DESIGN OF CONTROL STRATEGIES FOR THE LOAD FREQUENCY CONTROL (LFC) IN MULTI AREA POWER SYSTEM FOR PARALLEL OPERATION OF POWER PLANTS

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International Journal of Electrical Engineering & Technology (IJEET)
Volume 7, Issue 5, September–October, 2016, pp.08–19, Article ID: IJEET_07_05_002
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© IAEME Publication
DESIGN OF CONTROL STRATEGIES FOR THE LOAD
FREQUENCY CONTROL (LFC) IN MULTI AREA
POWER SYSTEM FOR PARALLEL OPERATION OF
POWER PLANTS
Chitta Padmaja Priyanka
M. Tech, Department of Electrical Engineering,
PVKKIT, Anantapur, India
S.N.Lavanya
Lecturer in Electrical Engineering,
Government Polytechnic College, Tadipatri, India
G.N.S.Vaibhav
HOD, Assistant Professor, Department of Electrical Engineering,
J. Ramesh
Assistant Professor, Department of Electrical Engineering,
ABSTRACT
This paper features the Differential Evolution (DE) by controller parameters tuning algorithm
and also an application of a multi source power system to a Load Frequency Control (LFC) by
having several sources of power generation techniques. At first, a single area multi-source power
system using integral controllers for every unit is taken and DE procedure is implemented to attain
the controller parameters. Several mutation procedures of DE are estimated and the control
parameters of DE for best obtained procedure are tuned by implementing numerous runs of
algorithm for every change in parameter. Multi-area multi-source power system is also discussed
and a HDVC link is also taken in accordance with the current AC tie line for the internal
connection between the areas. The two variables of Integrals which are to be enhanced using tuned
DE algorithm are proportional integral and proportional integral derivative.
The advantage of the proposed strategy has been exposed by estimating the results with the
freshly published enhanced output feedback controller for the similar power systems. Further, the
assessment is conducted by performing several procedures like overshoot, tie line power derivation
followed by a step load perturbation (SLP), settling time and standard error criteria of frequency.
It is also observed that, the maximum output of the feedback controller is less than the dynamic

Design of Control Strategies for the Load Frequency Control (LFC) in Multi Area Power System for Parallel
Operation of Power Plants
performance of the proposed controller. Additionally, the proposed power system is not affected by
altering the loading conditions, size of SLP and system parameters.
Key words: Differential Evolution (DE), step load perturbation (SLP), HDVC link, Multi-area
multi-source power system, Load Frequency Control (LFC).
Cite this Article: Chitta Padmaja Priyanka, S.N. Lavanya, G.N.S. Vaibhav and J. Ramesh, Design
of Control Strategies for the Load Frequency Control (LFC) in Multi Area Power System for
Parallel Operation of Power Plants. International Journal of Electrical Engineering & Technology,
7(5), 2016, pp. 08–19.
http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=7&IType=5
1. INTRODUCTION
Load Frequency Control (LFC) is the problem of controlling the real power output of generating units in
response to changes in tie-line power interchange and system frequency within specified limits. LFC is
also a part of Automatic Generation Control (AGC) and plays a very important in the operation and control
of power systems. . Large Scale Power Systems are generally composed of control areas representing
coherent groups of generators, which have combination of hydro, thermal, nuclear, gas, and renewable
energy sources. Researchers across the world are trying to propose several new strategies for LFC of
power systems to maintain the system frequency and tie line flow at their scheduled i.e. pre-defined values
during normal operation and also during small perturbations. It can be seen through literature survey that,
most of the LFC power system works have been carried out on two area thermal–thermal or hydro-thermal
systems. It is noticed that, large amount of research work indicates to propose better AGC systems based
on Neural Network, Modern Control Theory, Reinforcement Learning [8], Fuzzy Systems and ANFIS
approach. But, with these advanced approaches there are complications and also need experienced users
which in turn reduces their applicability. Due to its structural simplicity and reliability the classical
Proportional Integral Derivative (PID) controller remains an engineer’s preferred choice, which even has
an advantage of favorable ratio between performances and cost. This model requires lower user-skills,
minimal development effort and simplified dynamic modeling. In current technology, modern artificial
intelligence-based approaches have been projected to optimize the PI/PID controller parameters for AGC
system. The increase in complexity and size of electric power systems along with raise in power demand
has made it compelling to use the intelligent systems that combine methodologies, techniques and
knowledge from varied sources for the real-time control of power systems. The design problem for the
proposed controller is developed as an optimization problem. A frequency controller is needed to maintain
the system frequency, as burden of a power system is constant and never given to ensure the quality of
supply of energy, at the desired nominal value. In the deregulated energy system, control area contains
various disturbances and varied types of uncertainties because of more complex modeling errors of the
system and the changes in the structure of power system. Therefore, we need a control strategy which not
only maintains the desired tie-line power and flow rate constancy but can also achieve zero steady-state
error and inadvertent exchange.
The most popular feedback controller used by the process industries is the proportional integral
derivative (PID) controller is the. It is a robust controller, easy to understand that it can provide excellent
control performance despite the various dynamic characteristics of the process plant. As the name
proposes, the PID algorithm has basically three modes, proportional mode, integral mode and derivative
mode. A proportional controller effects in reducing the rise time but can never eliminate the steady state
error. Where as an integral control can eliminate the steady-state error, but it will make the transient
response still worse. The derivative control improves the system stability by decreasing surplus, and
improved transient response. Proportional integral controllers (PI) are nowadays most often used in the
industry. We can use a controller without the derivative mode (D) when a fast system response is not
required; there is high delay in the transport system and during high noise and disturbance present during

