Energy and Power Engineering, 2013, 5, 458-462
doi:10.4236/epe.2013.54B088 Published Online July 2013 (http://www.scirp.org/journal/epe)
An Investigation of Scaled-FLC Using PSO for Multi-area
Power System Load Frequency Control
Aqeel S. Jaber1, A.Z. Ahmad1,2, Ahmed N Abdalla1
1Faculty of Electrical and Electronics Engineering, University Malaysia Pahang, Pekan, Malaysia
2Sustainable En.& Power Elec. Res. (SuPER) Group, University Malaysia Pahang, Pekan, Malaysia
Received April, 2013
ABSTRACT
Load Frequency Control (LFC) is one of power systems important requirements which maintain the zero steady-state
errors in the frequency changing and restoring the natural frequency to its normal position. Many problems are subject
to LFC such as suddenly large load or suddenly disconnecting generating unit by the protection device. In this paper,
multi-area Frequency Control by using the combination of PSO and fuzzy logic control (FLC) technique. PSO optimi-
zation method is used to tuning the fuzzy controller input and output gains. Four of an interconnected electrical power
system used as a testing the effectiveness of the proposed method compared to a conventional PI controller and
scaled-fuzzy controller. The simulation result has been shown that the controller can generate the best dynamic response
in multi-load conditions.
Keywords: Fuzzy Control; PSO; Load Frequency Control
1. Introduction
One of the major requirements in parallel operation of
interconnected power systems is the Load Frequency
Control (LFC) which is responsible for scheduled power
transfers between the interconnected areas at any distur-
bance in the case of the connecting or disconnecting ge-
nerating unit or suddenly large load. Various controllers
have been used in different areas could not efficiently
control the frequency and rather slow for the output re-
sponse due to fact of non-linearity in system components
[1-2], time invariant and governed by strong
cross-couplings of the input variables. Therefore, the
controllers have to be designed with taking into account
the nonlinearities and disturbances.
Many of control methodologies have been suggested
to solve LFC problem. Static Output Feedback gains and
Linear Matrix Inequality are the most efficient and effec-
tive tool which stabilizes the system which used to cal-
culate the gains of PID controller [3]. The Robust adap-
tive control also has been used to deal with the change in
a system parametric [4]. Optimization techniques have
been done to solve LFC, but they require information
about the entire system rather than local information [5].
Other control approaches such as PID-ANN, PI-fuzzy
and optimal control applied to LFC has been reported in
[6]. Using genetic algorithm to scale of PI fuzzy control-
ler in LFC has been reported in [7].
This paper presents the FLC using PI-fuzzy controllers.
The proposed controller is tuned using PSO to obtain the
controller gains in order to get an efficient fuzzy control
on four of an interconnected electrical power system.
This is a new approach to optimize the fuzzy controller
that differentiates to other's methods. The simulation
results are carried out in term frequency response for its
damping under different load conditions and compared it
to the effectiveness of proposed controllers with other
controllers. Simulation results show that the undershot
and settling times with the proposed controller are better
and guarantees robust performance under a wide range of
operating conditions.
2. Theoretical Background
Power systems have multi-variable and complex struc-
tures and consist of different control blocks and deal with
nonlinear and/or non-minimum phase systems [8]. Power
systems are divided into control areas connected by tie
lines and all generators are supposed to constitute a co-
herent group in each control area.
2.1. Load Frequency Control
The aim of LFC is to maintain real power balance in the
system through control of system frequency. Small
changes in real power are mainly dependent on changes
in rotor angle δ and, thus, the frequency f. whenever the

A. S. JABER ET AL. 459
real power demands changes, a frequency change occurs.
However, the change in angle δ is caused by momentary
change in generator speed. This frequency error is ampli-
fied, mixed and changed to a command signal which is
sent to turbine governor. The governor operates to restore
the balance between the input and output by changing the
turbine output. This method is also referred as Megawatt
frequency or Power-frequency (P-f) control [9].
