renewable energy sources are used to
fill the developing energy claim. The expansion in industrialization has increasing the energy demand. It is
widely known that the biggest energy request is provided by the fossil fuels. Nonetheless, the induced air
pollution as well as the expanding cost of fossil energy have made it important to look towards renewable
energy sources as a future energy solution. Therefore, the integration of renewable energy resources with the
grid has prompted significant researches in power electronic converters for energy conversion [1].
Power quality (PQ) issues have turned out to be essential problems for power consumer at all level
of utilization. Electrical power quality is a wide field which covers power systems engineering, from
transmission and distribution, to end client issues. Approximately 70 to 80% of all the related power quality
problems are attributed to faulty connections. There are various categories of PQ issues, namely the power
frequency disturbances, electromagnetic interference, transients, harmonics and low power factor. Among all
of these problems, current harmonics are one of the most dominant concern which is worth emphasized.
To date, one of the popular approaches for controlling the performance of power system is by
utilizing the power electronic interfaces. Some common control parameters usually involve frequency,
system voltages, current harmonics, active and reactive power. A proper selection of power converter is
important in order to work as a good interface between the grid and renewable energy sources. However,
interconnection of renewable sources into the grid is generally a new development which is very challenging
due to the intermittent characteristics of the renewable energies, particularly the wind and photovoltaic

IJPEDS ISSN: 2088-8694 
Predictive Direct Power Control (PDPC) of Grid-connected …. (H.H. Goh)
1525
energy which are highly dependent on the unforeseen climate change. This may bring about abundance
variety of voltage or frequency of the grid and further deteriorate the quality of the grid. In this regard, the
control of power electronic converter which can synchronized to the grid and efficiently maintain the power
quality of the system become exceptionally important [2].
The control of grid connected voltage source converter has attracted much attentions nowadays.
Generally, control methods can be broadly classified into two categories, i.e. the direct and indirect control
methods. Voltage Oriented Control (VOC) is a type of indirect control technique which is mostly used to
control the voltage source converter. On the other hand, direct power control (DPC) is one of the most
popular direct control strategies in grid connected inverter. This technique is derived from the concept of
direct torque control (DTC) from which in each sampling period, an optimal voltage vector is selected from a
look-up table in order to push the state of the system towards the reference value. The main drawback of
DPC strategy is the use of hysteresis controller that caused variable switching frequency and hence dispersed
harmonic spectrum. In addition, it suffers from poor reference tracking with large power ripples. Predictive
Direct Power Control (PDPC) can be viewed as an extension of DPC by replacing the switching table with
predictive vector sequence selection. PDPC approach has been employed in order to overcome the drawbacks
in DPC strategy [3].
Adoption of efficient controllers for the system alone is not enough, different topologies of
converters also have great impact on the system performance. The various multilevel inverters presented in
literature have been generally perceived as interesting solutions to enhance the voltage limits to a desired
level. Therefore, multilevel inverter (MI) has the merit of low current total harmonic distortion (THD) with
closely sinusoidal output current waveforms and lower switching losses [4]. The induced low harmonics and
low power ripple are very important since it may prevent, or at least reduce the costs arisen in power losses
and bad functioning of equipment from either the consumers or electrical distribution system. In this
instance, this work proposes the implementation of a type of MI, termed as dual-active bridge multilevel
inverter (DABMI) as the grid connected converter. The concept of advance PDPC control strategy is adopted
to control power quality issues of the proposed DABMI for renewable energy integration.
2. DUAL-ACTIVE BRIDGE MULTILEVEL INVERTER TOPOLOGY
Compared to other cascaded MIs, the dual-active bridge multilevel inverter (DABMI) topology
has received little attention despite its simplicity of fault-tolerant capacity [5]-[6]. As the name implies, it
comprises two inverters cascaded in the form shown in Figure 1. It is reliable because its outputs can be
short-circuited when there is damage in either one of the cascaded inverters. In this regard, DABMI is
functioning as a standard two level three-phase inverter [7]. The two isolated dc sources are used to cut the
path of common-mode current flow and to achieve multilevel voltage waveforms [8]. Note that both the
cascaded inverters use equal number of transistors which allow the DABMI to imitate and produce voltages
similar to waveforms generated by a two-level, a three-level or a four-level inverter based on the possible
switching states and active vectors [9]-[10].
Figure 1. Dual-active bridge multilevel inverter

