VOLTAGE CONTROL BY FUZZY LOGIC OF THE PHOTOVOLTAIC PRODUCTIONS INTEGRATED IN THE HTA GRID

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),

ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 27-38 © IAEME

27

VOLTAGE CONTROL BY FUZZY LOGIC OF THE PHOTOVOLTAIC

PRODUCTIONS INTEGRATED IN THE HTA GRID

Mohamed DHARIF1, Abdellah AIT OUHMAN

2

Labo.of Optimization of Communication Systems Advanced, Systems and Security

University CADI AYYAD, (ENSA) Marrakech, Morocco

ABSTRACT

The intense integrating of the decentralized production PV (DP) into the HTA grid causes a

fluctuation in the voltage during the day, this paper discusses a method for regulating the voltage at

DP based on fuzzy logic controller, through the injection or the absorption of the reactive energy,

taking account of local stresses of the measured voltage.

Keywords: PV Production, Fuzzy Logic, Reactive Energy, Fluctuation, Voltage Control.

1. INTRODUCTION

The massive integration of renewable energy production in the grid, including photovoltaic

plants, dramatically, changes the energy structure of the HTA distribution grid. The impact of

decentralized productions (DP) is significant especially on the voltage at the output variation and

load.

In fact, a simulation of an intelligent voltage control (based on fuzzy logic) is needed to learn

about the contribution of such control on the stability of the voltage plane of the HTA grid.

This paper deals with an auto-adaptive control [1], designed to be embedded on production

devices unobservable to allow an autonomous control of voltage at the connection point. This control

is based on the regulation of transit reactive power (injection or absorption) to maintain the voltage

level within permissible limits.

The simulation results are displayed as a graph and a critical analysis was conducted to

describe the contribution of such control.

INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &

TECHNOLOGY (IJEET)

ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 27-38

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2. HIGHLIGHTING PROBLEMS OF FLUCTUATION VOLTAGE INDUCED BY DP

In the conventional distribution grid of radial structure, the voltage is generally higher at the

substation and decreases towards the end of the line. Power flows in one direction from the

substation towards the consumers. But with the presence of DP on the grid, the system becomes

active and the power flows are changed.

Fig.1: Determination of the voltage drop

The voltage drop (∆V pu) between the substation and the connection point of DP (Figure 1) is

determined as follows [1], [2]:

∆ . . cos . . . sin 1

∆ . . . 2

∆ . . 3

Where:

• R, L is the total resistance and inductance of the line

• VN voltage at node N and It the current flowing in the line

• P, Q the active and reactive power at node N

• PGQG are the active and reactive power supplied by DP

• PL, QL are the active and reactive power consumption

• Qc is the reactive power compensation device

Equation (3) illustrates one of the main problems with connecting the DP to grid. Indeed,

injection of active and reactive power will induce an increase in the voltage at connection node.

Impacts on voltage of such productions will be different depending on the type of grid to which they

are connected. According to the grid structure, the characteristics of the substation, the connection

point and the power injected by the DP, the voltage can be raised to the point of connection, and can

even exceed the permissible limit.

In the HTA grid of distribution, the reactance is greater than the resistance, and if X >> R, the

formula (3) can be simplified as follows:

∆ .!"#" 4



29

The injection of reactive power will have more impact on the voltage level of the injection of

active power.

The voltage level of distribution grid depends strongly on the level of consumption and

power factor of the load. A change in load causes a change in voltage on the grid. The extreme case

for a rise in voltage corresponds to a zero load consumption associated with a maximum production.

3. MODELING PV PRODUCTION

A three-phase PV system is modeled by a current injector with its power control. The control

system regulates the power injected by the PV DP, at the connection node.

The active power is determined by the MPPT PV module and the reactive power is required

by a adaptive control, taking into account the stresses at the connection point of the production.

On this basis, the model of adaptive will be developed, incorporating the loop voltage model P/V on

the control model P/Q [1], [3].

Fig.2: Hybridization of P/V model and P/Q causing the adaptive control

Thus, the model of the PV production is constructed as a whole represented by a power

injector, the figure shows in detail the model with the different control loops[4], [5].

Fig.3: Modeling current injector of PV generation with adaptive control

4. OPERATING PRINCIPLE OF ADAPTIVE CONTROL

Control consists of two independent regulatory loops, loop reactive power to control the high

voltage (Figure 4.a) and loop reactive power control for low voltage (Figure 4.b) [6].



