Ijeet 06 08_005

http://www.iaeme.com/IJEET/index.asp 43 editor@iaeme.com
International Journal of Electrical Engineering & Technology (IJEET)
Volume 6, Issue 8, Sep-Oct, 2015, pp.43-52, Article ID: IJEET_06_08_005
Available online at
http://www.iaeme.com/IJEETissues.asp?JType=IJEET&VType=6&IType=8
ISSN Print: 0976-6545 and ISSN Online: 0976-6553
© IAEME Publication
___________________________________________________________________________
TIE LINE POWER OSCILLATION
CONTROL IN AN INTERCONNECTED
SYSTEM USING TWO LAYERS FUZZY
LOGIC CONTROLLERS WITH UNIFIED
POWER FLOW CONTROL
A. Deepa Lakshmi and M. Malathi
Student, M.E-Applied Electronics, IFET College of Engineering, Tamilnadu
K. Bhuvaneshwari
Assistant Professor Electronics and Communication Engineering,
IFET College of Engineering, Tamilnadu
ABSTRACT
This paper focuses on systematic approach towards damping the tie-line
power oscillations in an interconnected thermal power plant using Unified
Power Flow Controller (UPFC) with two layer fuzzy logic controller (FLC).
Conventionally, Automatic Generation Control (AGC) is carried out by
primary governor control and secondary Proportional- Integral (PI)
controller. PI controller helps to damp out tie-line power and frequency
oscillations when subjected to unit step load disturbance. UPFC based
damping controller helps to stabilize the tie-line power oscillations of power
system. The simulation indicates reduced frequency and tie-line power
transient with much faster settling time is obtained by using UPFC along with
two layer fuzzy logic controller.
Key words: Automatic Generation Control, Proportional-Integral Controller,
Unified Power Flow Controller, fuzzy logic controller.
Cite this Article: A. Deepa Lakshmi, M. Malathi and K. Bhuvaneshwari, Tie
Line Power Oscillation Control In An Interconnected System Using Two
Layers Fuzzy Logic Controllers with Unified Power Flow Control.
International Journal of Electrical Engineering & Technology, 6(8), 2015, pp.
43-52.
http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=6&IType=8
1. INTRODUCTION
System load changes in an interconnected power system leads to oscillations in
frequency and mismatches in scheduled power interchange between the areas. AGC

A. Deepa Lakshmi, M. Malathi and K. Bhuvaneshwari
helps to damp out the frequency oscillations and match the power flow between the
areas within predetermined limits [1- 3]. AGC scheme has two main control loops
namely, primary control and secondary control [1]. During load variations, the
primary speed governor control generation, to match with the demand. With the help
of speed controller, various generators in the control area track the load variations and
share the load in proportion to their capacities. The secondary controller will fine tune
the frequency and tie line power. As per Cohn control strategy, the power flow control
among the areas becomes a joint undertaking for the control areas [4].
The control areas may contain thermal, hydro, nuclear or gas turbine plant. The
inter connection between these areas of any combination is made by tie line.
In this paper, two area thermal power systems are considered. Depending upon the
turbine type, the primary control loop responds within few seconds. Though, primary
AGC match generation with demand, there will be a dip in frequency. The speed
changer setting control by secondary controller is used to resets the error to zero in
few minutes. This fine adjustment carried out through integral action by controlling
the speed changer setting is considerably slower and goes into action only after the
primary control loop has completed its job [1- 3].
Mostly as secondary controller, PI controller is employed [4- 9]. The PI controller
is tuned using various techniques [5- 8]. Further improvement in the tie line power
flow is achieved using Flexible AC Transmission System (FACTS) devices. The
Superconducting Magnetic Energy Storage (SMES) [15] appreciably improves the
frequency oscillations but the tie line power variations are not much improved [16].
The tie line power oscillations are much damped out using the Static Synchronous
Series Compensator (SSSC) [14] [17]. The Unified Power Flow Controller (UPFC)
[18- 20] is used in this paper for damping the tie line power oscillations.
This paper describes about two area thermal power in section 2. Two layer fuzzy
logic Controller and its tuning are explained in section 3. The modeling of UPFC and
its connection with two area thermal power system is furnished in section 4. The
performance of UPFC along with FLC controller is described in section 5.
2. MODEL OF TWO AREA THERMAL PLANT – PRIMARY
CONTROL
2.1. Speed Governing System
Speed-governor in thermal power plant controls the turbine speed with the help of fly
ball and speed Changer mechanism. The steam input valve to the turbine is controlled
by speed governor. The speed control in turn controls the active power output of the
system. During load variations, by controlling the steam the real power is made to
match with the demand. The governor indirectly measures the mismatch between
generation and demand by sensing the change in frequency. Though, primary AGC
match generation with demand, there will be error in frequency. Fine tuning of
frequency is carried out by secondary controller which in turn controls the speed
changer setting.
2.2. Hydraulic Valve Actuator
Very large mechanical forces are needed to position the valve against the high steam
pressure inlet to turbine. These forces are obtained via hydraulic amplifier

