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Load Frequency Control of Two Area
Interconnected Power System Using Fuzzy
Logic Control and PID Controller
Mehmet Rida Tur
Selim Ay
Abdulfetah Shobole
Mohammed Wadi
Electrical Engineering
Engineering Department,
Electrical and Energy
Engineering Department,
Department, Yildiz Technical
I.Sabahettin Zaim Uni
I.Sabahettin Zaim Uni.,
Department, Mardin Artuklu
University, Istanbul, Turkey,
Istanbul,Turkey,
Istanbul,Turkey,
University, 47500 Mardin,
selimay@yildiz.edu.tr
Turkey, ridatr@gmail.com mohammed.wadi@izu.edu.tr, abdulfetah.shobole@izu.edu.tr
Abstract— Demand for electricity is constantly increasing
day by day. The biggest challenge is to provide uninterrupted
and high-quality electricity to customers in variable conditions.
To accomplish this, the two parameters must always be
checked for each condition; these parameters are load
distribution and network frequency. In this study, fuzzy logic
and Proportional Integral Derivative controller method is
applied in power system connected with two different fields to
remove the fluctuations of electric energy. For this, MATLAB
offers simulation applications and models in Simulink. Both
different control directions are compared according to the
results obtained. To emphasize the effectiveness of the
proposed fuzzy control system, two field-connected power
systems were simulated in four scenarios in a wide range of
operating conditions. In addition to all these, the performance
of the proposed method is compared with a Proportional
Integral Derivative tester with simulation studies and the
results show the effectiveness and superiority of the proposed
approach.
Index Terms— fuzzy systems, frequency control, matlab,
power system control, proportional control.
I. INTRODUCTION
Recently electricity generation has gained even more
importance with the growing demand and environmental
awareness. Besides, natural expectations of producers and
consumers seek to use electrical energy in the most efficient
way. Therefore, interconnected electrical power systems
were formed in order to meet the needs of energy that are
for both suppliers and consumers. Furthermore,
interconnected electrical power systems have been created
not only for domestic consumers within the countries but
also with its neighboring countries to exchange electric
energy for any circumstances between each other [1].
Some number of arrangements and adjustments are
required to the linking up interconnected electrical power
systems for both within the countries and between the
countries. All sub-systems that connect to whole system
must be stable internally and the overall system frequencies
that can be controlled should be same with the each other
[2,3]. In interconnected electrical power systems, the
nominal frequency level is basically dependent on the
balance of active power consumed and consumed. The
active power imbalance in the system is recognized by the
differences on the frequency variation in the whole system.
Nonetheless, considering the industrial loads those connect
to interconnect electrical power system depend on quality
electrical energy substantially, the steady state frequency
,&5(5$
error in the system must be maintained between the
acceptable values [4]. Active power balance of the
interconnected system is provided with making sensitive to
the system frequency [5-7].
In the normal situation, the difference between the power
output and instantaneous load change of synchronous
generators in the system changes the nominal frequency. If
the amount of power produced in the power systems is
greater than the requested power, the operating speed of the
generators increases and therefore the frequency increases.
Likewise, if the amount of production on my site is lower
than the requested power, the system frequency is reduced.
In the event of a trip, the frequency of the generators is
controlled again to bring the frequency back to the desired
level. To do this, the power and frequency generated in the
power system must be set for every minute, depending on
the changing consumption referred to as the load frequency
control (LFC) model. [8, 9]. In some studies, the robustness
of the IMC-based PI Controllers was verified by changing
the parameters of the microgrid sources. The 2-proportional
integral derivative (PID) and two-degree of freedom
controller also performs satisfactorily for random load
perturbation and random wind generator input. [10-12]. In
addition, an additional controller function has been proposed
for a new modeling Interline Power Flow Controller to
dampen low frequency oscillations by considering these four
alternative damping controllers [13].
In this study, fuzzy logic control and Proportional Integral
Derivative (PID) control perspectives have been applied in
two field coupled power system with variable load
frequency control model to eliminate the frequency
fluctuations experienced in electric energy.
II. MODEL OF POWER SYSTEMS
Interconnected systems consist of production areas where
are compatible with each other. In such a system, under
normal working conditions, the nominal value of the system
frequency towards the load changes and power of between
the production areas that is determined by agreement are
kept constant defined as load frequency control. The load
frequency control operation is carried out in three stages
called primary, secondary and tertiary control [14, 15]. PI
has proposed an implementation of two strategies for this
and has been implemented to speed up and control [16].