 ISSN: 2088-8694
IJPEDS Vol. 8, No. 4, December 2017 : 1524 – 1533
1526
The merits of DABMI are also pronounced when it is compared to other type of MI. For instances,
it does not require fast recovery clamping diodes and immune to neutral point fluctuations experienced by the
neutral point clamped multilevel inverter (NPCMI) configuration. When compared to flying capacitor
multilevel inverter (FCMI) topology, DABMI uses less capacitors [11] and hence getting rid of complicated
capacitor control. On the other hand, it also uses fewer isolated dc supply than H-bridge converters [12]-[14]
and less diodes than NPCMI [15].
3. PREDICTIVE DIRECT POWER CONTROL OF A DUAL-ACTIVE BRIDGE MULTILEVEL
INVERTER
3.1. System Description
This work is putting emphasis on DABMI topology with PDPC control, as presented in Figure 2.
The standard two level inverters with a total of twelve switches work in a complementary manner to avoid
short circuit. On the other hand, apart from isolating the load from the system, the three-phase transformer
also serves to match the voltage levels to the grid. The primary transformer is fed by the two cascaded
inverters while the secondary transformer is connected to the RL loads which are connected to the power
grid. The series equivalent resistance is considered in the circuit and function to acquire more accurate
control of power. Modulation scheme is nonessential in this control approach since the PDPC itself will
generate the possible switching state to produce switching pattern. The effectiveness of minimizing
harmonics current, power ripple and precise power tracking has been proven by the performance of
PDPC [16].
The most basic and fundamental requirement for multilevel inverter with grid connected
applications is to keep the inverter synchronized with the grid while ensuring appropriate power supply
regardless of the variation of frequency, amplitude and phase in grid voltages. Synchronization unit has been
acknowledged to be a compulsory part for grid connected converter [17]. Power and reactive power can be
directly control by using PDPC while eliminating the use of phase lock loop (PLL) [18]. It is also proved to
be a promising alternative to provide the synchronization between the grid and inverter with low
computational burden and low complexity.
Figure 2. PDPC Control Block Diagram of a DABMI
3.2. Predictive Model of Grid Connected DABMI
The grid voltage component vs and phase current component i, are transformed from the natural
abc reference frame to the stationary reference frame by using the magnitude invariant Clarke
Transformation, which is given by

1527
 
 
2 2
1
1
s s s c s c L s
s c s c L s L s
dP
v v v v v v R P Q
dT L
dQ
v v v v R i v R Q P
dT L
     
     


     
    


























c
b
a
P



2
3
2
1
2
3
3
2
0
1
3
2
(1)
The dynamic input current of the converter can be expressed as
(2)
Where vsαβ denotes the grid voltage, iαβ denotes the phase current, and vcαβ denotes the output voltage of
inverter all in αβ frame. The respective derivative of phase current component can be determined by
rearrangement of (2).
(3)
The magnitude invariant instantaneous active power P and reactive power Q are defined as
)
(
2
3



 s
s
S i
v
i
v
P 

)
(
2
3



 i
v
i
v
Q s
s 
 (4)
The resulting dynamic model of active and reactive power are
(5)
Discretization of (5) enable the calculation of the predicted active power and reactive power at the next
sampling instant, ( 1)
P k  and
P(k +1) = P(k)+
Ts
L
3
2
vsa
2
(k)+ vsb
2
(k) - vsa
(k)vca
(k) - vsb
(k)vcb
(k)
( )- RL
P(k)
é
ë
ê
ù
û
úws
Ts
Q(k)
Q(k +1) = Q(k)+
Ts
L
3
2
vsa
(k)vcb
(k) - vsb
(k)vca
(k)
( )- RL
Q(k)
é
ë
ê
ù
û
ú -ws
Ts
P(k)
(6)
It is worth emphasized that both ( 1)
P k  and depend not only on the grid parameters
but also taken into account the grid frequency. The evaluation of (6) is necessary to predict the optimum
voltage vector. The quadratic cost function which measures the deviation between the reference power and
the predicted power is defined as
s L c
di
v L R i v
dt

  
  
 
1
s c L
di
v v i R
dt L

  
  
 
1
s c L
di
v v i R
dt L

  
  
( 1)
Q k 
( 1)
Q k 

 ISSN: 2088-8694
1528
(7)
where P* represents the reference active power and Q* represents the reference reactive power.
4. SIMULATION RESULT
In order to verify the feasibility of the proposed system, the PDPC of grid connected DABMI has
been simulated using MATLAB/ Simulink Software. The system parameters used in simulation are shown in
Table 1.
In DABMI, each inverter consists of two voltage level and six phase legs, which constitutes 64 feasible
switching states. However, there are only 37 unique voltage vectors for selection due to the redundancy of
switching states, as shown in Figure 3. As a result, the DABMI is able to generate more possible switching
states. The output voltages can hence be stepped in smaller increment and permit lower total harmonic
distortion with lower switching frequency and thus reduce the switching losses.
The reference active power, P*of this system is set to 5000W and the reference reactive power, Q*
is kept at zero. The output power and reactive power illustrate in Figure 4 shows that the proposed controller
manage to keep the active and reactive power close to their references. Low active and reactive power ripple,
which are approximately 100W and 106VAR respectively, are identified with lower current harmonics
established in the system.
Figure 3. Voltage Vector of DABMI
   
2 2
1 1
g P P k Q Q k
 
   
     
   
Table 1. System Parameter
Description Variable Value
Rated Power
DC Voltage 1
DC Voltage 2
Transformer voltage rating
Sampling Time
Line Voltage Frequency
Inductance
Equivalent series resistance
of inductor
P
Vdc1
Vdc2
Tx
Ts
f
L
RL
5kW
300V
150V
500/500V
50e-6s
50Hz
9e-3H
0.14Ω

