This document describes a major project report submitted for the partial fulfillment of an Electrical Engineering degree. The report discusses load frequency control of a two area power system using an Artificial Bee Colony algorithm. It includes sections on introduction, literature review, project objectives, load frequency control, controllers, the ABC algorithm, a two area power system model, simulations, results, and conclusions. The objective is to design a controller based on ABC algorithm parameters to limit frequency deviations and regulate power flow through tie lines when load demand varies.

1. MAJOR PROJECT ON REPORT SUBMITTED
TOWARDS PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
ELECTRICAL ENGINEERING
UNDER THE GUIDANCE BY
Mr. AITIHYAJEET MOHAPATRA
SUBMITTED BY :-
• ALOK KUMAR PATI
• KHARABELA BAIRIGANJAN
• A SAINATH PATRO
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GROUP - 10

2. CONTENTS
 INTRODUCTION
 LITERATURE REVIEW
 PROJECT OBJECTIVE
 LOAD FREQUENCY CONTROL
 CONTROLLERS
 ARTIFICIAL BEE COLONYALGORITHM
 ABC ALGORIDAM FLOW CHART
 TWO AREA POWER SYSTEM
 SIMULATIONS MODEL
 RESULTS & SIMULATION
 CONCLUSION
 RFERENCE
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3. INTRODUCTION
• An electric power system is a network of electrical components deployed to supply, transfer and use electrical power.
The electrical power system provides a means of generating, transmitting and distributing energy in the form of electric
current to the ultimate users and the load .
• An example of power system is the electrical grid that provides power to homes and industries within an extended area.
The electrical grid can be broadly divided into the generators that supply the power, the transmission system that carries
the power and the distribution system that feeds the power to nearby homes and industries .
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4. INTRODUCTION
• A Single area system is a coherent area consisting of Turbine, governor, generator and controller and all of the generators
power is supplied to the load. There is an assumption of no tie line connection with any other area and no connection with
the grid.
• A Two Area System is defined as a system of two single area systems which are interconnected via a tie-line.
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5. INTRODUCTION
In an electrical power system, Automatic Generation Control (AGC) is a system for adjusting the power output of multiple
generators at different power plants, in response to change in the load.
The functions of AGC are-
• Make a balance between generation and load.
• Monitor as well as control the system frequency, power interchange and tie-line flows.
• Keep the time average of frequency deviations and tie-line power deviations at a low value.
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LITERATURE REVIEW
Sl
no
SUMMARY NAME OF PAPER
1 Frequency regulation of power systems by fuzzy aided PID controller is proposed in this study. The controller gains
are optimized by an Improved Grey Wolf Optimization (IGWO) method. The improvement in GWO method is done
by an approach which does not consider less important delta wolves for updating the position vector in the hunting
stages of the algorithm thus making the algorithm simpler with less implementation time.
BP sahoo, Panda S (2018) “Improved grey
wolf optimization technique for fuzzy aided
PID controller design for power system
frequency control”. Sustain Energy Grids
Networks
2 Various natural systems teach us that very simple individual organisms can create systems able to perform highly
complex tasks by dynamically interacting with each other. The Bee Colony Optimization Metaheuristic (BCO) is
proposed in this paper. The artificial bee colony behaves partially alike, and partially differently from bee colonies in
nature. The BCO is capable to solve deterministic combinatorial problems, as well as combinatorial problems
characterized by uncertainty.
Dusan Teodorovic , Mauro Dell’Orco
(2005) “Bee colony optimization, a
cooperative learning approach to complex
transportation problems.
3 A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the
food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of
neighbourhood search combined with random search and can be used for both combinatorial optimisation and
functional optimisation.
Pham DT, Kog E, Ghanbarzadeh A, Otri S,
Rahim S, ZaidiM(2006) “The bees
algorithm, A snovel tool for complex
optimisation problems”.
4 A novel hybrid Differential Evolution (DE) and Pattern Search (PS) optimized fuzzy PI/PID controller is proposed
for Load Frequency Control (LFC) of multi-area power system. Initially a twoarea non-reheat thermal system is
considered and the optimum gains of the fuzzy PI/PID controller are optimized employing a hybrid DE and PS
(hDEPS) optimization technique
Sahu RK, Panda S, Yegireddy NK (2014) “
A novel hybrid DEPS optimized fuzzy
PI/PID controller for load frequency control
of multi-area interconnected power systems”