Chitta Padmaja Priyanka, S.N.Lavanya, G.N.S.Vaibhav and J. Ramesh
the process of operation. Derivative mode helps in improving the system stability and allows increase gain
and decrease in integral gain, which in turn boosts the speed of response proportional controller. PID
controller is mostly used when quick response and stability are main concern. In view of above, I, PI and
PID controllers are considered analytical herein. In the design of a modern technique based on the
optimization controller, we first define the objective function based on the specifications and limitations.
So the design of the objective function is for tuning the controller and generally based on a performance
index that considers the entire closed-loop system response. Time domain conventional output
specifications are rise time, steady-state error, overshooting peak, settling time.
2. DESIGN OF CONTROL SYSTEM
For the design of controller in the system the prominent specimen to be considered is the two area system
covering the warming of gas turbines, hydraulic and thermal units. Figure 1 shows the LFC power system
under study. This system is used for simulation by linearising the governors, hydraulic turbines, reheat
turbines, gas turbines. The participation and contribution of each unit to the rated load is decided by its
parameter adjustment factor. In each Control unit the sum of the participation factor need to be equal to
1.In Figure.1R1, UT and KT are parameter of regulation, output of control and participation factor
respectively for thermal unit; R2,UH and KH are parameter of regulation, output of control and
participation factor respectively for hydraulic unit; R3,HG and KG are parameter of regulation, output of
control and participation factor respectively for gas unit, is the constant speed governor thermal unit
(seconds), is steam turbine time constant (seconds), Kr is the constant reheat steam turbinate is the
reheat time constant of the steam turbine (seconds), is time nominal starting penstock water
(seconds), is the regulator of the hydraulic turbine speed reset the time (seconds), is Hydro turbine
speed governor transient droop time constant(seconds), is hydro turbine main regulator time constant
servo speed (seconds), is the time constant standby gas turbine;where c is the time constant delay of the
gas turbine and speed regulator in seconds, is the valve positioned of the gas turbine, is the constant of
the gas turbine valve positioner, is the gas turbine fuel time constant (seconds), is the gas turbine time
delay combustion reaction (seconds), is the gas turbine compressor discharge at constant volume time
in (seconds), is gain of power system (Hz/puMW) , is the system time constant power in (seconds),
∆F is the incremental change in the exchange rate and incremental load ∆PD.The nominal system
parameters are displayed in reference.
For our current study integral of time-weighted absolute error (ITAE) is preferred objective function as
the deviation of power in the control areas will be diminished quickly and also used in study of ACG.
Hence objective function is expressed as:
J=ITAE= |∆ |. .
Where the simulation is time interval and ∆ is the frequency deviation system. As the constraints
of the problem are the limits of the driver parameters. Therefore, our design problem can be forged as the
below optimization problem. Subject to diminish J:
! "
≤ ! ≤ ! %&
Where our objective functions is J and !'()
and !'*+
theminimum control parameters and maximum
control parameters. As proclaimed in the literature, the minimum and maximum values of the controller
parameters are chosen as -1 and 1 respectively.