Tie Line Power Oscillation Control In An Interconnected System Using Two Layers Fuzzy
Logic Controllers with Unified Power Flow Control
2.3. Turbine-Generator Response
The prime mover driving a generator unit is a steam turbine of thermal power plant.
The turbine power is directly proportional to the flow of the steam. Here, a non-reheat
type turbine is used. It relates the position of the valve that controls the emission of
steam into turbine to the power output of the turbine. The turbine power output is
given as the input to the generator which in turn provides electrical power to the
power system.
2.4. Tie-line
Practically, all power systems are tied together with neighbouring areas. The problem
of load-frequency control becomes a joint undertaking in controlling the power flows
on the inter-ties. In this paper, a two-area thermal power plant is connected by a tie-
line as shown in Fig. 1.
The interconnected thermal power plant is subjected to a unit step load
disturbance, the system frequency and tie-line power deviations are controlled.
Figure 1 Block Diagram of Two Area Interconnected Power Plant
Primary AGC mathematical model of a two-area thermal power plant is used for
simulation as shown in Fig. 2.
3. DEVELOPMENT TWO LAYER FUZZY LOGIC
CONTROLLER
3.1. PI controller
Conventionally, PI controller acts as secondary controller which sets the turbine
reference power of each area. When a two-area thermal power plant is subjected to a
unit step load disturbance, the variation in one area affects the other area via tie-line in
terms of frequency and tie-line power. These two variations have to be combined

linearly as one signal which serves as the input to the secondary PI controller. The
input of each controller is called as Area Control Error (ACE).
The ACE adopted in two area system is developed by Cohn [4]. The proportional
control is used for increasing the loop gain to make the system less sensitive to load
disturbance. The integral control is used to eliminate steady state errors [5]. The
secondary PI controller resets both the frequency and tie-line power variations back to
nominal values.
According to Cohn control strategy [4], all the areas in the network should control
both tie-line and frequency deviations when subjected to step load disturbance.
3.2. Fuzzy logic Controller
Fuzzy logic systems belong to the category of computational intelligence technique.
One advantage of the fuzzy logic over the other forms of knowledge-based controllers
lies in the interpolative nature of the fuzzy control rules. The overlapping fuzzy
antecedents to the control rules provide transitions between the control actions of
different rules. Because of this interpolative quality, fuzzy controllers usually require
far fewer rules than other knowledge-based controllers.
Figure 3 Block Diagram of Fuzzy Logic Controller
A fuzzy system knowledge base consists of a fuzzy if then rules and membership
functions characterizing the fuzzy sets. The block diagram and architecture of fuzzy
logic controller is shown in fig 4. Membership Function (MF) specifies the degree to
which a given input belongs to a s∆∆et. Here triangular membership function have
been used to explore best dynamic responses namely Negative Big (NB), Negative
Small (NS), zero (ZE), Positive Small (PS), Positive Big(PB).Fuzzy rules are
conditional statement that specifies the relationship among fuzzy variables. These
rules help to describe the control action in quantitative terms and have been obtained
by examining the output response to the corresponding inputs to the fuzzy controllers.
Defuzzification, to obtain crisp value of FLC output is done by centre of area
method. The fuzzy rules are designed as shown in Table 1.
3.2 Design of a Two Layer Fuzzy Logic Controller
The aim of introducing two layered fuzzy logic controller [16] is to eliminate the
steady state error and improve the performance of the output response of the system
under study. The proposed control scheme is shown in Fig. 5. The controller consists