2.2. Fuzzy Logic
According to many researchers, there are some reasons
which present popularity of fuzzy logic control such as
easily applied for most applications in industry. Besides,
it can deal with intrinsic uncertainties by changing the
controller parameters. On the other hand, their robustness
and reliability make fuzzy controllers useful in solving a
wide range of control problems [10]. The fuzzy control-
ler for the single input, single output type of systems is
shown in Figure 1 [7]. Fuzzy logic shows experience
and preference through its membership functions. These
functions have different shapes depending on system
experts’ experience [11].
2.3. PSO Algorithm
PSO was introduced by Eberhart and Kennedy as a new
heuristic method [12,13]. PSO was inspired by the food-
searching behaviours of fish and their activities or a flock
of birds. In D-dimensional search space. The best indi-
vidual position of particle i and the best position of the
entire swarm are represented by
(1)
(2)
where:-
Pi=(pi1, pi2,…, piD) and G=(g1, g2,…, gD), respec-
tively, ω is inertia weight parameter and c1, c2 is accel-
eration coefficients. In each iteration the particles will
using eq. 1&2 to update their position (xi) and velocity
(vi) [12].
3. Four Area LFC Model
The net power (ΔP) due to disturbance (ΔPD) is when the
changes in power generation. Where the ΔPG is described
as(3).
(3)
This change will absorbed by changing in kinetic en-
ergy (Wkin,) load consumption and export of power (ΔPtie)
so ΔP for ith area is as follows;
Figure 1. Fuzzy controller block diagram.
(4)
where, Di is power regulation and equal to ΔP/Δf. By
taking Laplace transformation
(5)
where, , (H) is inertia constant and (f) is the
frequency. If the line losses are neglected, the individual
ΔPtie ij can be written as
(6)
(7)
where, and δ is load an-
gle. Upon Laplace transforming (5), one gets
(8)
The transfer of generator turbine (Gtf) is written by
(9)
where, TT are turbine time constant and TG speed gover-
nor time constant. The parameters can be represented
such in the Figure 2.
From Figure 2, the bias factor (Bi ) is suitable value
can be computed as follows
(10)
ACEi, Ri are area control error, speed droop charac-
teristic of area (i) respectively.
Figures 3 & 4, show ith area block diagram of and
illustrate the tie line block diagram of interconnected
power system [10].
Figure 4 shows the method of interconnection be-
tween four areas that have been used in this paper.
Figure 2. LFC Model for one area of system.

A. S. JABER ET AL.
460
Figure 3. Model of tie-line power control are a.
Figure 4. Four-area interconnection power system.
4. Proposed Method
bership functions that are ad-
dium positive, SP: small positive, SN: small
ne
The boundary of the mem
justed based on expert in classical Fuzzy methods, per-
son’s experiences may be does not guarantee the sys-
tems’ performance. The boundaries of the membership
functions are tuned by PSO to select the best boundaries
by finding suitable gains (scaled fuzzy parameters) for
the inputs and output fuzzy controller. These gains obtain
by three parameters Gin1, Gin2 and Gout that shown in
Figure 5 are defined the uncertain range by PSO algo-
rithms. The fuzzy rule has been designed as in Table 1
hat based on the number of membership function from
the inputs and the output (as in Figure 7).The flow chart
of PSO algorithm to optimize the scaled fuzzy parame-
ters is shown in Figure 6.
where:
MP: me
gative, Z: zero and MN: medium negative.
Table 1. Fuzzy controller rules.
MP SP Z SN MN
MP MP SP SP Z Z
Sp SP SP Z Z NS
Z SP Z Z NS NS
SN Z Z NS NS MN
MN Z NS NS MN MN
Figure 6. Optimizing fuzzy parameter using PSO.
Figure 7. Member ship function for input & output of fu
5. Result and Discussion
ess was tested in order to
zzy
controller.
The proposed method effectiven
investigate the system performance by using the MAT-
LAB 7.1. Tables 2 &3, list all system parameter and tie
line parameter.
