187156724-computational-intelligence-based-tehnique-for-load-shedding-scheme.pdf
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COMPUTATIONAL INTELLIGENCE BASED TEHNIQUE FOR
LOAD SHEDDING SCHEME
LUKMAN HAKIM BIN HAMRON
FACULTY OF ELECTRICAL ENGINEERING
UNIVERSITI TEKNOLOGI MARA
MALAYSIA
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COMPUTATIONAL INTELLIGENCE BASED TECHNIQUE FOR
LOAD SHEDDING SCHEME
Project report is presented in partial fulfillment for the award of the
Bachelor of Electrical Engineering (Hons)
Universiti Teknologi MARA (UiTM)
LUKMAN HAKIM BIN HAMRON
Faculty of Electrical Engineering
UNIVERSITI TEKNOLOGI MARA
44404 SHAH ALAM, SELANGOR
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A report submitted to Faculty of Electrical Engineering, Universiti Teknologi MARA
in partial fulfillment of the requirement for Bachelor of Electrical Engineering (Hons).
This thesis is approved by:
……………………………….
Associate Professor Dr. Ismail Musirin
Project supervisor
Faculty of Electrical Engineering
Universiti Teknologi MARA (UiTM)
44404 Shah Alam
Selangor.
Date:………………..
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DECLARATION
It is hereby declared that all materials in this thesis are the result of my own work and
all the materials that are not the result of my own work have been clearly
acknowledged. Although, certain result on this thesis is effort from other dispute.
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ACKNOWLEGMENT
First and foremost, all praise to Allah S.W.T. The Most Gracious and Most Merciful
who had given me the strength, ability and patient upon completing this final year
project.
I wish to conveymy deepest gratitude and appreciation to my supervisor, Assoc. Prof.
Dr. Ismail Musirin for his guidance, concern, valuable time, effort, constant
encouragement and patience in supervising this project from the start until the
completion if this thesis.
I also wish to take this opportunity to express my gratitude to my family especially to
my mother and my father for supporting me along way my journey in this field. They
have encourage me throughout my education , and I will always be grateful for their
sacrifice, generosity and love. May Allah S.W.T. bless them all.
Not forget to my friends and anyone who directly or indirectly giving their support
and contribution to finished this project. May Almighty Allah bless and reward them
for their generosity. Thank you very much.
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ABSTRACT
Losses in generation and overloading effect are two phenomena that may
occur due to progressing demand at the load side. This may lead to system instability
in forms of voltage and frequency. In order to avoid this problem, the under voltage
load shedding scheme can be performed to shed some amount of load before the
disturbance occur. This paper presents computational intelligence technique for load
shedding. The study involves the development of fuzzy rules in order to make
decision on load shedding. This method functions will determine the amount of load
that needs to be shed depending on the measured minimum voltage of the system.
The result of this paper will show the performance of under voltage load shedding
scheme in determining power system stability by shedding some amount of the load
demand. The technique has been validated on the IEEE 03-bus system.