Figure 1 normally creates a simplified schematic of the
paper frame to control the frequency. This arrangement
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contains three relevant parts of the control. These parts are
named Tertiary, Secondary and Primary controlled [17]. In
large systems connected to each other, there are three
control operations. Secondary control in small isolated
system.
In the simplest form, demand side management actions
that use frequency regulation are not included in this chart,
but can be evaluated without conceptual changes.
Primary control
(aoutomatic)
+
+
-
f
Governor
Pwanted
+
Interconnecte
d network
F
(common
frequency)
Pproduced
Generator
+
+
+
Ptertiary control
Tertiary control
(managed by
Transmission
System Operator
(TSO))
In Figure 3, the main components of the LFC for a single
field power system are shown as flow charts. Single area
representation equations matrix in details can be found in
[14,15].
Steady-state error that occurs at the frequency can be
minimized with the activation of a secondary control loop
which contains controller. This process of load frequency
characteristics is obtained by bringing the desired position
as indicated in Figure 4.
fnominal
Pprimer control
Pscheduled Pdispatched
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Psecondary control
f
Ptie lines
LFC
(Load
Frequency
Control)
Figure 3: An example of single area power control system
ftarget
Psched uled tie lines
Secondary control (managed directly by TSO)
Figure 1. Frequency stabilization system.
Three types of reserves can be defined for frequency
control as follows [18].
• Primary Control Reserve: An automatic control backup
used by the system. It provides a power backup against
any frequency variation with the governor to keep the
falling frequency level in place.
• Secondary Control Reserve: Provides reserve power to
restore the frequency level of the primary backup to the
nominal value.
• Tertiary Control Reserve: if the secondary reserve is
not sufficient to restore the frequency to its nominal
value, the frequency is used to return to the nominal
level to support the secondary reserve.
As can be seen from Figure 2, changes in the operating
frequency and load of the system are always inversely
proportional [19]. Any increase in current will reduce the
operating frequency of the system. This conversion in the
frequency is detected by the regulator in the primary control
loop, then the speed of rotation of the turbine and thus the
amount of production is increased to the nominal level of
frequency. Nonetheless, as a result of the primary control,
each production area to meet the total load demand increases
their own capacity proportionately with the amount of
production without being bound to international agreements
because of nothing make production control on the system.
Each production area in the interconnected system is
envisaged to meet own load without power transfer with
adjacent zone due to the fact that it is undesirable [20].
As can be seen in Figure 4, while f0 nominal value of
system frequency is stationary at the P0 MW load demand,
load that increases to Pi MW is met by the primary control
and system frequency is maintained at with a certain error
according to the nominal frequency by providing active
power balance.
After a while, characteristic is moved from "a point" to "d
point" with activation of secondary control and consequently
system frequency is brought back to its nominal value which
is f0. In addition to this, production control is also performed
by feeding back to the secondary controller with connection
line load sharing which depends on the agreement between
production areas in the interconnected system is provided at
the same time.
An interconnection line between two areas loadfrequency control systems should be designated in order to
observe deviations both inside the system and adjacent
zones [21]. This situation can be seen from Figure 5.
f (Hz)
f0
d
c
b
a
f1
P (MW)
P0
P1
Figure 4: Charge frequency control characteristic of two areas [22]
f (Hz)
f0
f1
P (MW)
P0
P1
Figure 5: Load frequency control systems for two areas
Figure 2: Variation of load-frequency characteristic
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Equations of two areas load frequency control system can
be given as follows [23]:
1
1
1
PG1 +
f1 +
u1
TG1
R1TG1
TG1
1
1
PT 1 =
PG1 +
PT 1
TT 1
TT 1
PG1 =
f1 =
K P1
1
K
K
PT 1 +
f1 P1 Ptie P1 Pd 1
TP1
TP1
TP1
TP1
Ptie = T12 f 1 T12 f 2
(1)
(2)
(3)
(4)
PG 2 =
1
1
1
PG 2 +
f 2 +
u2
TG 2
R2TG 2
TG 2
(5)
PT 2 =
1
1
PG 2 +
PT 2
TT 2
TT 2
(6)
f2 =
KP2
1
K
K
PT 2 +
f 2 12 P 2 Ptie P 2 Pd 2
TP 2
TP 2
TP 2
TP 2
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There are ways to a more literary power system model with
ways to consider physical constraints such as thermal
turbine production speed restriction, speed regulator dead
band and time delay [27]. In some studies, control is
planned for a four-zone interconnected power system with
control dead bands and production rate constraints [28, 29]
However, controller in this paper consists of two inputs.
