Optimal control of load frequency control power system based on particle swarm optimization technique

The International Journal Of Engineering And Science (IJES)
|| Volume || 5 || Issue || 9 || Pages || PP 67-72 || 2016 ||
ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
www.theijes.com The IJES Page 67
Optimal control of load frequency control power system based on
particle swarm optimization technique
Ahmed Jasim Sultan
Department of Electrical Power Engineering Techniques, College of Electrical Engineering Techniques,
Middle Technical University
--------------------------------------------------------ABSTRACT-----------------------------------------------------------
In this work, PSO is proposed to set the gains of PID controller for LFC in single power systems area. This
work has very significant issue because of persistent and random change load through working of power
system. The proposed algorithm offer fluent performance, stable, and fast convergence to target value.
Simulation results using MATLAB R2015a demonstrate that the proposed controller has more efficient of
dynamic performance, better convergence, fast response from the other methods depend on rise and settling
time of frequency deviation.
Keywords: LFC, single area power system, PSO, PID controller
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Date of Submission: 17 May 2016 Date of Accepted: 03 October 2016
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I. INTRODUCTION
The substantial development of universally led to increasing demand of electric power. For normal operation of
power system, the frequency should be constant with specific limits. Hence, the LFC has essential role to control
real power output of generating unit. The objective of the LFC is preserving zero error steady state of frequency
deviation [1]. Classical LFC used an integral control that had limits dynamic response. Therefore, to improve the
power system stability must be enhance the control loop of system. Traditional PID controller is widely used to
control almost process loops of industrial systems due to their simple structure and reliable [2]. The PID
parameters (Kp, Kd, and Ki) should be accurately tuned. In literatures, many methods have been sophisticated to
tune these parameters like Fuzzy logic system, evolutionary algorithms, and Neural Network … etc. In [3] the
authors present a PID controller tuning by two artificial algorithms, GA and PSO to improve LFC response. In
[4] the authors proposed a PSO and BFO for LFC to boost the power systems stability.
This paper presents a PSO algorithm to tune optimal PID parameters of single area load frequency controller.
This includes Integral absolute error (IAE), integral square error (ISE), and integral of time multiplied by square
error (ITSE) which ITSE was experimentally better than the other based on maximum overshoot, rise, and
settling time.
II. MATHEMATICAL MODEL OF LFC
The schematic diagram of LFC is illustrated in figure (1). It consists of three sections; governor, turbine, and
rotor inertia & load with feedback of speed regulation [5]:
 The Governor is:
(1)
 The Turbine is:
(2)
The Rotor inertia and Load is:
(3)
is the droop characteristics.
Where:
Load disturbance (p.u. MW)

Preparation of Papers for the International Journal of Engineering and Science
Time constant of governor (sec)
Time constant of turbine (sec)
= 2H: Time constant of rotor inertia (sec) and H: Inertia constant
Gain of electric power system
Governor speed regulation (Hz/p.u. MW)
Is a percent change in load divided by percent change in frequency (pu MW/Hz)
III. PARTICLE SWARM OPTIMIZATION
The first American electrical engineer Eberhart and psychologist Kennedy developed a PSO algorithm depend
on similarity of swarm of bird and fish pool [6]. In PSO the system is initialized the swarm assigning random
position and searches for optimal location by update of generations. Each particle is flying out of the problem
search space by following the current optimum particles. The velocity of each particle can be modified as
follows:
(4)
(5)
In order to increase convergence of algorithm, linearly decreasing inertia weight W function is used to enhance
the efficiency and performance of PSO as following [7]:
(6)
Where:
Velocity of particle I of iteration k.
Initial weight
Final weight
Maximum iteration
Current iteration
Random number between 0 and 1.
Acceleration constant.
Current searching point.
Modified searching point.
Best position of the ith particle.
The index of best particle among the entire particle in the population.
IV. DESIGN OF THE PROPOSED CONTROLLER
The proposed algorithm is used to set the PID parameter and enclose the better dynamic performance in LFC.
The structure of the PID – PSO is shown in figure (2).
∆𝒇
+
+
Rotor inertia
and load
Governor Turbine
∆𝑷 𝑳
Droop characteristics
𝒖
Figure (1) Block diagram of single area power system
Figure (2) The proposed PID – PSO