Figure 1 Transfer function model of multiple sources-multiple areas with HVDC link
3. CONVENTIONAL CONTROLLER TUNING METHODS
As of now the PID (proportional integral derivative) controller is the most common control algorithm.
Most of the feedback loops are being controlled by this algorithm or with minor variations in it. This can
be implemented in different forms like a stand-alone controller or as a part of a Direct Digital Control
package or a hierarchical distributed process control system. PID algorithm consists of three basic modes:
• Proportional mode
• Integral mode
• Derivative mode
A proportional controller regulates the reaction to the current error and rise time, but can never
eliminate the steady-state error. Integral control regulates the reaction based on the sum of recent errors
and eliminates steady-state error, but it makes the transient response worse. Derivative control regulates
the reaction to the rate at which the error has been changing i.e. increases the stability of the system, which
in turn reduces the overshoot, and improves the transient response.
Proportional Integral controllers are the most regularly used controllers in industry now. We can use a
controller without D mode when: a fast response of the system is not mandatory, large noise and
disturbances are present during operation of the process and there is significant delay in the transport
system. Derivative mode increases the stability of the system and enables improvement in gain K and
decreases integral time constant Ti, which in turn increases the speed of the controller response.PID

control is generally used when the stability and quick response is mandatory. In this paper for Load
Frequency Control we have considered I, PI, PID controllers. By setting the control parameters to their
optimum values for obtaining desired control response we tune these controllers. Foremost requirement is
stability of the multi-area system. Anyways as different systems will have different behavior and different
applications will have different requirements and these requirements may conflict with each another. Even
though the principle of PID is simple and has only three parameters, tuning of ID is challenging as it needs
to be answered in complex criteria. Manual setting method, PID tuning methods software and Ziegler-
Nichols tuning method are some of the prominent control loop tuning methods.
4. PSO CONTROLLER
James Kennedy and Russell Eberhart recommended Particle swarm optimization in 1995, which is
population movement and swarm intelligence, based speculative optimization technique. PSO is a
computational technique, developed and inspired by social behavior of bird flocking or fish schooling. A
group of random particles (solutions) are generated in this technique. Each particle is now treated as a
point in an n-dimensional space, which will adjust its "fly" according to the flight experience of other
particles and its own experience of flight. According to the value of fitness, the best solution is determined
for the current iteration and the best fitness value is also stored. The best possible solution is known as
pbest. Another finest fitness value is also tracked in the iterations so far obtained. This finest fitness value
is the global best and the corresponding particle solution is called gbest. In each of the iteration all of the
particles have been restored by following the pbest (best previous position) and gbest (best particle among
all the particles) in the swarm. There is no crossover operation involved PSO. In Evolutionary
Programming (EP) balance between global and local search we can set the strategy parameter, while
equilibrium in PSO is reached by the inertial weight factor (w).
PID controller with PSO optimization is designed for LFC and tie-power control. Main objectives are
to control the frequency and tie-between area with good power oscillation damping, also get good
performance in this study, the optimal values of the PK parameters Ki and Kd for PID controller easily and
accurately calculated using a PSO.In a typical run of the PSO, an initial population is generated randomly.
This is known as initial population generation 0th. Each individual of the initial population has an index
value associated performance. The use of performance index information, the PSO then produces a new
population. So as to obtain the performance index value for each one of the individual in the current
population, the system needs to be simulated. The PSO will now produce the next generation of people
who can use crossover operators and mutation breeding. These processes are kept repeating till the
population is converged and the optimal value of parameters found. The document aims to use the PSO
algorithm in order to obtain optimal values of the PID controller to a system of two frequency load area.
Each adjustment controller may represent a particle in the search space that changes its parameters
proportionality constant Kp, an integral constant Ki, and Kd derivative constant to minimize the error
function. The error function used here is the integral time absolute error (ITAE).
4.1. PSO Steps: Steps PSO as Implemented for the Optimization
• Step 1: Initialize an array of particles with random positions and velocities associated to satisfy the
inequality constraints.
• Step 2: Check satisfying the equality constraints and modify the solution if necessary.
• Step 3: Assess the fitness function of each particle.
• Step 4: Compare the current value of the fitness function better with particles above value (pbest). If the
current fitness value is lower, then assign the current value of fitness for pbest and assigned the coordinates
(positions) to pbest x.
• Step 5: Determine the current global minimum value of fitness between the current positions.