The first input that represents as variable ACE is the area
control error as shown in Figure 7, and the second input
called variable (ACE) is the change of area control error as
shown in Figure 8; while the output (Freq-Level) indicates
the level of change in frequency due to increasing or
decreasing in the load as depicted in Figure 9.
The basic design of the fuzzy controller requires three
steps as shown in Figure 6.
(7)
where;
Figure 6: Main steps of fuzzy control
The input variables are determined by assigning a single
fuzzy set, a set with membership function (A), and a zero at
another location. If the output variable is a fuzzy set, the
maximum min and fuzzy relation with the composition
expresses the desired control action. The fuzzy set of the
output variable is solved by blurring to obtain a clear
numerical value by the centroid method. The fuzzy rule
base, as shown in equation 8, consists of a collection of form
based on the fuzzy base rule IF-THEN principle.
R(k):IF x1 and x2 is Fk, THEN y1 is Gk, for k=1,2,..n
III. FUZZY LOGIC CONTROLLER
In this article, the basis of the selection of the Mamdani
method; There are many features for the use of the fuzzy
logic controller [24]. The first of these uses fuzzy logic to
control complex non-logical systems without performing
mathematical analysis for them. Secondly, if there is any
change in practice in power systems, we do not need to start
from the first step. However, in this preference, some
member functions and some rule bases can be added and
removed. Combinations can also be made with conventional
techniques to simplify fuzzy logic applications.
Automatic production control aims at planning the power
flow and balancing the frequency independent of the load
changes in interconnected systems. The link line is
calculated according to the reference values of the Area
Control Error (ACE), frequency and link power, which is a
linear combination of power disagreement and frequency
deviations [25]. There are ways to a more literary power
system model with ways to consider physical constraints
such as the constraint on the speed of thermal turbine
production, speed regulator dead band and time delay [26].
,&5(5$
(8)
Where,
x1, x2,… . xn U and y1 R are the inputs and output of
fuzzy sets in U1, U2 and R representing the kth antecedent
pairs and conclusion pair respectively.
While, Fk =U1U2….. Ui, Gk =R1R2….. Ri, and
n is the numbers of rules. Table 1 represents all of rule bases
of proposed algorithm, the range -1 to 1 is divided into
seven-member functions in order to increase the stability
and accuracy of fuzzy controller. The obtained results in
Tables 3-6 shows the superiority and the applicability of
fuzzy logic controllers.
Figure 7: The Area control error for the first input
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change in an interconnected two areas power system with
same parameters. The load is increased by 200 MW (0.2
p.u) at two areas. Figure 10 illustrates the implemented two
area control system schemes within Simulink/MATLAB.
Furthermore, Block diagram in Figure 10 based on both of
equations (1-7) and FLC implemented by FIS toolbox.
After performed simulation, the following results are
obtained as shown in Table 3 and system responses of PID
and fuzzy controllers can be seen in Figure 11 and Figure 12
respectively.
Figure 8: The change area control error (ACE) for the second input
TABLE I: RULE BASES OF PROPOSED METHOD
HN: High Negative, MN: Medium Negative, LN: Low Negative, Z: Zero,
HP: High Positive, MP: Medium Positive, LP: Low Positive
Figure 10: Applied two area control system scheme
TABLE III: RESULTS OF THE FIRST SCENARIO
Controller
PID
Fuzzy
(ts)
20
10
(O.S%)
1.8
0.7
SSE
0
0
Oscillation
high
low
Figure 9: The change in frequency level for output
IV. SIMULATION RESULTS
In this article, basically a comparison between PID and
fuzzy controllers is presented in four different scenarios.
The comparison of PID and fuzzy will be in the most
important factors from which we can determine the best
controller such as settling time (ts), maximum over shoot
(O.S%) oscillation and steady state error (SSE) as will be
confirmed in different simulated scenarios. In addition, to
distinguish between the controllers, we will solve the
following numerical example by using FIS toolbox and
Simulink in MATLAB.
Figure 11: The first scenario response of LFC with using PID controller
TABLE II: PROPOSED NUMERICAL EXAMPLE [30]
Area and Parameters
Area 1
Area 2
0.05
0.0625
0.6
0.9
5
4
1000
1000
Governor Time Constant (g) sec
0.2
0.3
Turbine Time Constant (T) sec
0.5
0.6
Speed Regulation (R)
Frequency Sens. Load Coeff. (D)
Inertia Constant (H)
Base Power (SBASE)MVA
1.
FIRST SCENARIO
According to the first scenario, response of LFC to a load
,&5(5$
Figure 12: The first scenario response of LFC with using fuzzy controller
2.