Index Terms—voltage collapse, system stability, fuzzy logic, under voltage load shedding
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TABLE OF CONTENTS
CHAPTER DESCRIPTION PAGE
DECLARATION i
ACKNOWLEDGEMENT ii
ABSTRACT iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
LIST OF ABBREVIATIONS viii
0.3 INTRODUCTION 0
0.0 INTRODUCTION 0
0.1 PROBLEM STATEMENT 0
0.0 OBJECTIVE 0
0.1 SCOPE OF THE PROJECT
0.1 RESEARCH FRAMEWORK
0.1 OVERVIEW OF THE REPORT
1
1
1
1.3 LITERATURE REVIEW 1
1.0 ECONOMIC DISPATCH (ED) 1
1.1 DYNAMIC ECONOMIC DISPATCH(DED) 1
1.1.0 Ramp Rate Constraint 8
1.0 PARTICLE SWARM OPTIMIZATION (PSO)
1.1 METHOD TO SOLVE DED
9
03
viii
0.3 METHODOLOGY 01
0.0 INTRODUCTION
01
0.1 DYNAMIC ECONOMIC DISPATCH (DED)
FORMULATION
01
0.1.0 Objective Function 01
0.1.1 Equality Constraint 01
0.1.0 Inequality Constraint 01
0.1.1 Dynamic Constraint 01
0.1.1 Fitness Function 08
0.0 PARTICLE SWARM OPTIMIZATION (PSO) 09
0.0.0 Basic PSO Algorithm 09
0.0.1 Particle’s Velocity Update 13
0.0.0 Constriction Factor Approach (CFA) 13
0.0.1 Particle’s Position Update 10
0.0.1 Representation of Particle’s Position 10
0.1 DED BASED ON PSO TECHNIQUE 10
1.3 RESULTS AND DISCUSSION 11
1.0 DATA FOR IEEE 11-BUS TEST SYSTEM 11
1.1 PSO PARAMETERS SETTING 18
1.0 SIMULATION RESULTS FOR SOLUTION
OF DED BASED ON PSO
19
1.1 ANALYSIS OF PSO METHOD ON DED
SOLUTION
01
1.3 CONCLUSION 01
1.3 RECOMMENDATIONS FOR FUTURE WORKS 01
REFERENCES 08
ix
APPENDICES 10
LIST OF FIGURES
FIGURE TITLE PAGE
1.0 Single line diagram of transmission line 1
0.0 Matrix representation of particle’s position 11
0.1 Modification of gBest according to generator constraints 11
0.0 Flow chart for DED based on PSO process 11
1.0 Variation of Cost with Power Demand Curve for 1 units
system 03
1.1 Variation of Power loss with the Load Demand for 1
units system 03
1.0 Graph of Fuel Cost against Load Demand for
comparison between PSO and Newton Raphson method 00
1.1 Convergence Characteristics of PSO Method for 1 units
system 01
x
LIST OF TABLES
TABLE TITLE PAGE
1.0
Generating Unit Capacity and Coefficients for IEEE 11-
bus system
11
1.1 Initial output power and Ramp Rate limits for IEEE 11-
bus system 11
1.0 Load Demand for IEEE 11-bus system of 11 hours 11
1.1 Transmission loss Coefficients for IEEE 11-bus system 18
1.1 PSO parameters 18
1.1 Optimal MW Generation for each unit, Transmission loss
and Fuel Cost of 11 hours 19
1.1 Comparison of PSO and Newton Raphson Result 01
1.8 Result for the Variation Number of Particles 01
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LIST OF ABBREVIATIONS
ED - Economic Dispatch
DED - Dynamic Economic Dispatch
PSO - Particle Swarm Optimization
SED - Static Economic Dispatch
AI - Artificial Intelligent
FACTS - Flexible Alternative Current Transmission Systems
DP - Dynamic programming
GA - Genetic Algorithm
SA - Simulated Annealing
EP - Evolutionary Programming
LP - Linear Programming
NLP - Non-Linear Programming
QP - Quadratic Programming
DE - Differential Evolution
ANN - Artificial Neural Network
HNN - Hopfield Neural Network
CFA - Constriction Factor Approach
IEEE - Institute of Electrical and Electronics Engineers
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CHAPTER 0.3
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
In power system operation, the balance between load demand and the available
generation is important to make sure the stability of the system is in good condition
[0]. Nowadays, there are many situation occur where the demand load have reached
the limit of an available generation in certain place. When this condition occurs, there
will be same situation as in 1331 where there was power outage in Malaysia where
many states of Malaysia’s northern peninsular, including Perlis, Perak, Penang and
Kedah due to the occurred fault. This situation happened due to the load demand used
by the user has exceed the limit that the available generation can support. From this
situation a load shedding scheme is initiated to avoid the system from collapsed [1].
There are many factories have take improvement step to prevent this phenomena
happening again by developing a new alternative extensively to ensure the power
system network operates in the normal steady state condition conveniently [0].
A system enters a state of voltage instability when a disturbance, increase in
load demand, or change in system condition causes a progressive and uncontrollable
drop in voltage [1]. The main factor for instability is the inability of the power system
to meet the demand of increased reactive power. Literally, it will cause the system
collapse.