• Step 6: Compare the current global minimum above the minimum overall (gbest). If the current global
minimum is better than gbest, then assign the global minimum current for gbest and assigned the coordinates
(positions) to gbestx.
• Step 7: Change speeds.
• Step 8: Each particle moves to the new position and return to step 2.
• Step 9: Repeat from step 2-8 until the stopping criterion is satisfied or we have reached the maximum
number of iterations.
Figure 2 Flowchart For PSO
5. DIFFERENTIAL EVOLUTION
Differential Evolution (DE) is a population based indefinite optimization algorithm which is capable of
handling non-linear, non-differentiable and also multi-modal objective functions, which has some readily
selected control parameters. The design problem of the proposed controller is then formulated as an
optimization problem and here DE is used in searching optimal control parameters. DE works with two
different people; the old generation and the new generation of the very same population. The population
size can be adjusted by the parameter NP. This population is composed of actual dimension values D

vectors which equals to the number of design parameters divided by variables control. This population is
run randomly within the initial parameter. The optimization process is mainly affected by these three main
operations: mutation, crossover and selection. In each of the generation, the individuals in the current
population become the targets vectors. In vector for each target, the mutation operation produces a mutant
vector, then by addition of the weighted difference between any two vectors selected randomly, a third
vector is made target. The cut operation now generates a new vector, which is called as test vector by
mixing the entries of the vector mutant with that of the target vector. If this test vector gets a better fitness
value than that of the target vector, then this test vector replaces the target vector in the preceding
generation. Evolutionary operators are as described below:
5.1. Initialization
For each parameter j with the lower and upper bound the values of the initial parameters are usually
uniformly at random in the interval [ ,
-
; ,
.
].
5.2. Mutation
For any given parameter vector X0,2, we take the three vectors (X34,2X35,2 ,X36,2) are randomly selected in
such a way that the indices i, r1, r2 and r3 are distinct. The donor vector V0,284 can be created by adding
the weighted difference between the two vectors to a third vector as:
V0,284=X34,2+ F. (X35,2- X36,2),
Where F is a constant from (0, 2)
5.3. Crossover
These three parents are now selected for the crossover and the child is a perturbation of any one of them.
Our trial vector Ui,G+1 is developed from the elements of this target vector (Xi, G) and the elements of the
donor vector (Xi, G). Elements of the donor vector then enter the trial vector with probability CR as:
Uj,i,G+1=9
V:,0,284 if rand:,0 ≤ CR CDE = GHIJK
X:,0,284 if rand:,0 > MN CDE = GHIJK
O
Withrand:,0~ U (0, 1), I3RSTis a random integer from (1, 2, . . ., D) where D is the solution’s dimension
i.e. number of control variables. I3RST ensures that V0,284 ≠ X0,284
5.4. Selection
The target vector X0,2is now compared with the trial vector V0,284and the one with the better fitness value is
admitted to the further generations. The selection operation in DE can be represented by the below
equation:
, 84=V
U0,284 if f( U0,284) < [(X0,2)
X0,2C ℎ]D^(_]
O
Where I∈ [ 1,a ]