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LITERATURE REVIEW
sl
no
SUMMARY NAME OF THE PAPER
5 A hybrid Firefly Algorithm (FA) and Pattern Search (PS) optimized fuzzy PID controller is proposed for Load
Frequency Control (LFC) of multi area power systems. Initially a two area thermal system with Governor Dead Band
(GDB) nonlinearity is considered and the gains of the fuzzy PID controller are optimized employing a hybrid FA and
PS (hFA–PS) optimization technique.
Sahu RK, Panda S, Pradhan PC (2015)
“Design and analysis of hybrid firefly
algorithm-pattern search based fuzzy PID
controller for LFC of multi area power
systems”.
6 The intermittent feature of renewable energy sources leads to the mismatch between supply and load demand on
microgrids. In such circumstance, the system experiences large fluctuations, if the secondary load frequency control
(LFC) mechanism is unable to compensate the mismatch. In this issue, this paper presents a well-structured
combination of the fuzzy PD and cascade PI-PD controllers named FPD/PI-PD controller as a supplementary
(secondary) controller for the secondary load frequency control in the islanded multi-microgrid (MMG).
Gheisarnejad M, Khooban MH (2019)
“Secondary load frequency control for multi-
microgrids: HiL real-time simulation”.
7 Global analysis of the power system markets shows that load frequency control (LFC) is one of the most profitable
ancillary services of these systems. This service is related to the short-term balance of energy and frequency of the
power systems and acquires a principal role to enable power exchanges and to provide better conditions for electricity
trading. The main goal of the LFC problem is to maintain zero steady state errors for frequency deviation and good
tracking of load demands in a multi-area power system.
H. Shayeghi , H.A. Shayanfar, A. Jalili
(2008) “Load frequency control strategies: A
state-of-the-art survey for the researcher”
8 in load frequency is an index for normal operation of power systems. When load perturbation takes place anywhere in
any area of the system, it will affect the frequency at other areas also. To control load frequency of power systems
various controllers are used in different areas, but due to non-linearities in the system components and alternators,
these controllers cannot control the frequency quickly and efficiently.
D.K. Chaturvedia,*, P.S. Satsangia , P.K.
Kalra (1999) “Load frequency control: a
generalised neural network approach”