There are several studies that indicate about voltage stability of the power
system. One of these studies is about estimating the voltage stability of power system
[1]. This study is based on the fast calculation of indicators of risk of voltage
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instability has been developed. These indicators can detect on-line voltage instability
and signal the tendency towards a critical situation.
Several methods have been developed to prevent the voltage from collapse. In this
paper load shedding is applied to the selected bus so the voltage minimum will
increase and the system become stable. This technique is proposed to make sure the
system in a balanced condition. In [1], there are several methods to perform the load
shedding technique such as under-voltage load shedding and under-frequency load
shedding. The best way to perform load shedding scheme in a system is by
minimizing the amount of load to be shed [1] for voltage collapse prevention. In [1],
the paper study about the practical approach to perform the load shedding scheme.
In order to perform the developed technique, a fuzzy logic algorithm was
proposed. This algorithm provides solution as decision making to determine which
load bus that need to be shed and how much load will be shed to make sure the system
recover to the normal operation. Fuzzy logic was a useful algorithm where it can be
used in wide area of study. In [8], fuzzy logic was used to solve the unit commitment
problem. While in [9], fuzzy load shedding based algorithm is performed by using
voltage stability indicator for averting voltage collapse. In this paper, fuzzy logic is
performed by monitoring the minimum voltage by running the load flow. Then under
voltage load shedding will be perform to get the system back to normal operation. The
variable is selected from the load flow results.
This paper presents computational intelligence based technique for load
shedding scheme. The study involves the development of fuzzy rules in order to make
decision on load shedding. Results from the experiment indicated that the proposed
technique is successful to solve the load shedding problems. The load levels increase
are divided into several different loading factors. The fuzzy technique is applied to
each case to select load bus to be shed and to calculate the amount load to be shed to
prevent voltage instability.
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1.1 PROBLEM STATEMENT
Everyday people are using equipment continuously and the load demand for
each distribution network is increasing with the increasing of electric usage among the
user. Each generation that was established in Malaysia is enough to support the load
demand in certain area depending on the load usage. There are some cases where the
load demand is higher than the generation level. This will cause voltage collapse in
the area. For example in 0991, blackout situations happen in Malaysia due to high
load usage. The reason why this situation happened is because of the hot weather at
that time. The same situation occurred in 1331 where the biggest blackout happened
in Malaysia where there is no electricity due to the fault of the main cable
transmission line grid.
As the usage of equipment is increasing, the load demand will also increase.
This condition will burden the generation to support the load demand. A generation
has their limit to support the load demand in each area. When the consumer load
demand has gone beyond the limit of available generation, it may lead to blackout.
When this situation happens, it will cause problem to all consumer. This reason
becomes the why a new method is needed to overcome this problem.
1.1 OBJECTIVE
i. To develop load shedding scheme in power system
ii. To identify the Selected bus for load shedding and amount of load demand
that should be shed for stable power system operation
iii. To improve the power balance in power system operation by using
computational intelligence
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1.4 SCOPE OF THE PROJECT
The scope of this project is to analyze the balance between the load demand
and the available generation. The data will be taken from legal resource as the first
step of this project. Later, it will be analyzed to match with the load shedding
technique. This technique is used to develop an algorithm as the solution for solving
the load shedding problem. A selected load bus will be chosen for shedding based on
the output of the develop algorithm.
The under voltage load shedding scheme is employed in order to determine
which load and amount of load that need to be shed. The voltage magnitude, active
power and reactive power at load bus will be assigned as the input variable to fuzzy
logic system. This fuzzy system will be implemented using MATLAB software. The
proposed method gives satisfactory results in term of blackouts prevention and
minimum voltage improvement.
Moreover, this project will show the performance of fuzzy logic algorithm to
be effective and useful in problem concerning the load shedding. The results of this
method will be used to decide which of the load is the most suitable to be removed for
maintaining the stability of the system.