of two “layers”: a fuzzy pre-compensator and a usual fuzzy PI controller. The error
e(k) and change of error ∆e(k) are the inputs to the pre compensator.
Table 1 Fuzzy Logic Rules for LFC
A
C
E
.
N N P P
AC B S Z S B
E
N N N N Z
B B B NS S E
N N N Z Z
S S B NS E E
N N Z P
Z S S ZE E S
P Z N P P
S E S ZE S S
P Z Z P P
B E E PS B B
Figure 4.Basic Structure of Fuzzy Pre-Compensated Pi Controller
The aim of introducing two layered fuzzy logic controller [16] is to eliminate the
steady state error and improve the performance of the output response of the system
under study. The proposed control scheme is shown in Fig. 5.
The controller consists of two “layers”: a fuzzy pre-compensator and a usual fuzzy
PI controller. The error e(k) and change of error ∆e(k) are the inputs to the pre
compensator. The output of the pre-compensator is µ (k).
The pre-compensation scheme [18, 19] is easy to implement in practice, since the
existing PI control can be used without modification in conjunction with the fuzzy
pre-compensator as shown in Fig 6. The procedure of rule generation consists of two
parts (i) learning of initial rules which determines the linguistic values of the
consequent variables. (ii) fine tuning adjusts the membership function of the rules
obtained by the previous step. The structure of the pre-compensation rule is written as
If e is Le, and ∆e is L∆e then C is Lc where Le, ∆Le and Lc are linguistic values of e,
∆e, c respectively. Each fuzzy variable is assumed to take 5 linguistic values Le,∆ Le,
or Lc = {NB, NS, ZE, PS, and PB} this leads to fuzzy rules, if the rule base is
complete.

Figure 5 Proposed Two Layered Fuzzy Logic Controller
The proposed two layered FL Ccompensate these defects and gives fast responses
with less overshoot and/or undershoot. Moreover the steady state error reduces to
zero. The first layer fuzzy pre-compensator is used to update and modify the reference
value of the output signals to damp out the oscillations. The fuzzy states of the input
and output all are chosen to be equal in number and use the same linguistic
descriptors as N = Negative, Z = Zero, P = Positive to design the new fuzzy rules. The
fuzzy logic rules for pre- compensator are presented in Table-2.
Table 2 Fuzzy Logic Rules for Pre Compensator
AC
E
N Z P
ACE
N N Z
Z Z p
Z P P
The second layer which is known as feedback fuzzy logic control reduces the
steady state error to zero.
4. UNIFIED POWER FLOW CONTROLLER
Unified Power Flow Controller (UPFC), the FACTS device is a fast acting
compensator. It controls the power system parameters in terms of voltage, phase angle
and impedance. It can be used not only for power flow control but also for stabilizing
the power system [18]. UPFC improves both steady state and dynamic performance of
the system. UPFC with damping controller further improves the dynamic performance
of the system [19]. UPFC with damping controller is connected in series with the
transmission line or in a tie-line. It provides damping of tie-line power oscillations
with fast control of voltage in maintaining the stability of the system. Schematic block
of UPFC based damping controller is shown in Fig. 5.
Figure 6 Block Diagram of UPFC Based Damping Controller

UPFC comprises of high pass filter known as signal wash-out which prevents
steady changes in frequency by modifying input parameters. The wash out time
constant Tw is not sensitive and could
UPFC comprises of high pass filter known as signal wash-out which prevents
steady changes in frequency by modifying input parameters. The wash out time
constant Tw is not sensitive and could lie in the range of 1s to 20s. Tw of 10s is taken
as wash-out time constant. The phase compensator is a lead compensator whose time
constants are chosen so that the system is fully compensated. The damping controller
parameters are determined by means of phase compensation technique [19]. The
UPFC based damping controller relates
Figure 7 Mathematical Model for Electrical Power for Damping Controller
5. SIMULATION RESULTS
The two area thermal power plant shown in Fig. 2 is developed using MATLAB /
Simulink, and is subjected to a unit step load disturbance in area 1 alone.The response
shows steady state error pertaining to oscillations in frequency and tie-line power.
Later, secondary PI controller, is discussed in section 3.1 is included to the two-
area thermal power plant as shown in Fig. 2. The interconnected thermal plant is
subjected to a unit step load disturbance in area 1 alone. The response with secondary
PI controller provides better result with reduced peak overshoot attaining zero steady
state error, when compared with the open loop response as shown in Fig. 8.
Figure 8 Change In Area1 Frequency
Further, the secondary PI controller is replaced by VSS controller as shown in Fig.
2 is simulated. The response of the plant with secondary PI controller and FLC
controller is compared and furnished in Fig. 9.

