Adaptive Fuzzy PI Current Control of Grid Interact PV Inverter

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 1, February 2018, pp. 472~482
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i1.pp472-482  472
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Adaptive Fuzzy PI Current Control of Grid Interact PV
Inverter
R.S. Ravi Sankar1
, S.V. Jayaram Kumar2
, G. Mohan Rao3
1,3
Departement of Electrical and Electronic Engineering, Vignan‟s Institute of Information Technology,
Vishakapatanam, India
2
Departement of Electrical Engineering, GRITE, Hyderabad, India
Article Info ABSTRACT
Article history:
Received Oct 15, 2017
Revised Jan 7, 2018
Accepted Jan 22, 2018
Now a day‟s, Photo Voltaic (PV) power generation rapidly increasing. This
power generation highly depending on the temperature and irradiation. When
this power interface with grid through the voltage source inverter with PI
controller. Its gains should be updated due to the parametric changes for the
better performance. In This Work Fuzzy Controller updates the gains of the
proportional integral (PI)s Controller under variable parametric conditions.
the gaines of the PI Controller are updated based on the error current and
change in error current through the fuzzy controller. The error current in
direct and quadrature frame are the Inputs to the PI controller. The PI
Controller generates the reference voltage to the pulse width modulation
technique. Here reference voltage is compared with the carrier signal to
generate the pulses to the 3-Ph Inverter connected to the grid. This controller
has given well dynamic response with less steady state error and also given
The less THD of the grid current compared to the PI and Fuzzy controller.It
Is implemented and verified in MATLAB Simulink.
Keyword:
Adaptive Fuzzy PI Controller
Propositional Integral control
Renewable Energy Sources
Three phase Inverter
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
R.S. Ravi Sankar,
Departement of Electrical and Electronic Engineering,
Vignan‟s Institute of information technology,
Vishakapatanam, India.
Email: satya_ravi2001@yahoo.com
1. INTRODUCTION
With development of renewable energy technologies, different inverter structures and control
systems are investigated for renewable energy supplied inverters. In the old literature, PI or PID controllers
are used to control the voltage source inverter (VSI) interact grid. PI controller with fixed gains for the fixed
operating point provides an acceptable performance, but poor transient performance is obtained when the
inverter operating point varies continuously is depending on the dynamics of the plant and also depending
natural conditions such as solar radiation in case of the PV system and wind speed [1]. More ever voltage,
frequency of the grid also may change and line impedance during the operation of the inverter [2]. Hysteresis
current control has benefits of simple Implementation, robust structure, high stability, fast response, it has
disadvantage of the variable switching frequency causes interference to communication lines, design of filter
difficult, switching loss more [3-5].Studies on different topology in inverter such as multilevel inverter and
HERIC inverters connected to the grid with linear control was used to achieve the high converter efficiency
with minimizing the switching loss [6].Transformer less inverter topology are studied with linear controls
and hysteresis control and dead beat control method to improve the efficiency of the single phase inverter
connected to the grid. However, feed forward of the voltage and inverter current are used to improve the
performance of the inverter connected to the grid when the delay in the control time and variations on passive
elements values affect the deadbeat control were proposed [7]. Although, to improve dynamic response of the