Flow chart in Figure 0 below summarizes the involved process:
Preparing the system data
Initialize the load shedding scheme
Develop the fuzzy logic algorithm
in matlab
Determine the shedding load to
balance the system
Figure 1: Scope of project
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0.1 RESEARCH FRAMEWORK
START
KNOWLEDGE
ACQUISITION
MATLAB
PROGRAMMING
FUZZY LOGIC
ALGORITHM LOAD
SHEDDING DEVELOPMENT OF SIMPLE
LOAD SHEDDING TECHNIQUE
AND UVLS
DEVELOPMENT OF
CONCEPTUAL MODEL OF
LOAD SHEDDING
DEVELOP THE
PROGRAMMING CODES IN
MATLAB
IMPLEMENTATION OF FUZZY
LOGIC ALGORITHM FOR LOAD
SHEDDING
Figure 1: Research framework
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0.1 OVERVIEW OF THE REPORT
This thesis consist of five chapters explain about solving under voltage load shedding
(UVLS) schemes implemented by using fuzzy logic system. Chapter 0 describes an
introduction of the project which includes the objective of this research and also scope
of work to complete this project.
In Chapter 1, the theory and basic of voltage stability, under voltage load shedding
and fuzzy logic systems are reviewed and explained properly. The summary are
include the full details of problem in power system, theory of UVLS scheme, theory
of fuzzy logic and its application and some literature review on method to solve the
load shedding problems.
This project thesis was followed by the design methodology that explained clearly in
Chapter 0. This chapter explains the DED formulation algorithm including all the
constraints and the PSO techniques algorithm and lastly implementation of PSO
techniques to DED problems. This chapter also indicates the flow chart of DED based
on PSO techniques.
Next is Chapter 1 that illustrated all the results obtained together with the discussion
of the results. All the tables and graph plotted are discussed clearly in this chapter
including the analysis of PSO techniques on DED solution.
On the Chapter 1, a conclusion that has been made upon the result obtains and the last
chapter is Chapter 1 which discusses the recommendations for future works in order to
improve the solution for DED problems. The last parts of this thesis are the references
and appendix.
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CHAPTER 1.3
LITERATURE REVIEW
1.0 POWER SYSTEM VOLTAGE STABILITY
In a power system, the operation condition should be in a stable condition
where the voltage and the frequency is in equilibrium state. It means that the criteria
of the system operation should be meet the various operational, and it should also be
secure in the event of any credible contingency. That is why it is important to
maintaining the system in stable condition and secures the power system operation.
Nowadays, the problem and the challenges keep coming causing the power system
that being operated closer to their stability limits and the voltage in the system
dropping where it become unstable. Voltage instability and voltage collapse have been
considered as a major threat to present power system networks due to their stressed
operation. The disturbance that occur cause the voltage decreasing continuously and
lead to voltage collapse where the value of the voltage below its normal value. To
prevent the system collapse, lots of mechanism has been develop such as VAR
compensators, undervoltage load shedding and underfrequency load shedding [03].
Voltage collapse is the process by which the voltage falls to a low, unacceptable value
as a result of an avalanche of events accompanying voltage instability [00]. Once
associated with weak systems and long lines, voltage problems are now also a source
of concern in highly developed networks as a result of heavier loading.
In this chapter, the concept of voltage stability and the conventional method of
voltage stability analysis which is undervoltage load shedding is presented. Simulation
results on test power systems are presented to illustarate the problem of voltage
stability and the under voltage load shedding scheme to analyze the problem. The
undervoltage load shedding scheme the being implemented by using one of the
artificial intelligence technique which is called fuzzy logic.
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1.1 VOLTAGE STABILITY
According to the IEEE Power System Engineering Committee, voltage
stability is being defined as “Voltage stability is the ability of a system to maintain
voltage so that when load admittance is increased, load power will increase, and so
that both power and voltage are controllable.” [01]. If there is disturbance occur and
the voltage in the system dropping, it will become voltage instability and lastly it will
cause voltage collapse. And nowadays, this voltage collapse is one of the major
problems which electric power networks might face [00]. The voltage stability
practically can be classified into two subcategories which are Long term and Short
term.