30

a) Control loop of the high voltage b) Control Loop low voltage

Fig.4: Adaptive control loops based on the measured voltage

5. DYNAMIC SYSTEM AND LIMITATIONS

Dynamics chosen for the control loop of the active power is that of the MPPT (Maximum

Power Point Tracking) that is a few seconds, while the dynamic control of reactive power is selected

that of the inverter, with tenth of a second.

The current can be directly limited on the amplitude values Id and Iq into the domain of Park,

located at the output of the control loop, and corresponding to the current components to be injected

onto the grid. These components are shown in Figure 3. The limitation for the Id component is

selected based on the limitation of the DC power source. And the limitation for Iq component is

chosen accordingly , so as not to exceed the limitation reactive power chosen based on a tangent phi

generally limited to 0.4 [1], [7].

In order not to make the system unstable, the dynamic of adaptive block is chosen twice as

fast as the control loop reactive power.

6. TEST CASE OF ADAPTIVE CONTROL VOLTAGE

To determine the performance of the control voltage as a function of local grid constraints,

adaptive control has been simulated on a grid HTA, taking into account extreme operating electric

grid configurations, namely, high production and low load as well as a low production and high load.

In order to simulate the impact of a strong integration of PV production on the behavior of the grid, a

penetration rate of 72% was adopted (penetration rate = total power production / power substation).

Fig.5: HTA Grid simulated



31

This grid was implemented on MATLAB/SIMULINK by using the SimPowerSystems

library, the simulation scenario is as follows:

• Change of load

o At t =2s, ∆Sload=-20%

o At t =5s, ∆Sload=20%

o At t =19s, ∆Sload=10%

o At t =22s, ∆Sload=-20%

• Set point change of the production

for all PV, t =4s to t=16s.

Production increase of 4.5MW to

14.4MW.

Fig.6: Transit in the substation for the defined scenario

The characteristics of the simulated grid are summarized in the following table:

Component Parameter Value retained

HTB Source

Psc 277MVA

Nominal voltage 63kV

Nominal

frequency

50Hz

R/X 0,05

Transformer data

Power 20MVA

Primary voltage 63kV

Secondary

voltage

22kV

Usc 16%

Wiring YNyn

Neutral resistance 42,5Ωm

Line data Rd 0,2236 Ωm/km

Ro 0,368 Ωm/km

Xd 0,35 Ωm/km

Xo 1,588 Ωm/km

Cd 11,13nF/km

Co 5nF/km

0 2 4 6 8 10 12 14 16 18 20 22 240

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]

Active p

ow

er

[pu]

Transit of active power in substation



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7. SIMULATION OF GRID BEHAVIOR WITHOUT CONTROL VOLTAGE

During this simulation no control voltage will be executed, the following graphs illustrate the

fluctuation of the voltage at connection points of PV plants with a tolerance of ±10% of voltage.

a) Branch n°1 b) Branch n°2

c) Branch n°3

Fig.7: Evolution of the voltage at the connection point of the PV production

This scenario leads to see the sub voltages at times t=2s to t=5s, in the connection points of

the PV production N3 , N4 , N7 , N11 , N14 , N17 and N18 , and a high level of the voltage at the

output of the DP connected in the nodes N2 , N8 and N11 . The voltages seen by other production

units are not critical and remain within acceptable values.

Therefore, this scenario highlights the exceeded of the voltage limits due to the production of

active power into the distribution grid, HTA in this case. Thus, it will be possible to intervene with

groups of productions, by absorbing or supplying reactive power according to the stresses measured

at the connection point of each DP. The adaptive control created will be tested on the same case.

8. SIMULATION OF ADAPTIVE VOLTAGE CONTROL

To enable the PV productions to participate in voltage regulation , adaptive control was

activated at PV production , the permitted voltage range that is tolerated at the HTA grid is ±10% of

the contract voltage, Vmax = 1.1pu and Vmin = 0.9pu , the simulation results are as follows:

0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Volt

age

[p

u]

N1 N2 N3 N4 N7 Vmax Vmin

0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Volt

age

[p

u]

N8 Vmax Vmin

0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Vo

lta

ge

[p

u]




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c) Branch n°3

Fig.8: Evolution of the voltage at the connection point of the PV production

The results show that this type of regulation is effective for DP N2, N3, N11 and N14.

Indeed, the critical voltages in the preceding simulations are avoided by acting on their production of

reactive power. However this is not sufficient in the case of DP N4, N7, N8, N17 and N18. Figure 9

show that the DP in end of grid cannot effectively control the voltage in the connecting node.

By analyzing the evolution of reactive power at each output, we see that only the productions under

constraints involved in control the voltage at the connection point, therefore a limitation to support

the voltage plan of the HTA grid.