8. LITERATURE REVIEW
sl
no
SUMMARY NAME OF THE PAPER
9 In this paper an extensive literature review on load–frequency control (LFC) problem in power system has been
highlighted. The various configuration of power system models and control techniques/ strategies that concerns to LFC
issues have been addressed in conventional as well as distribution generation-based power systems. Further,
investigations on LFC challenges incorporating storage devices BESS/SMES, FACTS devices, wind–diesel and PV
systems etc have been discussed too.
Shashi Kant Pandey, Soumya R. Mohanty,
Nand Kishor (2013) “A literature survey
on load–frequency control for
conventional and distribution generation
power systems”
10 In this paper an adaptive controller is presented for load-frequency control of power systems. The new controller uses a
PI adaptation to satisfy the hyperstability condition for taking care of the parameter charlges of the system. Only the
available information of the states and output of the model as well as the plant output are required f.or the control. No
explicit parameter identification is required.
C. T. PAN Member C. M. LIAW (1989)
“An adaptive controller for power system
load frequency control”
11 Open transmission access is a legal requirement in the United States, but is not fully implemented. Discussion of
deregulation has so far focused principally on the tariff structure for transmission access, but operating the power
system in this new environment will present significant problems of an almost purely technical nature. Something as
simple as frequency control becomes challenging when implemented in the competitive, distributed control
environment that true third party wheeling creates.
Anjan Bose , Richard D. Christie(1996)
“Load Frequency Control Issues In Power
System Operations After Deregulation”
12 Power systems are the most complex systems that have been created by men in history. To operate such systems in a
stable mode, several control loops are needed. Voltage frequency plays a vital role in power systems which need to be
properly controlled. To this end, primary and secondary frequency control loops are used to control the frequency of the
voltage in power systems.
Hassan Haes Alhelou, Mohamad-Esmail
Hamedani-Golshan, Ehsan Heydarian-Forushani
and Pierluigi Siano (2018) “Challenges and
Opportunities of Load Frequency Control in
Conventional, Modern and Future Smart Power
Systems: A Comprehensive Review”
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9. PROJECT OBJECTIVE
As we know 50 Hertz is normal operating frequency in India and if there is a variation of ± 2.5 hertz then it is going to seriously
affect the entire system. For example, turbine blades are prone to get damaged in such condition. Also, there is a relation
between frequency and motor speed which is also going to be affected by frequency variation. The objective of this work is:
• Designing a controller on the basis of configured parameters from ABC Algorithm. The algorithm for limiting the
value of the frequency shift to a constant against any variation in demand for loads.
• It is necessary to maintain the power flow through the tie line of each area to its pre-value mentioned.
• Minimize the frequency regulation error of the micro grid system.
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10. LOAD FREQUENCY CONTROL
When a system connected to multiple loads the system frequency changes as load varies sometimes frequency regulation is not
required but when it is required the operator adjusts the governor characteristics in turn regulating the frequency which is called
Load Frequency Control .
If a change in load is taken care by two generating stations running at parallel, then the complexity of the system increases. The
possibility of sharing the load by two machines is as follows:
• If any change in load is taken care by A so as to have constant frequency, then this kind of regulation is called Flat Frequency
Regulation.
• The other possibility of sharing the load is that both A and B would regulate their generations to maintain the constant
frequency is called Parallel Frequency Regulation.
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11. • The third possibility is that the change in the frequency of a particular area is taken care of by the generator of that
area thereby maintaining constant power flow in the tie line is known as Flat Tie-line Loading Control.
• In Selective Frequency control each system in a group is takes care of the load changes on its own system and does
not aid the other systems un the group for changes outside its own limits.
• In Tie-line Load-bias control, all power systems assist in regulating frequency and tie-line power flow regardless
of from where the frequency variation originates.
LOAD FREQUENCY CONTROL
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12. CONTROLLERS
• CONTROLLERS
It is a mechanism that seeks to minimize the difference between the actual value of a system and the desired value of the system.
• Types of Controllers
1. Continuous Controllers - The controlled variable can have any value within the controller’s output range .
2. Discontinuous Controllers – The controlled variable changes between discrete values depending upon how many different
stages the controlled variable can assume e.g. two position, three position and multi-position controllers
Now the continuous controller have three basic modes on which the control action takes place which are-
I. Proportional Controllers
II. Integral Controllers
III. Derivative Controllers
We also use the combination of these controllers to control our system and to have more accurate output. These three types of
controllers can be combined into new controllers . These are-:
a. Proportional and Integral Controllers (PI controllers)
b. Proportional and Derivative Controllers (PD Controllers)
c. Proportional and Integral and Derivative Controllers (PID Controllers)
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13. CONTROLLERS
• Proportional Controller
In a proportional controller as the name suggests the output signal is directly proportional to the error signal
Representing this mathematically we have-:
R 𝑠 𝛼 𝐸 𝑠
Removing the sign of proportionality we have-:
R 𝑠 = 𝐾𝑝 × 𝐸 𝑠
Where 𝐾𝑝is proportional constant also known as controller gain. R 𝑠 is input signal and E 𝑠 is the error signal.
It is recommended that 𝐾𝑝 should be kept greater than unity. If the value of 𝐾𝑝 is greater than unity (>1), then it will amplify the
error signal and thus the amplified error signal can be detected easily.
Block diagram of Proportional Controller 17-05-2022
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14. • Integral Controller
As the name suggests in integral controllers the output (also called the actuating signal) is directly proportional to the integral of
the error signal.
Mathematically we have-:
R s α 0
t
E τ ⅆτ
Removing the sign of proportionality we have-:
R s = 𝐾𝑖 × 0
t
E τ ⅆτ , τ: a dummy integration variable.
Where Ki is an integral constant also known as controller gain. R 𝑠 is input signal and E 𝑡 is the error signal.
Block diagram of Integral Controller
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14 CONTROLLERS