1.1.1 LONG TERM VOLTAGE STABILITY
In power system, the long-term voltage stability involves rather slow acting
equipment such as tap changing transformers, thermostatically controlled loads, and
generator current limiters. To analyze system dynamic performance, the long term
simulation is required. In these studies, stability is usually determined by the resulting
outage of equipment, rather than the severity of the initial disturbance.
1.1.1 SHORT TERM VOLTAGE STABILITY
The short-term voltage stability in power system involves dynamics of fast
acting load components such as induction motors, electronically controlled loads, and
HVDC converters. The study period of interest is in the order of at most several
seconds, and analysis requires solution of appropriate system differential equations
which is similar to analysis of rotor angle stability. Dynamic modeling of loads is
often essential. In contrast to angle stability, short circuits near loads are important.
This kind of voltage instability could easily happen in the result of a serious fault
occurrence in the power system network. So, it would have close relations with
electric power system protection methods [1].
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1.0 VOLTAGE INSTABILITY
Figure 1.3-0: Power System with Remote Generation
Fig. 1.0 illustrates a simplified power system with a remote generator
supplying a substantial portion of the load at the load center through six transmission
lines. Es is the voltage at the remote generator buses 1 and Eg is the voltage at the
load center buses. As lines between the remote generators and the load center trip, the
MW power flows over fewer lines resulting in increased Var losses.
Figure 1.3-1: Real Power (MW) vs. Voltage (P-V) Curve
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Figure 1.1 illustrates how voltage decays as lines trip. In power system, the
utilities system planners use this type of P-V curves analysis as an analysis tool to
determine the real power transfer capability across a transmission interface to supply
local load. Generally, all the planning engineers call this type of curves as nose
curves. The reason why this type of P-V curves is called as nose curves is because
when there is condition starting from a base-case system (all lines in-service),
computer-generated load flow cases are run with increasing power transfers while
monitoring voltages at critical buses. When power transfers reach a high enough level,
a stable voltage cannot be sustained and the system voltage collapses. As illustrates in
Figure 1.1, the shape of the nose of the curve depends on the nature of the load at the
load center. High levels of motor load combined with capacitor bank support of load
center voltage tend to make the voltage drop very rapidly for a small increase of
power at the nose of the curve.
The set of P-V curves illustrates that for baseline conditions shown in curve A,
the voltage remains relatively steady (changing along the vertical axis) aslocal load
increases. System conditions are secure and stable to the left of point A0. After a
contingency occurs, such as a transmission circuit tripping, the new condition is
represented by curve B, with lower voltages (relative to curve A). This is because the
power being transmitted from the remote generators now follows through five, rather
than six, transmission lines. The system must be operated to stay well inside the load
level for the nose of curve B. If the B contingency occurs, then the next worst
contingency must be considered. The system operators must increase local generation
(Eg) to reduce the power being transmitted for the remote generators to reduce losses,
as well as increase voltage at the load center to within the safe zone, to avoid going
over the nose of curve C.
1.1 DISTURBANCE IN POWER SYSTEM
Usually all the electrical energy that has been provided by the electrical utility is
safe and reliable. The problem comes when there are disturbance, disruptions,
irregularities and nature of electricity occurred. For an example there are lightning,
equipment failures, and high winds can cause power line disturbance and also will
affect the voltage in the system. If electrical equipment is being used and the is power
disturbance occur, it will cause data or memory losses, altered data and other
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functional errors, as well as equipment damage. And if there is no preventive method
towards this problem, then it may cause scheduling problems, downtime and
expensive troubleshooting. Before proceeding to the preventive method, first thing to
do is by understanding the causes of the problems.
1.1 CAUSES OF DISTURBANCE
There are several types of irregularities that affect electrical power which are
surges, sags. transients, noises and power outage.
1.1.0 SAGS
Sags is the condition when the voltage in a system is lower than the stable
range which caused by power failures, down lines, utility recloser operations and
storms. In power system, sags are the most common problem compared to others and
it can be assume that voltage below 4..0p.u. is considered sags.. This problem can be
corrected by using backup power sources such as UPSs, generators or voltage
restoration technologies.