Int J Elec & Comp Eng ISSN: 2088-8708 
Adaptive Fuzzy PI Current Control of Grid Interact PV Inverter (R.S. Ravi Sankar)
473
system line voltage feed forward is used, unwanted compensations increase the Voltage harmonics in the
current waveform in this method. Recently, proportional resonant (PR) controllers [8-10] areused as current
controller to achieve the unity power factor correction rectifiers connected to the grid. The PR controller has
given the infinite gain at resonance frequency and zero steady state error. Whatever it is harmonic
compensation of the PR controller is limited to low order current harmonic frequency is out of the band width
of the system. But it has disadvantage that it needs the resonance frequency information is depending on the
grid frequency in grid connected system which is variable [11].
So the operation of the grid connected inverter with linear control techniques with fixed gain is not
given suitable performance with change in the system parameters. The grid connected inverter not only
operates different circumstances and also meet the international standards [12-14]. Fuzzy logic control (FLC)
is a non-linear which is used to updates the gains of the PI or PID controller has given the better performance
with changing the parameter conditions and load disturbances [15-17]. Thus, FLC is used and given the
better performance to control the plant with dynamic changes and grid interact inverter in variable wind
energy applications and in FLC is used to extract the maximum power from PV panel [18-20].In fuzzy-PI
controllers, fuzzy logic decides the gains of the PI controller according to operation point of the system is
depending on the parameter changes [21-22].Fuzzy PI control makes the system insensitive to external
circumstances changes are there in Renewable energy system like PV and Wind [23]. Since supply
specifications and grid conditions are variable, adaptive control of the grid connected inverters is important to
achieve requirements of the grid as well as consumers [24-25].
This paper is organised as Section 2 Describes the modeling of PV Array, Section 3 Describes the
inverter and design of the LCL filter and Section 4 Presentes the design of the Adaptive Fuzzy PI controller.
In Section 5 the Resultes And Discussion.
2. DESIGN OF THE PV ARRAY
Generally, A PV cell is a simple P-N junction diode which converts solar irradiation into electricity.
PV cell is represented as a combination the current source (Ipv), shunt resistance (Rp), diode and a series
resistance (Rs). In order to overcome the imperfections of PV single diode modeling at high voltages and low
voltages, a two diode model of PV solar cell is considered is shown in the Figure 1 [3].
Figure 1. Two diode model of the PV Cel
The PV cells are connected in series (Ns× Np cells, where Np= 1) to produce the PV module. Basic
equation that describes the current output of PV module using the two- diode model is given by equation.
The PV cell voltage- current relationship in equation (1) is modified for PV module as [4-6].
sh
t
pss
I
Vn
NRIVN
NpIoNpIphI 













 
 1
*
/*/
exp (1)
In grid connected PV system, modules are configured in series- parallel structure with any number
of PV modules (Nss ×Npp modules) to produce the PV array. Then the PV module voltage-current relationship
in Equation (1) is modified for PV array and it is given by (2), [5].



















r
go
r
rso
TTnk
Eq
T
T
II
11*
exp (2)

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 1, February 2018 : 472 – 482
474
Where Ipv, Io1, Io2, Rs are the parameters of the individual modules, Ns and Np are the number of cells
connected in series and parallel of module respectively, Nss and Npp are the number of modules connected in
series and parallel respectively for array. In this work PV Array is designed using the KC200GT solar module. Its
Parameters are given in the Table 1 [4].
Table 1. KC200GT PV Module Parameter
Parameter Value
Maximum Voltage (Vm) 26.3V
Current at Maximum Power (Im) 7.61A
Open Circuit Voltage (Voc) 32.9V
Short Circuit Current (Isc) 8.21A
Total No.of Cells in Series (Ns 54
Total No.of Cells in Parallel (Np) 1
Temperature Coefﬁcient of Voc(Kv) -123 mV/o
C
Temperature Coefﬁcient of Isc(Ki) 3.18mA/o
C
saturation current Io1 = Io2 1.045×10−9
A
Rp 415.405 Ω.
Rs 0.221 Ω.
3. THE THREE PHASE INVERTER
3-Ph Voltage source inverter connected to grid with LCL filter. Consisting the six switches as
shown in the Figure 2. In this paper these switches are controlled with the help of the Adaptive fuzzy PI
controller. Inverter output voltage is defined in terms of the switching states and input DC voltage. The
magnitude of the output voltage is controlled by means of the modulation index of the PWM and frequency
of the output voltage is equal to the frequency of the reference wave generated by the fuzzy controller.
3-Ø
GridPV
SYSTEM
Figure 2. Three-phase grid connected PV inverter
Then, the output voltage „Vi „can be related to the switching state vector „S „and VPV.
SVV PVi  (3)
Where VPV is the PV voltage after MPPT.
The overall block diagram of the system is given in the Figure 3. The fuzzy controller is used to
update the gains of the PI Controller for the Parametric Changes. The PI controller processes the Error in
current and generates the reference Voltage.The reference voltage is input to the PWM operation.Generates
the switcheing signal to the 3-Ph Inverter. In this work frequency modulation index is taken as the 200.The
magnitude of the output voltage is controlled by voltage modulation Index. It is dependeing on the error in
Current.