15. CONTROLLERS
• Derivative Controller
We never use Derivative controllers alone. It should be used in combinations with other modes of controllers because of its few
disadvantages which are written below:
I. It never improves the steady-state error.
II. It produces saturation effects and also amplifies the noise signals produced in the system.
Now, as the name suggests in a derivative controller the output (also called the actuating signal) is directly proportional to the
derivative of the error signal.
Writing this mathematically we have-:
R 𝑠 𝛼
𝑑
𝑑𝑡
𝐸 𝑡
Removing the sign of proportionality we have-:
R 𝑠 = 𝐾𝑑 ×
𝑑
𝑑𝑡
𝐸 𝑡
Where, 𝐾𝑑 is proportional constant also known as controller gain.
Block diagram of Derivative Controller
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16. CONTROLLERS
• Proportional and Integral Controller
As the name suggests it is a combination of proportional and an integral controller the output (also called the actuating signal) is equal
to the summation of proportional and integral of the error signal.
Now let us analyze proportional and integral controller mathematically.
As we know in a proportional and integral controller output is directly proportional to the summation of proportional of error and
integration of the error signal, writing this mathematically we have-:
R 𝑠 𝛼 𝐸 𝑡 + 0
t
E τ ⅆτ
Removing the sign of proportionality we have-:
R s = (𝐾𝑖× 0
t
E τ ⅆτ) +(𝐾𝑝 × 𝐸 𝑡 )
Where, Ki and 𝐾𝑝integral constant and proportional constant respectively.
Block Diagram of Proportional Integral Controller
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17. CONTROLLERS
• Proportional and Derivative Controller
As the name suggests it is a combination of proportional and a derivative controller the output (also called the actuating signal)
is equals to the summation of proportional and derivative of the error signal. Now let us analyze proportional and derivative
controller mathematically.
As we know in a proportional and derivative controller output is directly proportional to the summation of proportional of error
and differentiation of the error signal, writing this mathematically we have-:
R 𝑠 𝛼 (𝐸 𝑠 +
𝑑
𝑑𝑡
𝐸 𝑡 )
Removing the sign of proportionality we have-:
R 𝑠 = (𝐾𝑝× 𝐸 𝑠 ) + (𝐾𝑑 ×
𝑑
𝑑𝑡
𝐸 𝑡 )
Where, 𝐾𝑑 and 𝐾𝑝 derivative constant and proportional constant respectively.
Block diagram of Proportional and Derivative controller
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18. CONTROLLERS
• Proportional Integral and Derivative Controller
As the name suggests it is a combination of proportional and integral and a derivative controller the output (also called the
actuating signal) is equals to the summation of proportional and integral and derivative of the error signal. Now let us analyze
proportional and integral and derivative controller mathematically.
As we know in a proportional and integral and derivative controller output is directly proportional to the summation of
proportional of error and differentiation of the error signal, writing this mathematically we have-:
R 𝑠 𝛼 𝐸 𝑡 + 0
t
E τ ⅆτ +
𝑑
𝑑𝑡
𝐸 𝑡
Removing the sign of proportionality we have-:
R 𝑠 = (𝐾𝑝× 𝐸 𝑡 ) + (𝐾𝑖 × 0
t
E τ ⅆτ) + (𝐾𝑑×
𝑑
𝑑𝑡
𝐸 𝑡 )
Block diagram of Proportional and Integral and Derivative Controller
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19. CONTROLLERS
• PID(1+PIDD)
General structure of a PID+1+PIDD controller is shown in the fig.
It is a 1+PIDD controller cascaded with a conventional PID controller. The name itself indicates that it has proportional, integral and
derivative modes with their own characteristics.
In addition to the PID controller one proportional gain of value 1 and one more derivative controller has been added in parallel with the
derivative control gain, to work as the 1+PIDD controller.
The configuration of the 1+PIDD controller is exhibited . The proportional mode can reduce the effect of rise time, however steady state
error will be increased. The integral approach can nullify the steady state error but the transient response of the system becomes worse.
Finally, the derivative mode increases the system stability, reducing the overshoot an improving the transient response.
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+
++ +
+
+
+
+
PROPORTIONAL
1
PROPORTIONAL
INTEGRAL
DERIVATIVE
DERIVATIVE
INTEGRAL
DERIVATIVE

