Comprehensive Approach Towards Modelling and Simulation of Single Area Power Generation System using PI Control and Stability Solution using Linear Quadratic Regulator

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1677
Comprehensive Approach towards Modelling and Simulation of
Single Area Power Generation System Using PI Control and
Stability Solution Using Linear Quadratic Regulator
,
1Graduate Student, Dept. of Electrical Engineering, Rochester Institute of Technology, Dubai, UAE
2Professor, Dept. of Electrical Engineering, Rochester Institute of Technology, Dubai, UAE
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In this paper, the open loop single area power
generation system is modelled using state space
representation. The output response which is frequency
deviation at steady state is simulated using MATLAB. Then,
Proportional Integral (PI) controller is added to the system
to understand the effect of a conventional controller on
system steady state output response. The controlled system
is stabilized through design of Linear Quadratic Regulator
(LQR). The performance of system steady state output
response is measured in terms of undershoot percentage,
settling time, and steady state error. The controlled system
simulation at the end of this paper shows that PI control is
an efficient, reliable, and robust technique to solve power
generation system optimization problem. The output
response of the considered controlled system has settling
time of 0.7 second, zero steady state error, and undershoot
of 5.45%.
Key Words: Optimization, Single Area Power
Generation System, LQR Technique, PI Controller,
Steady State Response
1 INTRODUCTION
An interconnected system called Automatic Generation
Control (AGC) consists of two sub-systems: Load
Frequency Control (LFC) and Automatic Voltage Regulator
(AVR). AVR is responsible to regulate the terminal voltage
and LFC is employed to control the system frequency. In
this paper, modelling and simulation of LFC is considered
for careful analysis since LFC is more sensitive to changes
in load compared to AVR. LFC and AVR are decoupled and
can be analyzed separately. There is only weak overlap of
effect between the two sub-systems [1].
Optimizing thermal power generation system will reduce
energy or fuel consumption. Fuel reduction of even a small
percentage will lead to large energy saving which results
into saving the environment [2]. Hence, many researchers
have been interested to solve optimization problem in
thermal power generation systems. In order to optimize
power generation system, the plant has to operate at
desired operating level which corresponds to operating at
nominal frequency.
2 OPEN LOOP ANALYSIS
Figure 1 shows the SIMULINK generated block diagram of
an uncontrolled generating unit which consists of a speed
governor, a turbine, a re-heat, and a generator [1]. The
inputs of the system are Δ representing the change in
speed generation by utility and Δ representing the
change in load by consumer also known as disturbance.
Since user has no control over load changes, Δ is
considered the only input of the system. The effect of Δ
will be disappeared when a controller is added to the
system.
The fact that the frequency changes with load generation
imbalance gives an accurate way to regulate the
imbalance. Hence, frequency deviation Δf is considered as
a regulation signal to study the system performance. The
output of LFC is Δf which represents the change or
variation in steady state frequency. The objective is to
have a constant output frequency which corresponds to Δf
being zero or very small. The value of Speed Regulation R
also known as Droop is the ratio of frequency deviation
(Δf) to change in power output of the generator. Table 1
shows the constants used for single area power system in
Figure 1.
Table -1: List of Constants
Symbol Description Value
Governor Time Constant 0.4sec
Turbine Time Constant 0.5sec
Re-heat Time Constant 10sec
Generator Time Constant 20sec
Re-heat Gain Coefficient 0.5
Generator Gain Coefficient 125
R Speed Regulation 2.5 HZ/p.u MW

Fig -1: Block Diagram Representation of Single Area Power Generating Unit
The output of each integrator in Figure 1 is a state
variable. Hence, state variable matrix A must be a 4x4
matrix. Equations 1-4 show the developed transfer
functions:
Generator = (1)
Re-heat = (2)
Turbine = (3)
Governor = (4)
To develop state space representation of the system
shown in Figure 1, rate of change of each state variable is
needed. Therefore, Inverse Laplace Transform of the
transfer functions shown in Equations 1-4 is taken and the
equations are re-arranged into equations 5-8:
̇ = + - Δ (5)
̇ = + + (6)
̇ = + (7)
̇ = - (8)
Then, Equations 5-8 are transformed into state space
model. It is important to note that the output of the system
Δ is the state variable . Hence, the output matrix C will
be a row matrix of size 1x4. The input matrix B is a matrix
of size 4x1. State Space representation of any system
follows the following structure:
̇ =A x(t) + B Δ (9)
y(t)= C x(t) (10)
Following is state space representation of LFC shown in
Figure 1:
A=





































