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IMPROVED FUZZY-PI CONTROL SCHEME FOR
POWER FLOW OF DISTRIBUTED GENERATION
Azuki Abdul Salam1, Nik Azran Ab Hadi2, Fatimah Zaharah Hamidon3 and Ismail Adam4
1
Universiti Kuala Lumpur-British Malaysian Institute
[email protected]
2
Universiti Teknikal Malaysia Melaka,
[email protected]
3
Universiti Kuala Lumpur-British Malaysian Institute
[email protected]
4
Universiti Kuala Lumpur-British Malaysian Institute
[email protected]
ABSTRACT
This paper presents the mathematical model of the Proton Exchange Membrane Fuel Cell (PEMFC)
and analyzes the structure of a grid connected PEMFC generation system. In order to get better
waveforms of grid current, a Fuzzy-PI controller is introduced into the grid connected PEMFC
generation system. The current control scheme for grid connected PEMFC generation system is a PI
controller scheme, which would lead to large transient response due to the load increases. Thus, a
Fuzzy-PI control scheme is proposed in order to improve the power flow control. The PI controller
parameters automatically, according to changes of system parameters. When the proposed grid
connected PEMFC generation system using the Fuzzy-PI controller is simulated in Matlab/Simulink,
the results show that the proposed control scheme works effectively for the power flow grid.
Keywords: PEMFC, Fuzzy-PI, Matlab/Simulink
1. Introduction
The interest in Distributed Generation (DG) systems is rapidly increasing because larger power plants
are becoming less feasible in many regions due to increasing fuel costs and stricter environmental
regulations. Distributed generation systems (DGS) have mainly been used as a standby power source
for improving the power quality[1]. The introduction of DG to the distribution system has a
significant impact on the flow of power and voltage conditions to the customers and utility
equipment. The impacts were mainly seen on voltage support and improved power
quality,diversification of power sources, reduction in transmission and distribution losses,
transmission and distribution capacity release and improved reliability[2]. The increasing number of
DG requires a new technique for the operation and management of the electricity grid to enhance
power supply reliability and power quality.
Fuel Cell Distributed Generation (FCDG) systems can be strategically placed on any site in a power
system at the distribution level for grid reinforcement. It will eliminate the need for system upgrades
and improves system integrity, reliability and efficiency[3]. Therefore, proper controllers need to be
designed for a FCDG system to make its performance characteristics as desired[4]. Fuel cells are of
various types such as Proton Exchange Membrane Fuel Cell (PEMFC), Alkaline Fuel Cells
(AFC),Molten Carbonate Fuel Cells (MCFC) and Solid Oxide Fuel Cells(SOFC). Among these fuel cell
E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by
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31
types, the Proton Exchange Membrane Fuel Cells and Solid Oxide Fuel Cells have great potential for
distributed generation applications. The simulation model is developed for the grid-connected
PEMFC generation system by combining the individual component models and the controllers
designed for the power conditioning units. Matlab/Simulink SimPowerSys toolbox had been used to
model the fuzzy logic controller. Hence, in this paper, the Fuzzy-PI control structure had been
developed for a FCDG system with active power management and reactive power control capability.
Simulation results are given to show the overall system performance for active and reactive power
management.
2. PEM Fuel Cell Generation System
The PEMFC consists of a solid polymer electrolyte sandwiched between two electrodes (anode and
cathode). In the electrolyte, only ions can exit and electrons are not allowed to pass through. So, the
flow of electrons needs a path like an external circuit from the anode to the cathode, to produce
electricity because of a potential difference between the anode and cathode. The overall
electrochemical reactions in PEMFC fed with a hydrogen containing anode gas and an oxygen
containing cathode gas are as follows:
At anode reaction,
2H 2  4H   4e 
(1)
O2  4H   4e   2H 2 O
(2)
2H 2  O2  2H 2O  electricit y  heat
(3)
At cathode reaction,
Overall reaction
The output stack voltage V is defined as a function of the stack current, reactant partial pressures,
fuel cell temperature, and membrane humidity. The potential difference between the anode and
cathode is calculated using the Nernst’s equation and Ohm’s law and can be written as follows:

RT
V  N0  E0 

2F

 PH PO 0.5  
 ln 2 2    B ln CI  Rint I

PH 2O  


(4)
Where:
No is the number of cells connected in series;
Eo is the voltage associated with the reaction free energy;
R is the universal gas constant;
T is the temperature; is the current of the fuel cell stack;
F is the Faraday's constant.
The relationship between molar gas flow through the valve is proportional to its partial pressure
and can be expressed as,
qH
K an
(5)