Fig.9: Evolution of reactive power at PV productions

0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Voltage [

pu]


0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Voltage [

pu]

N8 Vmax Vmin

0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Voltage [

pu]


0 5 10 15 20 24-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Time [s]

Reactive p

ow

er

[pu]

PN°1

PN°2

PN°3

PN°4

PN°5

0 5 10 15 20 24-0.1

0

0.1

0.2

0.3

0.4

Time [s]

Reactive p

ow

er

[pu]

PN°6

PN°7

PN°8

PN°9



34

So, it is necessary to take the help of other DP, Indeed, if the downstream groups

participating in the control, the effort provided by them would be less important. It is in this

perspective that will be added a desired voltage window narrower than the window of eligibility.

It is with this objective, that the adaptive controller will evolve. The principle is simple, in

order to involve all DP in the control, a "desired" voltage window will be added to the principle of

control (Figure 10). Beyond this window of voltage, narrower than the previous window of

permissible voltage, output voltage regulation activates. It will therefore be possible to react before

reaching the critical voltages.

Fig.10: Plan of operation of the adaptive control

To ensure maximum participation [1] of the different productions, it is necessary to provide a

window of different desired voltage for each DP depending on its position and thus the constraints it

faces.

We started with the adaptive controller which it will be added a "smart" supervision [8], [9]

able to calculate voltage set points desired autonomously through local measurements of voltage and

reactive power. This intelligent supervision is achieved through fuzzy logic.

Fig.11: Basic principle of the fuzzy supervisor

Figure 11 illustrates the operating principle of fuzzy supervisor, it takes place up stream of

the adaptive control which has been previously illustrated, and be, depending on local measurements

of voltage and reactive power, capable of providing voltages orders in the manner consistently as

possible.



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9. STAGES OF DEVELOPMENT OF A FUZZY SUPERVISOR [10], [11] AND [12]

9.1 Fuzzification The following figure shows the fuzzy definition of each input variable (V, Q)

Fig.12: Definition of fuzzy input variables

Each variable must be expressed in an interval [-1,1]. Thus the measurements of voltage and

reactive power are expressed relative to their maxima and minima. These correspond to the values of

allowable stresses and the limits of reactive power of production considered.

∆ %&' () *+,-./ *+,0 ; ) *+, -23 *+,04 5

6 -./7 6

9.2 Inference In this step, the supervisor reaction will be determined according to the input data. So, it will

be decided "case by case", how to evolve the desired voltage window in function of the input state.

Thus, fuzzy logic makes simplify this reflection, since the entries are qualified by quantitative terms

[13], [14].

Table shows the result of reflection on the overall behavior that will be adopted by the

supervisor, according to the constraints defined [1].

Both areas are prohibited in this table, they are hatched.

The inference step will allow the definition of a coefficient to supervision Csupervisor, direct

image of the size of the desired voltage window. This coefficient will be defined by four membership

functions. These functions appear in the figure above [15], [16].



36

Fig.13: Fuzzy set defining the outlet supervisor

The SOM-PROD inference method was used, it enables development of more linear

response, in addition, this method can reduce simulation time in MATLAB.

9.3 Defuzzification

In order to calculate the size of the window of the desired voltage, fuzzy value Csupervisor must

be translated into numerical value. The method of the center of gravity is used. Figure 14 thus shows

the value of dimensional change of the coefficient of variation based on input measurements.

Fig.14: Three-dimensional representation of the evolution of the adjustment coefficient

Vdesired voltage values are defined by this factor as well as voltages Veligible.

9:;< _>?@2A?> B-./ 1'C@DE?AF2@*AG 1:HI _>?@2A?> B-23 1'C@DE?AF2@*AG 1 7K

The desired voltage window is an image of the coefficient Csupervisor provided. This voltage

window is based on measurements of the stress of voltage and reactive power injected.

10. SIMULATION OF THE CONTROL AUTO-ADAPTIVE

The auto-adaptive control will now be compared to the controls already tested at the

beginning, namely, the P/Q control and adaptive control. To do this, we'll use the same scenario

shown in Figure 6, with replacing the adaptive control by the auto-adaptive control system. That

integrates the function of selection of desired voltage, depending on measurements made at the point

of connection.

-1

-0.5

0

0.5

1

-1

-0.5

0

0.5

10.2

0.4

0.6

0.8

1

Reactive powerVoltage

Csuperv

isor



37

The results obtained are as follows:


c) Branch n°3

Fig.15: Evolution of the voltage at the connection point of the PV productions

The results presented in Figure 15 show that with this type of control, the voltages at the

connection points of the productions identified as critical in previous simulations, are maintained

near in the eligibility window.