SECOND SCENARIO
According to the second scenario, response of LFC to a
load change in an interconnected two areas power system
with different parameters. The load is increased by 200 MW
(0.2 p.u) at the first area.
After performed simulation, the following results are
obtained as shown in Table 4 and system responses of PID
and fuzzy controllers can be seen in Figure 13 and Figure 14
respectively.
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TABLE IV: RESULTS OF THE SECOND SCENARIO
Controller
PID
Fuzzy
(ts)
22
20
(O.S%)
1.5
0.7
SSE
0
0
Oscillation
high
low
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controlled by PID while the second area with fuzzy
controller.
TABLE VI: RESULTS OF THE FOURTH SCENARIO
Controller
PID
Fuzzy
(ts)
20
9
(O.S%)
1.3
0.8
SSE
0
0
Oscillation
high
low
After performed simulation, the following results are
obtained as shown in Table 6 and system responses of PID
and fuzzy controllers can be seen in Figure 16.
Figure 13: The second scenario response of LFC with using PID controller
Figure 16: The fourth scenario response of LFC with using PID and fuzzy
controller
Figure 14: The second scenario response of LFC with using fuzzy
controller
3.
THIRD SCENARIO
According to the third scenario, response of LFC to a load
change in an interconnected two areas power system with
same parameters. The load is increased by 200 MW (0.2
p.u) at the first area. However, the first area is controlled by
PID while the second area with fuzzy controller.
After performed simulation, the following results are
obtained as demonstrated in Table 5 and system responses
of PID and fuzzy controllers can be seen in Figure 15. By
investigating Figure 15, it can be noticed that FLC ts is half
of PID one. Also, FLC O.S is 10 times less than PID
controller.
TABLE V: RESULTS OF THE THIRD SCENARIO
Controller
PID
Fuzzy
(ts)
20
11
(O.S%)
1.5
0.15
SSE
0
0
Oscillation
high
low
As seen from Table 7, we can conclude that Fuzzy
controller provide better performance and response than
PID. Fuzzy controller offers more desirable settling time (ts)
which is in general less than PID by % 50 as well as fuzzy
presents less over shoot (O.S%) than PID by % 55. In
addition, the oscillation of Fuzzy controller is less and more
appropriate for load frequency control than PID.
TABLE VII: RESULTS FOR ALL SCENARIOS
Scenario
No
First
Second
Third
Fourth
-
-
Figure 15: The third scenario response of LFC with using PID and fuzzy
controller
4.
FOURTH SCENARIO
According to the fourth scenario, response of LFC to a
load change in an interconnected two areas power system
with different parameters. The load is increased by 200 MW
(0.2 p.u) at the two areas. However, the first area is
,&5(5$
Controller
(ts)
(O.S%)
SSE
Oscillation
PID
Fuzzy
PID
Fuzzy
PID
Fuzzy
PID
Fuzzy
20
10
22
20
20
11
20
9
1.8
0.7
1.5
0.7
1.5
0.15
1.3
0.8
0
0
0
0
0
0
0
0
high
low
high
low
high
low
high
low
First Scenario: both of two interconnected areas with
same parameters using PID and FLC.
Second scenario: both of two interconnected areas
with different parameters using PID and FLC.
Third Scenario: two interconnected areas with same
parameters, the first area is controlled by PID while
the second area with fuzzy controller.
Fourth Scenario: two interconnected areas with
different parameters, the first area is controlled by
PID while the second area with fuzzy controller.
Based on the obtained results by four Scenarios, it can be
said that both of controllers can work independently or
parallels in the presence of huge load fluctuations. However,
in terms of robustness and stability, fuzzy based controller is
more robust and stable.
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V. CONCLUSION
In this page, two very advanced methods were used
instead of the governor control system which made the load
frequency control. The methods used are PID and fuzzy
logic which are widely used in control. PID and fuzzy logic
are commonly used methods in control systems. In practice,
the performance of these controls was compared with the
different scenarios in the two area interconnected systems.
The aim of research is to show a comparison between PID
and fuzzy controllers in terms of performance aspect on the
two areas interconnected power systems which are
investigated with four different scenarios. With respect to
four different simulation results, performance of fuzzy
controller is proved that fuzzy approach for controlling the
two area interconnected power systems is better than PID
based applications. Moreover, fuzzy controllers have many
advantages over the later one such as less settling time (ts),
less maximum over shoot (O.S%) and low oscillation where
can be seen from the results of four different scenarios with
related figures.
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