The accurate setting of PID parameters can get better response. Therefore, the performance criteria has
important role to realize it. The most common performance indices are shown in Table (1) [8], a minimization
fitness function is selected as follows:
(7)
Table (1) Mathematical description of different performance criteria
These performance indices have an advantage and disadvantage. The disadvantage of the IAE and ISE
indices is short overshoot but long settling time. ITSE index can cope of this disadvantage but it is complex and
requires time to its analytical formula [9]. The proposed algorithm flowchart is illustrated in figure (3) and the
combining of LFC with PID controller are demonstrated in figure (4).
Figure (3) Flowchart of the proposed controller

V. SIMULATION RESULTS
The traditional LFC is designed with PI controllers that have a limit transient response comparison with other
controller methods. The proposed controller is used to control the frequency deviation in LFC. The single area
parameters are given in appendix A and the PSO algorithm parameters are set as follows:
• Population size: 80
• Maximum iteration: 100
• Acceleration factors C1 & C2: 1.2 and 1.4 respectively
• Wmax and Wmin: 0.9 and 0.4 respectively that be found experimentally to get excellent Convergence
of algorithm.
• Search space of each particle: 0 to +100
Each particle set of the PID parameters and will search for their optimal value in three dimensional search space
P, I, and D. So as to confirm the efficient of the proposed controller to control the steady state frequency
deviation is tested. The simulation result shown in figure (5) is achieved using multi performance indices in PSO
algorithm. It can be obviously seem that the performance index ITSE has lowest rise and settling time as listed in
table (2).
TABLE (2) Step performance of LFC
# Rise time (sec) Settling time (sec)
PID-IAE 0.004 5.3668
PID-ISE 0.009 7.4138
PID-ITSE 0.0012 1.02
Figure (4) The structure of LFC including PID controller
Figure (5) Step response of LFC with different performance indices

To emphasize the robustness of the proposed controller, a comparison is made with the traditional PI controller
as shown in figure (6) and different control scheme methods used by researchers as listed in table (3).
TABLE (3) Comparison of different control scheme
#
Rise time
(sec)
Settling time
(sec)
Traditional PI controller 0.3152 10
GA-PID [2] ---- 2.18
PSO-PID [2] ---- 2.39
PSO-PID [3] ---- 5.91
BFO-PID [3] ---- 5.08
PSO-PID 0.0012 1.02
Figure (7) demonstrate the ability of the proposed controller to damp the frequency oscillation when 10% step
change increase in power demand depends on rise and settling time.
Figure (6) Step response of LFC
Figure (7) 10% step change response of LFC

VI. CONCLUSION
In this work, the design of PSO algorithm is present to set optimal value of PID controller. The frequency
deviation of LFC is controlled by PSO-PID controller. Through simulation results, the proposed controller can
perform an accurate PID parameters set than the other methods and can improve the dynamic performance
through damping oscillation of frequency deviation in LFC.
REFERENCES
[1]. Kundur P., Power system stability and control, (McGraw-Hill; 1994).
[2]. Y W., R Z., and C W., Robust load frequency controller design for power system, IEE Proceedings C. Generation, Transmission
and Distribution, 140(1), 1993, 11-16.
[3]. Saadat H., Power system analysis (McGraw-Hill; 1999).
[4]. N. M., M. K. and K. C., Performance analysis of load frequency control in single area power system using GA and PSO based PID
controller, International journal of electrical, electronics and computer engineering 2(1), 2013, 108-114.
[5]. H. M. S., W. I. I. and N. R. H. A., Optimal load frequency control in single area power system using PID controller based on
Bacterial foraging and Particle swarm optimization, ARPN journal of engineering and applied sciences, 10(22), 2015, 10733-
10739.
[6]. J. Kennedy and R. C. E., Swarm Intelligence (2nd ed., Morgan Kaufmann, 2001).
[7]. J. C. B., P. K. S., M. S., A. V., Sh. S. J., A. A., Inertia weight strategies in particle swarm optimization, 3rd
IEEE conf. on Nature
and Biologically Inspired Computing (NaBIC), Spain, 2011.
[8]. L. J., PID controller tuning using evolutionary programing, conf. on American control, VA, 2001.
[9]. Z. L. G., A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System, IEEE Transaction on
Energy Conversion, 19(2), 2004.
APPENDIX
The nominal parameter values of the single area power system are:
0.2 sec
0.5 sec
10 sec
1
0.05 Hz/pu MW
0.8 pu MW/Hz

Optimal control of load frequency control power system based on particle swarm optimization technique

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