FLC controller relatively reduces the transients in frequency and tie-line power
with zero steady state error at faster rate.
From the Fig. 9, it shows the oscillation in tie-line power still persists for a while.
Therefore, FLC controller with UPFC damping controller is included in two- area
thermal power plant subjected to step load disturbance in area 1 alone as its
comparison response is shown with VSS controller in Fig. 10.
Figure 9 Change In Tie Line Frequency
Figure 10 change in area 2 frequency
6. CONCLUSION
The systematic procedure for improving the system dynamic performance of an
interconnected thermal power plant is presented in this paper. Two-area thermal
power plant when subjected to unit step load disturbance lead to frequency and tie-
line power oscillations with steady state error and transient overshoots. The transient
oscillation in frequency and tie-line power is reduced using ZN tuned secondary PI
controller. In secondary PI controller, a high proportional gain results in a large
change in the output during the steady state and high integral gain results overshoot
during the transient period.
Thus, it is overcome by using FLC controller. As FLC controller switches in
between P and PI controller, it helps in achieving improved system frequency and tie-
line power response. But, the transient tie-line power oscillation persists for a while
which is not desirable. Therefore, UPFC connected in interconnected tie-line helps to
stabilize the system tie-line power oscillations at a faster rate. The two-area thermal
power plant with FLC controller and UPFC yields much better faster response in
terms of reduced peak overshoot, controls the transient frequency oscillation and
reduces the tie-line power oscillations with zero steady state error.
LIST OF SYMBOLS
- R1, R2: Speed regulation of thermal system; = 2Hz/pu MW
- TH: Hydraulic amplifier time constant; = 0.08 sec

- TT: Non-reheat turbine time constant; = 0.3 sec
- KP: Power system gain constant; = 100
- TP: Power system time constant; = 20 sec
- PD1, PD 2: Change in load demand power in area1 and area2 respectively; =0.01
p.u.
- A12: Synchronizing power coefficient; = -1
- δ12: Operating voltage angle of the tie-line; = 450
- T: Synchronizing coefficient; =10% of area capacity = 0.1 Cos δ12 = 0.0707
- B1, B2: Frequency bias constant; = 0.425 p.u. MW/Hz
- Pref1, Pref2: Change in reference power in p.u;
- Pg: Change in governor power of the thermal system in p.u;
- PH: Change in hydraulic valve power of the thermal system in p.u;
- PT: Change in turbine power of the thermal system in p.u;
- f1, f 2: Change in frequency of area 1 and 2respectively in Hz;
- Ptie12: Change in tie-line power in p.u;
- s: Laplace operator;
- K: Constants of the UPFC based damping controller;
REFERENCE
[1] D. P. Kothari, I. J. Nagrath, Modern Power System Analysis, TMH Edition,
McGraw-Hill, 1980.
G.H. Cohen, G.A. Coon, Theoretical Consideration of Retarded Control,
Transactions of the American Society of Mechanical Engineers, 75, 1953, pp.
827-834.
[2] M. Gopal, Control Systems Principles and Design, Second Edition, Tata
McGraw-Hill, 2002.
[3] B.S. Manke, Linear Control Systems with MATLAB Applications, Eighth
Edition, Khanna Publishers, 2005.
[4] J.G. Ziegler, N.B. Nichols, Optimum Setting for Automatic Controllers,
Transactions of American Society of Mechanical Engineers, 64, 1942, pp. 759-
768.
[5] S. Balamurugan, K.R.M. Vijayachandrakala, K. Sankaranarayanan, Effective
Automatic Generation Control of Hydrothermal Plant Using Superconducting
Magnetic Energy Storage and Static Synchronous Series Compensator Units,
International Conference on Applied Energy (ICAE 2010), pp. 1180-1189,
National University of Singapore.
[6] Hassenzahl W, Will superconducting magnetic energy storage be used on electric
utility systems?, IEEE Transactions on Magnetics, 11(2), 1975, pp. 482-488.
[7] K.R.M. VijayaChandrakala, S. Balamurugan and K.Sankaranarayanan,
Automatic Generation Control for Hydrothermal Plant with Variable Structure
System Controller and Superconducting Magnetic Energy Storage, Journal of
Automation and Systems Engineering, 4(3), 2010, pp. 142-153.
[8] IssarachaiNgamroo, A Stabilization of Frequency Oscillations in an
Interconnected Power System using Static Synchronous Series Compensator,
International Journal of Science and Technology, 6(1), 2001, pp. 52-59.