gg
tt
rt
r
rt
r
r
p
p
p
TRT
TT
TT
K
TT
K
T
T
K
T
1
00
1
11
00
1
1
1
0
00
1
B=
















0
0
0
p
p
T
K
C=  0001
Figure 2 illustrates the output response of the LFC model
generated in MATLAB. The input Δ is a unit step
function. The output response begins with oscillations and
dampens at steady state. Since there is increase in load,
undershoot is expected. Increase in load leads to decrease
in frequency which corresponds to undershoot. Decrease
in load leads to increase in frequency which corresponds
to overshoot. The settling time of the system is 150
seconds and the steady state value is -2.5 HZ. The
undershoot percentage, settling time, and the steady state
value are significantly large and this leads to the necessity
of having a controller added to the system.
Fig-2: Open Loop Output Response

3 CONVETIONAL CONTROLLERS
The primary objective of having a controller in power
system is to eliminate the steady state frequency
deviation. In any reliable power system, following
specifications are expected to be met:
1. Steady state frequency error should not be more
than 0.01HZ.
2. Settling time should be less than 1 second.
3. The maximum undershoot should not be more
than 6% which corresponds to transient
frequency of 0.06HZ.
Each controller has different role. Proportional controller
is used to reduce rise time and settling time. Integral
controller is used to eliminate steady state error. The
negative effect of integral controller is creating oscillation.
Derivative controller is used to improve transient
response which means reducing overshoot/undershoot.
Equations 11-15 show structure of most commonly used
conventional controllers where U(s) is the controller
output and E(s) is the controller input.
Proportional (P): U(s) = E(s) (11)
Integral (I): U(s) = E(s) (12)
Derivative (D): U(s) = S) E(s) (13)
PI: U(s) = E(s) (14)
PID: U(s) = E(s) (15)
Area Control Error (ACE) is the difference between actual
power flow out of area and scheduled power flow. Ideally,
the main objective in optimization problem is to improve
the dynamic response of the system by minimizing or even
eliminating AEC. In other words, the main objective is to
lead each utility to constantly change its generation to
follow the ACE. In real life power systems, it is rare to have
no ACE due to instantaneous change in load. Hence, the
objective is to keep AEC as close to zero as possible.
Integral control is well suited in this purpose.
4 FEEDBACK ANALYSIS
For single area closed loop system, a Proportional Integral
(PI) controller is added to the system as shown in Figure 3.
Equation 14 shows the transfer function of the PI
controller used in this section.
The system shown in Figure 3 has 5 integral blocks which
corresponds to 5 state variables. Equations 16-20 show
the transfer functions of each integral block in Figure 3
where is the proportional constant and is the
integral constant.
Generator = (16)
Re-Heat = (17)
Turbine = (18)
Governor = (19)
PI Control = (20)
Inverse Laplace Transform of Equations 16-20 are taken
and the equations are re-arranged into differential
Equations 21-25 to find rate of change of each state
variable.
̇ = + - Δ (21)
̇ = + + (22)
̇ = + (23)
̇ = - - (24)
̇ = ( + - Δ (25)
Fig- 3: Block Diagram Representation of Feedback Single Area Power Generating Unit
Next, Equations 21-25 are transformed into state space
model. The only input of the system is Δ which is a unit
step function. Following is state space representation of
LFC shown in Figure 3:

=




















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







000
11
00
1
0
11
00
0)1(
11
0
000
1

p
pg
p
g
i
ggg
tt
tr
r
t
r
rr
p
p
p
T
KK
T
K
K
TTRT
TT
TT
K
T
K
TT
T
K
T
=
























p
pg
p
p
T
KK
T
K
0
0
0
= 00001
PI tuning is a challenging task as the parameters of the
controller need to be changed until the desired
requirements are met. Table 2 shows the nominal values
of the PI controller used in Figure 3.
Table 2: Controller Parameters for Feedback Single Area
Generating Unit
Type of
Controller
Integral
Constant (
Proportional Constant
PI 2.85 6
5 DESIGN OF LINEAR QUADRATIC
REGULATOR
When a controller is added to a system, a new pole is in
fact added to the system which may cause the system to
become unstable. This means a stabilizing technique is
essential. When a system is controllable, optimization will
automatically stabilize the system. However, if the system
is uncontrollable, optimization will not stabilize it.
Therefore, it is crucially important to check controllability
of the system before optimizing it.
To check controllability of the system, rank of
controllability matrix must be checked using Equation 26.
=  BABABAABB 432
(26)
Rank of the matrix shown in Equation 26 is 5 which is
equal to the size of the system. Hence, the system is
controllable and Linear Quadratic Regulator (LQR)
technique guarantees stability.
In optimization using LQR method, The Error Weighted
Matrix Q(t) and The Control Weighted Matrix R(t) need to
be selected wisely such that the system given
specifications are satisfied. Q(t) and R(t) are symmetric
matrices. The simplest way to choose Q(t) matrix is to
start with an identity matrix. The size of the identity
matrix depends on the number of state variables used in
the system modelling. Power system is an output regulator
system since the objective is to keep the output Δf close to
zero. Hence, in Q(t) matrix the most important element is
the element that is directly related to the output. The R(t)
matrix is related to input of the system. A system with n
inputs requires R(t) matrix of size nxn. Increasing value of
R will increase the implementation cost. Hence, it is
important to choose a small value for R(t). Once Q(t) and
R(t) matrices are selected, MATLAB can design the
controller.
Q(t)=
















10000
01000
00100
00010
00008.14
R(t)= 2.0
Matrix K is the feedback gain matrix generated by Matlab.
K=  322.2125.1431.0305.0565.2 
The optimal control law shown in Equation 27 will
generate a new state matrix as shown in Equation 26 [3].
U= - K X(t) (27)
= - K (28)
generates a feedback stable system with the
following eigenvalues: -99.5547 + 0.0000i, -2.7414 +
0.6461i, -2.7414 - 0.6461i, -2.6055 + 0.0000i, -0.1183 +
0.0000i. Since all the five poles are located on the left-half
plane, the closed loop system is a stable system.
6 RESULTS
Figure 4 is MATLAB generated output response of the
optimized system modelled in Figure 3. Based on the
simulation result, the system is stable and the steady state
error is very close to zero. The settling time is 0.7 second
and the percentage undershoot is about 5.54%. Hence,
addition of the proposed PI controller improved the
system performance significantly and the desired
specifications outlined in section 3 are all met.

shown in Figure 5.
Fig-5: Comparison of Controlled vs Uncontrolled Output
Response
7 CONCLUSION
type of conventional controller is suitable for a specific
purpose. Based on the specifications given for power
generation system, a PI controller has been selected. The
parameters of the controller which are integral gain and
proportional gain have been tuned in MATLAB till the
desired response is achieved. LQR method was used to
stabilize the controlled system. Response of the controlled
system had settling time of 0.7second, undershoot of
5.45%, and zero steady state error.
REFERENCES
[1] Dr. A. Ismail, Professor of Electrical Engineering at
Rochester Institute of Technology, Dubai, UAE.
Advances in Power Generation System (2017).
[2] P. Pechtl. Integrated Thermal Power and
Desalination Plant Optimization. PowerGen
Middle East, Paper No.110 (2003).
[3] R. Boruah, Dr. B. Kanta Talukdar. Modelling of
Load Frequency Control Using PI Controller and
Stability Determination with Linear Quadratic
Regulator. Assam Engineering College. India
(2014).
BIOGRAPHIES
Nastaran Naghshineh is a
graduate of Electronics
Engineering from Simon Fraser
University, British Columbia,
Canada. Currently, she is
completing her Master’s degree
in Electrical Engineering,
specializing in Control System, at
Rochester Institute of
Technology (RIT) Dubai, UAE.
Email: nxn5534@rit.eduDr. Abdulla Ismail obtained his
B.Ss (’80), M.Sc. (’83), and Ph.D.
(’86) degrees, in Electrical
Engineering from the University
of Arizona, U.S.A. Currently, he is
a full professor of Electrical
Engineering and assistant to the
President at Rochester Institute
of Technology (RIT) Dubai, UAE.
Email: axicad@rit.edu
Fig-4: Feedback System Output Response
For easier comparison of the controlled and uncontrolled
system, the two output responses are shown in subplots
In this paper, a single area power generation system was
considered and its performance in terms of settling time,
steady state frequency deviation and undershoot was
analyzed in depth. In order to improve the open loop
system performance, a conventional controller is needed
to feedback the output to the input of the system. Each

Comprehensive Approach Towards Modelling and Simulation of Single Area Power Generation System using PI Control and Stability Solution using Linear Quadratic Regulator

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Comprehensive Approach Towards Modelling and Simulation of Single Area Power Generation System using PI Control and Stability Solution using Linear Quadratic Regulator