Figure 5. Scaled Fuzzy PI controller diagram.

A. S. JABER ET AL. 461
The scaled fuzzy type controller was designed and
co
re 10 and
Fi
mum KP and KI for PI control-
le
Table 2. Four area model parameters.
Area R TG T
T T P K P B
mpared with the classical fuzzy and PSO-PID for LFC
under system uncertainties (controller robustness) in
multi load conditions. The frequency response results are
shown in Figure 8 and Figure 9 respectively.
The tie power response is shown if Figu
gure 11 respectively.
Table 4, show the opti
rs parameters using PSO, and the optimum values of
the scaled fuzzy parameters that are computed by using
PSO algorithms is shown in Table 5.
1 2.4 0.08 0.030 20.08 1200.401
2 2.1 0.091 0.025 17.24 1110.3
3 2.9 0.072 0.044 22.97 1350.48
1 4 .9950.044 0.044 53.19 1060.391
Table 3. Tie Line Parameters.
T12 T13 34 T14 T23 T24
0.425 0.5 0.4 0.455 0.523 0.6
Figure 8. Frequency deviations of 30% load change.
Figure 10. Tie power transfer of 3% load change.
Figure 11. Tie power transfer of 5% load change.
Tabl4. PI controller values.
Area Kp Ki
1 0.51 0.631
2 0.432 0.551
3 0.552 0.681
4 0.601 0.61
Table 5. Scaled Fuzzy Parameters.
Area Gin1 Gin2 Gout
1 0.138 0.0. 074 0114
2 0.129 0.057 0.0960
3 0.139 0.0234 0.1181
4 0.039 0.128 0.1172
Table 6 shows for the frequency deviation of peak
un
ot, St (s): Settling times (s),
der shoot & and settling time for scaled fuzzy-PI con-
troller and conventional PI controller for each intercon-
nected power system area.
where: PUS: Peak undersho
Figure 9. Frequency deviations of 50% load change. L.Ch: %load change

A. S. JABER ET AL.
462
Response Comparison For Scaled Table 6. Frequency
Fuzzy-PI Controller And Conventional PI Controller.
PI controller PSO fuzzy controller
L.Ch
Ps)
US St (PUS S.t (s)
1 0. 0 0818 21.2 .004514.23
2 0.0233 23.1 0.0081 14.97
3 0.0361 23.7 0.0132 15.12
4 0.0472 24.5 0.0178 15.43
5 0.0605 24.81 0.0227 15.91
able 7. Power transfer response Comparison of scaled
oller
T
fuzzy-PI controller and conventional PI controller.
PI controller PSO fuzzy contr
L.Ch
PUS*1 US 0^-3 St (s) P St (s)
1 3 5.342 22.4 .32117.14
2 10.938 23.2 6.712 18.52
3 16.311 23.7 9.131 18.79
4 22.331 23.9 12.342 19.31
5 27.211 24.8 16.201 20.54
Table 7 shows for the total power transfer deviation of
pe
d method performance is
de
ak under shoot & and settling time for scaled fuzzy-PI
controller and conventional PI controller for each inter-
connected power system area.
The robustness of the propose
monstrated based on ITAE that is under step change in
the different demands as
1
Finally, from tables (6,7) and figures (8 to 11) of
ch
6. Conclusions
ces PSO-FLC to improve the
step
ange, the scaled Fuzzy controller has better perform-
ance than the optimized PI controller at all operating
conditions. Therefore, the performance comparison be-
tween both controllers indicates that the frequency re-
sponse of the proposed method has smaller undershoot
and shorter in settling time with respect to PI controller.
This paper introduper-
formance of four-area power system and the linearization
in errors is considered as parametric uncertainties. Each
area consists of the turbine, governor and power system
which modelled by first-order transfer functions. In addi-
tion, PSO was used to adjust the input and the output of
FLC memberships. Simulation results proved that the
proposed scaled FLC has obtained fast response and less
undershoots compared to conventional PI controller.
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