The performance of auto-adaptive control is seed through changes of reactive power supplied

or absorbed by DP in Figure 16. The fact adapt intelligently the instructions forces all productions to

participate in control. And the constraints are better spread over the decentralized PV productions.

Fig.16: Evolution of reactive power at PV productions

0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Voltage [

pu]


0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Vo

ltage

[pu]

N8 Vmax Vmin

0 5 10 15 20 240.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Time [s]

Vo

ltage

[pu]


0 5 10 15 20 24-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Time [s]

Re

ac

tive

pow

er

[pu]

PN°1

PN°2

PN°3

PN°4

PN°5

0 5 10 15 20 24-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Time [s]

Rea

ctive

pow

er

[pu]

PN°6

PN°7

PN°8

PN°9



38

11. CONCLUSION

This paper shows the performance of the auto-adaptive voltage regulator and the interest of

its use in the case of high penetration of PV productions. On this scenario, the surge constraints are

erased and the regulator forces the participation of all of DP at an intelligently manner.

The integration of intelligence in control using fuzzy logic can achieve the conservation

objectives of the levels of voltage wave form without having communication system as well as

calculation of the independent instructions.

12. REFERENCE

[1] G.RAMI, "Auto-adaptive voltage control for decentralized energy productions connecting to

the electrical distribution grid", PhD thesis of ENSIEG, November 2006.

[2] [2] ABDELHAY.A.SALLAM, O.P.MALIK « Electric Distribution Systems », IEEE Press

Editorial Board, 2011.

[3] A.E.Kiprakis, A.R.Wallace, “Hybrid control of distributed generation of distributed

generators connected to weak rural grids to mitigate voltage variation”, CIRED, May 2003,

Barcelone.

[4] I.E.OTADUI "On the system of power electronics dedicated to the distribution electric

Application to Power Quality", PhD thesis of ENSIEG, November 2003.

[5] Tran-Quoc Tuan (IDEA), Bacha Seddik (G2elab), "Interactions PV inverter / Investigations

on the grid services provided by PV inverters" ADEME, in December 2011.

[6] T.Luong. LE "Dynamic Analysis of the distribution grid in the presence of decentralized

production" Doctoral Thesis Polytechnic Institute in Hanoi, January 2008.

[7] P.N.Vovos, A.E.Kiprakis, G.P.Harrison, J.R.Barrie, “Enhancement of grid capacity by

widespread intelligent generator control”, CIRED, Juin 2005, Turin.

[8] P.Y.Ekel, L.D.B.Terra, M.F.D.Junges, F.J.A.Oliviera, R.Kowaltschuk, T.Y.Taguti, “Fuzzy

logic in voltage and reactive power control in power systems”, in Proc. 1999 IEEE

International Conference on Control Applications, pp. 622-6627.

[9] P.Y.Ekel, L.D.B.Terra, M.F.D.Junges, F.J.A.Oliviera, A.Melek, T.Y.Taguti, “Fuzzy logic in

voltage and reactive power control in regulated and deregulated environments”,

2001IEEE/PES Transmission and Distribution Conference and Exposition.

[10] F.Chevrie, F.Guély, "Fuzzy logic" Cahier Technique Schneider Electric °191.

[11] H.Bühler, "Setting by fuzzy logic", polytechnic and university presses

romandes, 1994.

[12] A.V.Patel, “Simplest Fuzzy Controllers under Various Defuzzification Methods”,

International journal of computational cognition, vol. 3, No. 1, 21-32.

[13] G.Rami, "Modelling of decentralized production systems", Technical Paper GIEIDEA,

December 2004.

[14] G.Rami; T.Tran-Quoc, N.Hadjsaid, 2005, "Fuzzy logic supervision and control of distributed

generators", CIRED 2005, Turin.

[15] G.Rami, "Prospective study for the development of auto-adaptive controller," Technical

NoteGIEIDEA, February 2005.

[16] G.Rami, G.Verneau, L.Bernard, T.Tran-Quoc, N.Hadj said, "Regulations of dispersed energy

generation connected to the distribution grid," RIGE2006.

[17] Shubhangi Arbale and Rajesh M Holmukhe, “Monitoring and Analysis of Reliaibility of

Electrical Distribution System using Matlab – A Case Study”, International Journal of

Electrical Engineering & Technology (IJEET), Volume 4, Issue 2, 2013, pp. 330 - 337,

ISSN Print : 0976-6545, ISSN Online: 0976-6553.

VOLTAGE CONTROL BY FUZZY LOGIC OF THE PHOTOVOLTAIC PRODUCTIONS INTEGRATED IN THE HTA GRID

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