475
3-Ø
INVERTER
3-Ø
Transformer
3-Ø
GRID
3-Ø
PLL
3-Ø
Ref Current
Fuzzy logic
Controller
PI
Controller
d/dt
PWM
GENERATOR
Fuzzy logic
Controller
PI
Controller
d/dt
PV
SYSTEM
Figure 3. Overall Block Diagram of the PV Inverter Connected to the grid with Adaptive Fuzzy PI Controller
Table 2. Parameter of the System
Variable Speed (rpm)
PV Voltage 600 V
Grid Voltage 415V L-L (rms)
Grid Frequency 50Hz
Inverter Side Inductance(Li) 3mH
Grid Side Inductance(Lg) 2.2mH
Filter Capacitor(Cf) 0.015μF
Carrier Frequency 10KHz
4. DESIGN OF THE FUZZY PI CONTROLLER
The Architecture of thefuzzy controller is shown in Figure 4. It has three units Fuzzifier unit,
Interface unit and Defuzzifier at the out terminal. It has input and output variables. Error and change in error
currents are inputs whereas proportional gain (Kp) and Integral gain (KI) are the output variable [19-21]. The
error in current and change in error currents are defined in Equation (4) and Equation (5). Change in error
current means difference between the present error current and previous error current value. Which are
defined in (4) & (5) respectively. In this work input five membership levels are defined as Negative Large
(NL2), Negative Small (NS1), Zero (Z), Positive Small (PS1), Positive Large (PL2) and for output
membership function three levels are defined as Large (L2), Medium (M1), Small (S0) which are used to
build input and output membership functions and Based on the rule matrix‟s table foroutput variable Ki and
Kp are given in Table 3 & Table 4 [23], [24]. The membership fuction of the input and output varibles are
shown in Figure 5 to Figure 8. Three Dimentinal variation of the Fuzzy Inputs (I d&Iq) and outputs KIand Kp
are shown in Figure 9 and Figure 10 [25].
)k(I)k(I)k(ei gr  (4)
)1k(ei)K(eiei  (5)

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Figure 4. Architecture of the Fuzzy controller
Membership functions for Error Current (Id& Iq)
Figure 5. Membership functions of error in currents
(Id& Iq)
Figure 6. Membership functions of change in error
(Id& Iq)
` ` ` ` `
Figure 7. Membershipfunction of the output (Ki) Figure 8. Membership function of the output (Kp)
Table 3. Rule Matrices table for Kp output Variable
Output
(KP)
Error(e)
Change
In
Error(ce)
NL2 NS1 Z PS1 PL2
NL2 L2 L2 M1 M1 S0
NS1 L2 L2 M1 S0 S0
Z M1 M1 M1 M1 M1
PS1 S0 M1 M1 M1 L2
PL2 S0 S0 M1 L2 L2

477
Table 4. Rule Matrices table for Ki output Variable
Output
(Ki)
Error(e)
Change
In
Error(ce)
NL2 NS1 Z PS1 PL2
NL2 S0 S0 M1 L2 L2
NS1 S0 S0 M1 L2 L2
Z M1 M1 M1 M1 M1
PS1 L2 L2 M1 S0 S0
PL2 L2 L2 M1 S0 S0
Figure 9. Three dimensional variation of the Fuzzy
inputs and output Kp
Figure 10. Three dimensional variation of the Fuzzy
inputs and output KI
Three Dimensional variation of the Fuzzy inputs (Id & Iq) and outputs KI and Kp are shown in
Figure 9 and Figure 10.
The Kp and Ki taken from fuzzy logic controller is given to PI controller. The schematic diagram of
PI controller is shown in Figure 11. The Kp and Ki values multiplied with error and are sent to zero order hold
and discrete time integrator.
Figure 11. Schematic Diagram of PI Controller
5. SIMULATION RESULTS
5.1. Case Study 1: Change in Reference Current
The above mentioned system parameters in Table 4 are considered to study the adaptive fuzzy
current controller makes the inverter to tracks the change in reference current at fixed DC input voltage. Here
temperature and irradiation in the nature is considered as constant to get the constant DC Voltage to the
inverter as shown in Figure 12. Here changes in reference current is considered at 0.5 sec from 4A to 6A as
shown in Figure 17 causes to change the PV current supllied to the Inverter as shown in Figure 13. Inverter
output voltage is shown in Figure 14. The variation of the Kp and Ki values due to change in reference current
are shown in Figure 15 and Figure 16. Grid current followes the reference current with less transient behavier