20. ARTIFICIAL BEE COLONYALGORITHM
In this section, a brief review on ABC is going to be given.
1. Thoughts of the Algorithm. A honey bee colony can successfully discover the highest quality food sources in nature. Hence, the
idea of ABC comes from intelligent foraging behaviour of honey bees to finding good solutions for solving optimization
problems.
In a general way, according to the ways of searching food, The colony of bees is divided into three kinds-:
a. Employed Bees
The employed bees are responsible for exploiting the nectar sources. They explore the beforehand food source position and give the
quality information of the food to the onlooker bees. The employed bees maintain good solution.
b. Onlooker Bees
The onlooker bees wait in the hive and decide to exploit a food source based on the information shared by the employed bees. In order
to find a new nectar source. The onlooker bees improve convergence speed.
c. Scout Bees
The scout bees randomly search environment either depending on an internal motivation or based on possible external clues. The scout
bees enhance the ability to remove local optimum.
The position of a nectar source implies a possible solution of the optimization problems, and the profitability of a nectar source
corresponds to the quality (fitness) of the possible solution. Each nectar source is exploited by only one employed bee. In other words,
the number of nectar sources equals the number of employed bees or onlooker bees.
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21. ARTIFICIAL BEE COLONYALGORITHM
1 Generate the initial population 𝑥𝑖 , randomly, 𝑖 = 1,...,NS. ▷ Initialization
2: Evaluate the fitness function 𝑓𝑖𝑡𝑖, of all solutions in the population.
3: Keep the best solution Test in the population. ▷ Memorize the best solution
4: Set cycle=1.
5: repeat
6: Generate new solutions 𝑣𝑖, from old solutions,
where 𝑣𝑖𝑗 = 𝑥𝑖𝑗+ 𝜙𝑖𝑗(𝑥𝑖𝑗-𝑥𝑘𝑗), 𝜙𝑖𝑗€ (-1,1], k € {1,2,..., NS}, j € {1,2,...,n}, and i≠k. ▷ Employed Bees
7: Evaluate the fitness function fit, of all new solutions in the population.
8: Keep the best solution between current and candidate solutions. ▷ Greedy Selection
9: Calculate the probability 𝑃𝑖, for the solutions 𝑥𝑖, where 𝑃𝑖= 𝑓𝑖𝑡𝑖/𝛴𝑗=1
𝑁𝑆
𝑓𝑖𝑡𝑗
10: Generate the new solutions 𝑣𝑖 ; from the solutions selecting depending on its 𝑃𝑖. ▷ Onlookers Bees
11: Evaluate the fitness function 𝑓𝑖𝑡𝑖, of all new solutions in the population.
12: Keep the best solution between current and candidate solutions. ▷ Greedy Selection
13: Determine the abandoned solution if exist, replace it with a new randomly solution 𝑥𝑖. ▷ Scout Bee
14: Keep the best solution 𝑥𝑏𝑒𝑠𝑡 found so far in the population.
15:cycle = cycle +1
16: until cycle ≤MCN. ▷ MCN is maximum cycle number
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22. ABC ALGORIDAM FLOWCHART
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23. TWO AREA POWER SYSTEM
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ACE =Area control error
B = Frequency bias
𝑃𝑡𝑖 = Tie line
1
𝑅
= Integrator circuit
F = Change in frequency
Δ

24. SIMULATIONS MODEL
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25. RESULT & SIMULATIONS
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Output of PID(1+PIDD) using ABC algorithm

26. RESULT & SIMULATIONS
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Output of 1+PIDD using ABC algorithm

27. RESULT & SIMULATIONS
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Output of PID using ABC algorithm

28. CONCLUSION
 In LFC problem, each area has its own generator(s), and it is responsible for its own load and scheduled interchanges
with neighbouring areas. Area load changes and abnormal conditions lead to mismatches in frequency and scheduled
power interchanges between areas. LFC is defined as the regulation of the power output of generators within a prescribed
area, power system and necessity of frequency control. Different plots of frequency deviation were obtained by varying
the load demand of areas. From simulation and result we found that PID(1+PIDD) is having improved system
performance in terms of settling time, maximum overshoot compared to PID and (1+PIDD) controller. It is clear that
PID(1+PIDD) controller gives much better controlling results than PID ,(1+PIDD) controlling. This signifies the
superiority of PID(1+PIDD) over PID ,(1+PIDD) controlling.
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29. RFERENCE
1. BP sahoo, Panda S (2018) “Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control”.
Sustain Energy Grids Networks.
2. Dusan Teodorovic , Mauro Dell’Orco (2005) “Bee colony optimization, a cooperative learning approach to complex transportation problems.
3. Pham DT, Kog E, Ghanbarzadeh A, Otri S, Rahim S, ZaidiM(2006) “The bees algorithm, A snovel tool for complex optimisation problems”.
4. Sahu RK, Panda S, Yegireddy NK (2014) “ A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area
interconnected power systems” .
5. Sahu RK, Panda S, Pradhan PC (2015) “Design and analysis of hybrid firefly algorithm-pattern search based fuzzy PID controller for LFC of multi
area power systems”.
6. Gheisarnejad M, Khooban MH (2019) “Secondary load frequency control for multi-microgrids: HiL real-time simulation”.
7. H. Shayeghi , H.A. Shayanfar, A. Jalili (2008) “Load frequency control strategies: A state-of-the-art survey for the researcher”
8. D.K. Chaturvedia,*, P.S. Satsangia , P.K. Kalra (1999) “Load frequency control: a generalised neural network approach”
9. Shashi Kant Pandey, Soumya R. Mohanty, Nand Kishor (2013) “A literature survey on load–frequency control for conventional and distribution
generation power systems”
10. C. T. PAN Member C. M. LIAW (1989) “An adaptive controller for power system load frequency control”
11. Anjan Bose , Richard D. Christie(1996) “Load Frequency Control Issues In Power System Operations After Deregulation”
12. Hassan Haes Alhelou , Mohamad-Esmail Hamedani-Golshan , Reza Zamani , Ehsan Heydarian-Forushani and Pierluigi Siano (2018) “Challenges
and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review”
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29

30. 30
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