[9] H.F. Wang, Damping Function of Unified Power Flow Controller, IEE
Proceedings-C, 146, 1999, pp. 81.
[10] N. Tambey, M.L. Kothari, Unified Power Flow Controller (UPFC) based
Damping Controllers for Damping Low Frequency Oscillations in a Power
System, IE(I) Journal–EL, 84, 2003, pp. 35-41.
[11] J. Hao, Li-Baoshi, Yi-Xin Ni, C. Chen, Improvement of Transient Stability by
Unified Power Flow Controller based on Hamiltonian System Theory, 18, 2008,
pp. 617-635.
[12] MATLAB User Manuals, Mathworks Inc.: USA2000.
[13] Duran. Gomezj, L.Omezj, N.Fnjetip and Von.Jouannea, An approach to achieve
ride-through of an adjustable speed drive with fly back converter modules
powered by super capacitors, IEEE Transactions on Industrial applications, 38,
pp. 514–522, 2002.
[14] Moon. G. Joo, A method of converting conventional fuzzy logic system to two
layered hierarchical fuzzy system, IEEE International Conference on Fuzzy
Systems, pp.1357-1362, 2003.
[15] CC. Hang, K.J. Astrom. and WK. Ho, Refinements of the Ziegler – Nichols
tuning formula, IEEE Proceeding, Part D, 138(2), pp.111-118, 1991.
[16] KH. Kim, K.C.Kim and E. K. P. Chong, Fuzzy pre-compensation of PID
Controller, IEEE Transactions on Control Systems Technology, 2, pp.406-411,
1994.
[17] J.K. Kim, J.H. Park, SW Lee and KEP Chong, Fuzzy pre-compensation of PD
controller for system with dead zones, Journal on Intelligent fuzzy system, 1,
pp.125-133, 1993.
[18] D. Pattanayaka, M. Basub,R. N. Chakrabartic, Multi-Objective Differential
Evolution For Optimal Power Flow. International Journal of Electrical
Engineering & Technology, 3(1), 2012, pp. 31-43.
[19] Hyun-Joon Cho, K Wang-Bo Cho, Bo- Hyeun Wang, Fuzzy PID hybrid control:
Automatic rule generation using genetic algorithms, Fuzzy Sets and Systems, 92,
pp.305- 316, 1997.
[20] Ogata Katsuhiko, Modern Control Engineering, New Delhi, India, Prentice Hall
of India private Limited, 2010.
[21] R. Behera, C.R. Dash and Dillip Khamar, GA Based Optimal Power Flow Using
Fact Device. International Journal of Electrical Engineering & Technology,
5(12), 2014, pp. 342-356.
[22] Guguloth Ramesh and T. K. Sunil Kumar, Power Flow Solution with Flexible
AC Transmission System Devices. International Journal of Electrical
Engineering & Technology, 4(4), 2013, pp. 232 - 244.
[23] I. A. Chidambaram and S. Velusami, Design of decentralized biased controllers
for load-frequency control of interconnected power systems, Electric Power
Components and Systems, 33(12), pp.1313-1331, December 2005.

Ijeet 06 08_005

More Related Content

What's hot

Viewers also liked

Similar to Ijeet 06 08_005

More from IAEME Publication

Recently uploaded

Ijeet 06 08_005