 ISSN: 2088-8708
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and zero steady state error even though change in reference Current as shown in Figure 18. Total Harmonic
Distortion of the Grid current is less the 5% as shown in the Figure 19.
Figure 12. PV Output Voltage Figure13. PV Current
Figure14. Inverter Output Voltage Figure 15. Variation of the Proportional Gain (Kp)
Figure 16. Variation of the Integral Gain ( Ki) Figure 17. Referance Current

479
Figure 18. Referance Current & Grid Current Figure 19. THD analysis of the Grid Current
5.2. Case Study 2: Change in DC Voltage
Same system parameters are considered to study weather adaptive fuzzy current controller makes
the inverter to track the reference current if there is change in DC input voltage. Here change in irradiation is
considered to change the DC Voltage from 0.5 Seconds and PV Currents are shown in Figure 20 and
Figure 21 respectively. The constant grid current is considered as 5A shown in Figure 25. The variation of
the Kp and Ki values which are updated by fuzzy controller due to change in DC input voltage as shown in
Figure 23 and Figure 24. Total Harmonic Distortion of the Grid current is less the 5% as shown in the
Figure 27.
Figure 20. Change in PV Voltage Figure 21. PV Current
Figure 22. Inverter Output Voltage Figure 23. Variation of the Integral Gain ( Ki)

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Figure 24. Variation of the Proportional Gain (Kp) Figure 25. Grid Current
Figure 26. Referance Current & Grid Current Figure 27. THD Analysis of the Grid Current
The THD values of the Grid Currents with PI Controller, Fuzzy controller and Fuzzy PI current
controllers are given in the Table 5. Adaptive Fuzzy Controller improved the quality of the grid current.
Table 5. Comparision of the Controllers
Currents With PI THD% With FLC THD% With Fuzzy-PI THD%
4A 4.81 5.28 4.71
5A 4.01 4.27 3.54
6A 3.59 4.66 2.98
7A 4..80 4.79 2.57
8A 5.80 7.58 2.42
9A 5.50 6.63 2.16
6. CONCLUSION
In this work adaptive Fuzzy PI current controller is implemented for the grid connected 3ph PV
inverter under different parametric conditions. When there is change in PV voltage and reference current.
The Fuzzy controller updates the gains of the PI controller so that grid current traces the references Current
with less transient behavior, zero steady state error and low THD value of the Grid current compared to the
PI Controller, Fuzzy controller. It is implemented and verified in Matlab-Simulink.
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BIOGRAPHIES OF AUTHORS
R.S. Ravi Sankar obtained A.M.I. E degree from institute of engineers(India) in 2002.he obtained
M.Tech. from JNTUH Hyderabad in 2004.currently doing Ph.D. in JNTUA anthapuram. He got 10
years of the teaching experience. his area of interest is control of Voltage source inverter
connected to grid and application of power electronics to the power system and renewable energy
systems and presently working as associate professor of Electrical Engineering in VIIT
Vishakhapatnam.

 ISSN: 2088-8708
482
S.V. Jayaram Kumar obtained B.E and M.E degrees from Andhra university Visakhapatnam in
1976 and 1979. He obtained Ph. D degree from Indian Institute of technology Kanpur in the year
2000. He has got 35 years of teaching experience. His areas of interest are Flexible AC
Transmission systems, and application of power electronics to power systems. He worked in
JNTUH college of Engineering Hyderabad till 2014 and presently working as professor of
Electrical Engineering in GRIET Hyderabad
Gorapalli Mohan Rao currently is an assistant professor in the Vignan‟s institute of Information
Technology, JNTUK Kakinada, India. He received his M.Tech. Degree from the Department of
Electricals and Electronics Engineering, with the Best Dissertation Award. He received his B.Tech
from Visakha institute of engineering Technology. His research work has been focused on PV
system, MPPT, Fuzzy Systems, Grid Interactive Inverters, and Controllers.

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