Figure 3 Flow Diagram of DE Flow
6. RESULTS AND COMPARISION
During the simulation study, Error signals f1, f2 and tie line power required for the controller software is
transferred to the PSO. All positions of the particles in each dimension are held within the limits specified
by the user, and Velocities subject to the [Vmin, Vmax] range. A gradual increase in demand of 0.01 pu is
applied to zone 1 The frequency deviation of the first area frequency and f1 and second f2 deviation of the
area and the area between clamping signal power closed loop system shown in Figure. 4, 5, 6. Similarly a
step increase in demand of 0.01 applies to Area2. Frequency deviation of the first area and the f1 and f2
frequency of the second area and the area between clamping signal power circuitry of the closed loop
system are shown in Figure 7,8,9.

Figure 4 Change in Frequency of Area-1 for 1% change in Area-1
Figure 6 Change in Tie Line Power for 1% change in Area-1

Figure 9 Change in Tie Line Power for 1% change in Area-2

Simulation results show improved performance in the time domain specifications for a load of 0.01 pu
step Using the PSO approach, global and local solutions could be found simultaneously for a better
adjustment of the controller parameters . The PID value that was obtained by the PSO algorithm is
compared with the PID controller derived from differential evolution algorithm, PID controller, PI
controller and the I in different perspectives namely, robustness and stability. All simulations were carried
out using MATLAB A comparison of time domain specifications overshoot and settling time for a load of
0.01 pu step in area 1 are tabulated as given in the table (2) and it is very clearly that the PSO based
controller dramatically reduces overshoot by a large value. Settling time improved.
7. CONCLUSION
In this thesis, Load Frequency Control (LFC) of multi-unit source power system having different sources
of power generation like thermal, hydro and gas power plants is presented. The controller parameters are
tuned using particle swarm optimization (PSO) technique, Differential Evolution algorithm technique, PID
controller, PI controller and I controller. The superiority of our proposed approach has been interpreted
clearly by analysing the results with Integral (I) controller, proportional integral (PI) controller,
Proportional Integral Derivative (PID) Controller and DE employed PID controller for the same power
systems by using different performance measures like overshoot, settling time and standard error criteria of
frequency and tie-line power deviation using a step load perturbation (SLP). It is observed that, the
dynamic performance of proposed controller PSO is revised. Additionally, it is also seen that the proposed
system is flexible and is not affected by any change in its loading condition, system parameters and size of
SLP.
APPENDIX B: SYSTEM SETTINGS
b4= b5= 0.4312 p.u. MW/Hz; cH = 2000 MW; c-= 1840 MW; N4= N5= N6= 2.4 Hz/p.u.; = 0.08 s; =
0.3 s; = 0.3; = 10 s; 4= 5= 68.9566 Hz/p.u. MW; 4= 5= 11.49 s; 45= 0.0433; *45= -1;
= 1 s; = 5 s; = 28.75 s; = 0.2 s; = 0.6 s; d = 1 s; = 1; = 0.05 s; = 0.23 s; = 0.01
s; = 0.2 s; = 0.543478; = 0.326084; = 0.130438; = 1; = 0.2 s.
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DESIGN OF CONTROL STRATEGIES FOR THE LOAD FREQUENCY CONTROL (LFC) IN MULTI AREA POWER SYSTEM FOR PARALLEL OPERATION OF POWER PLANTS

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DESIGN OF CONTROL STRATEGIES FOR THE LOAD FREQUENCY CONTROL (LFC) IN MULTI AREA POWER SYSTEM FOR PARALLEL OPERATION OF POWER PLANTS