1529
Figure 4. Output power (P) and reactive power (Q) of DABMI
It is apparent from Figure 5 that the DABMI achieves its peak voltage ratings of , i.e. 200V. It
is also proved that the proposed PDPC can perform the multilevel operation for dual- active bridge multilevel
inverter. The performance of the three phase output currents is presented in Figure 6 which illustrates the
sinusoidal line current with peak amplitude of 28A out of phase with each other by 120o
. Figure 7 shows the
grid voltage and current are in phase for phases, a, b and c. Hence, it shows good agreement with Figure 4
that the reactive power is zero. Hence, the PDPC is verified to be functionaing properly without the need of
grid synchronization module such as PLL. Harmonic spectrum current of phase a, b and c in Figure 8 –
Figure 10 shows excellent value of total harmonic distortion (THD) of the proposed system, to be
specifically, 0.63% for phase a, 0.67% in phase b and 0.63% of phase c has been achieved, which is within
the IEEE standard 519.
Figure 5. Output voltage of DABMI

 ISSN: 2088-8694
1530
Figure 6. Output current of DABMI
Figure 7. Grid current and voltages
(a) (b)
Figure 8. Harmonic spectrum current of phase a Figure 9. Harmonic spectrum current of phase b

1531
Figure 10. Harmonic spectrum current of phase c
5. CONCLUSION
In order to improve the power quality performance in term of lower total harmonic distortion (THD)
and reduce the power ripple, this paper propose a control method, namely the predictive direct power control
(PDPC) for grid-connected dual-active bridge multilevel inverter (DABMI). DABMI enable the generation
of 64 feasible switching states with 37 unique voltage vectors. Modulation stage is unnecessary with on-line
optimisation is perfomed through minimizing a cost-function to obtain the optimized voltage vector for each
sampling period. By directly controlling the active power P and reactive power Q, grid current is
automatically aligned with the grid voltage without the need of additional synchronization module such as
phase-locked-loop (PLL). It is found that the proposed control method managed to produce low power ripple
and achieve low current THD which is well within the IEEE standard.
ACKNOWLEDGEMENTS
The authors would like to thank the Ministry of Higher Education, Malaysia (MOHE), and the
Office for Research, Innovation, Commercialization, Consultancy Management (ORICC), and Universiti Tun
Hussein Onn Malaysia (UTHM) for financially supporting this research under the FRGS grant No. 1529 and
IGSP U667.
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[11] Brychcín, J., et al., “Modulator for 4-level Flying Capacitor Converter with Balancing Control in the Closed Loop,”
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BIBLIOGRAPHY OF AUTHORS
Hui Hwang Goh received B. Eng, M.Eng and PhD. degrees in Electrical Engineering from
Universiti Teknologi Malaysia in 1998, 2003 and 2007 respectively. He is currently an Associate
Professor with the Department of Electrical Power Engineering, Faculty of Electrical and
Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia. His
research interests include embedded power generation modeling and simulation, power quality
studies, wavelet analysis, multicriteria decision making, renewable energies, and dynamic
equivalent. Dr. Goh is a member of the Institution of Engineering and Technology, U.K. He is
also a member of the Institution of Engineers, Malaysia (IEM), a Chartered Engineer under the
Engineering Council United Kingdom (ECUK), and a Professional Engineer under the Board of
Engineers, Malaysia (BEM).
Azuwien Aida Bohari was born in Batu Pahat, Johor in the year 1987. She was awarded B. Eng
(Hons.) and M. Eng in Electrical in 2011 and 2016 from Universiti Tun Hussein Onn Malaysia.
She is currently a PhD. student in the faculty of Electrical and Electronic Engineering, Tun
Hussein Onn University, Malaysia. Her research interest include power electronics, control
strategies for drive system and renewable energy integration.
Sze Sing Lee received the B.Eng. (Hons.) and PhD. degrees in electrical engineering from the
University of Science Malaysia, Penang, Malaysia, in 2010 and 2013, respectively. He is
currently an Assistant Professor at the University of Southampton Malaysia Campus, Johor
Bahru, Malaysia. His research interests include alternative power converter topologies and
control strategies for renewable energy integration.
Sy Yi Sim received B.Eng. (Hons.) and PhD. degrees in Electrical engineering from Universiti
Tun Hussein Onn Malaysia, in 2012 and 2016, respectively. She is currently a Lecturer with the
Department of Electrical Engineering Technology, Faculty of Engineering Technology,
Universiti Tun Hussein Onn Malaysia. Her current research interests include the area of power
electronics, motor drives control, renewable energy integration and artificial intelligence control.

1533
Kai Chen Goh received B. Sc (2005) and M. Sc (2006) from Universiti Teknologi Malaysia and
PhD. (2011) in Built Environment and Engineering Queensland University of Technology. He is
currently an Associate Professor with Department of Construction Management, Faculty of
Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu
Pahat, Malaysia.

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