K
2
PH 2
qH 2 O
PH 2 O
M H2

H2
K an
 K H 2O
M H 2O
(6)
Where,
qH2
qH2O
PH2
PH2O
PO2
: Molar flow of hydrogen
: Molar flow of water
: Partial pressure of hydrogen
: Partial pressure of water
: Partial pressure of oxygen
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KH2
KH2O
Kan
MH2
MH2O
: Hydrogen valve molar constant
: Water valve molar constant
: Anode valve molar constant
: Molar mass of hydrogen
: Molar mass of water
In this work, the model used to simulate the PEMFC generation system is given as shown in Fig.1.
Using the equations derived for the PEMFC model, a simulation model is developed by using the
SimPowerSystems toolbox of Matlab/Simulink. The parameters of the simulation model are given in
Table 1.
Fig.1 Model of PEMFC
Table 1: Parameters of PEMFC Generation System
Parameters
Faraday’s constant, F
Universal gas constant, R
Values
96484600 C/mol
8314.47 J/kmol-2 K
No load voltage, E0
1.229 V
Number of cells, N
420
Constant, K
1.0883 x 10-7kmol/(s-1 A)
Valve molar constant for hydrogen,
KH2
4.22 x 10-5kmol/(s-1atm)
Valve molar constant for oxygen,
KO2
2.11 x 10-5kmol/(s-1atm)
Valve molar constant for water,
KH2O
7.716 x 10-6 kmol/(s-1
atm)
Response time for hydrogen flow,
3.37 sec
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TH2
Response time for water flow, TH2O
18.418 sec
Response time for oxygen flow, TO2
6.74 sec
Utilization factor , U
0.8
Reformer time constant , τ1, τ2
2
Conversion factorenergydensity, CV
2
Activation current constant, B
0.04777
Activation voltage constant, C
0.0136 V
Stack internal resistance, Rint
0.00303
PI gain constants,K5 ,K6
10
Ratio of hydrogen to oxygen, rHO
1.168
Methane reference signal, Qmethref
0.000015
3. Fuzzy-PI Control Strategy
The Fuzzy-PI controller used in the outer voltage control loop to replace the conventional PI
controller would greatly reduce the overshoot and get a faster response. The Fuzzy-PI controller can
follow nonlinear load since it can adjust the control parameters for different operation[5]. The two
input arguments of fuzzy controller are voltage error and the derivative of the voltage error.
According to the fuzzy rules, the fuzzy logic, reasoning part would adjust the PI parameters and send
to the PI controller. With the real time updating of the PI parameters,the control process can be
more accurate than conventional PI controllers with fixed PI gains.Fuzzy logic control is the
evaluation of a set of simple linguistic rules to determine the control action. To develop the rules of
the fuzzy logic, a good understanding of the process to be controlled is needed, and it does not
require a complicated mathematical model[6]. Fig.2 shows the schematic diagram of Fuzzy-PI
generation system.
In the proposed work, the Fuzzy-PI control technique is used to generate the gating signals. After
proper amplification and isolation, the switching signals obtained are given to the switching devices
of the PWM converter. The DC link capacitor voltage is remaining constant by a fuzzy logic
controller. This controller is designed using the fuzzy inference system, which is the FIS editor,as is
shown in Fig 3. The input and output membership functions of the each variable error, change in
error and change in firing angle are shown in Fig.4, Fig.5 and Fig.6 respectively. Fuzzy logic is
characterized by five fuzzy sets are Negative Big (NB), Negative Small (NS), Zero (Z), Positive Small
(PS), and Positive Big (PB) for each input and output variables. The triangular membership function is
used for simplicity. The implication is performed using Mamdani type min-operator. Defuzzification
is employed using the centroid method. The Fuzzy rule based of the proposed fuzzy-PI is shown in
Table 2.
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Fig.2 Fuzzy-PI generation system
Fig.3 FIS editor
Fig.4 Input Membership function of FLC for error(e)
Fig.5 Input Membership function of FLC for change in error(de)
Fig.6 Output Membership function of FLC for firing angle(dalpha)
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Table 2: Fuzzy rule base
Change of error (de)
Error
(e)
NB
NS
Z
PS
PB
NB
NB
NB
NB
PS
Z
NS
NB
NB
NS
Z
PS
Z
NB
NS
Z
PS
PB
PS
NS
Z
PS
PB
PB
PB
Z
PS
PB
PB
PB
4. Simulation Results and Discussion
The model of a three-phase grid-connected VSI system and the proposed controller are simulated
using MATLAB/Simulink environment. In this model, the proposed controller of the DG unit is
designed based on PQ control mode, and the Fuzzy-PI controller is incorporated to find the optimum
parameters of this controller. In order to illustrate the power flow control of the DG unit and to
verify the performance of the proposed controller, the model is simulated for power exchange
between the grid connected PEMFC generation system and the utility. Thus, the control strategy can
use the Fuzzy-PI technique. In this instance, the load is supplied by the DG unit and its excess power
is automatically fed to the grid. This demonstrates that the proposed controller offers stable and
satisfactory power flow grid-connected PEMFC generation system.
In Fig.7, the load is increased to 2.63 p.u active power at 0.65s. In this case the balance power is
supplied by the grid, while the DG unit still injects a sustained output power. Now the load is
decreased to 0.9p.u active power at 0.9s. In Fig.8, the load is increased to 0.53p.u inductive reactive
power at 0.65s. In this case the balance power is supplied by the grid, while the DG unit still injects a
sustained output power. Now the load is decreased to 0.17p.u inductive reactive power at0.9s. The
Total Harmonic Distortion (THD) is calculated using FFT analysis tool which is provided in
Matlab/Simulink software. The nominal THD value is <5%. By using the Fuzzy - PI Controller, the THD
is calculated as 4.44% in Fig.9.
Fig.7 Active power flow in the grid connected PEMFC generation system
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Fig.8 Reactive power flow in the grid connected PEMFC generation system
Fig.9 THD Waveform using Fuzzy PI controller
5. Conclusion
In this paper, the power flow grid connected PEMFC generation system was presented. The main
objective is to control and meet the power demand variations occurring on load side. For this
purpose, a Fuzzy-PI controller is applied to the system to generate the required gate pulses for IGBTs
of VSI for proper switching to meet the load demand. The Fuzzy-PI controller is used in inner loop
current control on the VSI, which can regulate parameters of the PI controller automatically. The
proposed control strategy for this kind of distribution system helps in the proper operation of each
power source under power quality disturbances. The effectiveness of the proposed system can be
verified by using the MATLAB/SIMULINK environment.
Acknowledgement
This work is financially supported by the UniKL grant UniKL/IRPS/str11046 to conduct this research.
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