1.1.1 SURGES
The different between surges and sags is the surges is the condition when the
voltage in a system is above from the stable range. The voltage that considered as
stable range is between 4..0p.u. to 1.40p.u.. The affect of surges in a system is it will
damage the equipment used. They may be seen more frequently in facilities with
rapidly varying electrical loads, often caused by the switching on / off of electric
motors (inductive load switching). Air conditioners, electrical power tools, furnace
igniters or ignition systems, arc welders, electrostatic copy machines and elevators are
most likely to create surges.
1.1.0 TRANSIENTS
Transient can be define as a change happen in voltage causes by the short
duration and sharp impulse. In power system and if there is disturbance occur, a
transient voltage may exceed the normal voltage level by five or ten times. Present,
the transients normally caused by a lightning strike and the normal operation of
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electrical equipment such as switching on/ off electrical motors. Normally, the
presence of transient voltage can only be detected with special monitoring equipment.
1.1.1 NOISES
Noises can be classified as interferences that can be generated by any electrical
equipment. Usually the noise comes from equipment that not being installed correctly
and properly. This equipment may include: radio transmitters, fluorescent lights,
computers, business machines and even simple devices such as light sockets, wall
receptacles, plugs and loose electrical connections. These types of disturbances can
result in computer errors.
1.1.1 POWER OUTAGE
In power system, power outage can be defined as total losses of power. This
condition can be momentarily or last for extended periods of time. Generally, the
power in generation system must be equal to the load demand by customer plus the
losses. So if the load demand increase higher than the generation can support, it may
lead to the power outage. Besides that, the power outage also can be caused by
electrical load switching in utility power stations. Even a momentary outage, of only a
fraction of a second, will affect a computer and can result in data loss and the need for
data re-entry or reprogramming.
1.1 UNDERVOLTAGE LOAD SHEDDING SCHEME
Theoretically, the philosophy of UVLS is that when there is a system
disturbance and the voltage drops to a pre-selected level for a pre-determined time,
then selected loads are shed. It means that when there is voltage instability occurs due
to a disturbance and the load shedding is performed, the voltage will recover to
acceptable level thereby avoiding a more widespread system voltage collapse.
Practically, combination between protection engineers and system planners, who
together can determine the amount of load and time in the shedding program is
required to develop the undervoltage load shedding program. The system planning
engineers will analyze numerous studies using P-V curves as well as other analytical
methods to determine the amount of loads that to be shed to retain voltage stability
under credible contingencies. Voltage collapse is most probable under heavy load
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conditions where large amounts of power are to be transported from remote generation
sites and the bulk of the system load consists of motors.
In under voltage load shedding scheme, there are two types that being applied
in the system which are centralized and decentralized (distributed). A centralized
scheme is a method where it has undervoltage relay installed at key system buses
within the area and trip information is transmitted to shed load at various locations. As
the security is added to the system, sometimes the additional logic is applied. While a
decentralized scheme is where it has relays installed at the loads to be shed. The relays
will start to shed the load at the selected location when the voltage condition at the
locations begins to collapse. Moreover, this type of scheme is similar to the under
frequency load shedding schemes. Many of these schemes are categorized as “special
protection“or “wide area” protection schemes. These schemes require high-speed and
reliable communication to properly operate.
In [00, 01], it is been shown that load shedding is an effective counter measure
against voltage collapse. As been mention before, generally undervoltage load
shedding scheme is designed to shed a specific amount of load from one or more
locations within a power system after finite amount of time upon detecting the onset
of voltage collapse. There are three main areas for consideration in under voltage load
shedding which are the amount of load to shed, and the location where load is to be
shed.
1.6.1 THE AMOUNT OF LOAD TO BE SHED
Theoretically, there are many research indicate that as certain the amount of
load that is appropriate to shed under given conditions. If there is less load that been
trip than necessary, it is obvious that it would be not effective in arresting voltage
collapse. But if tripping too much load may result in transitioning the system from an
under-voltage to an over-frequency condition as the resulting system will have more
generation than load.
Load characteristic in power system play an important role in determining the
ability of the system to regain a stable equilibrium after a disturbance. The incorrect
presumption of load characteristics in load-flow and dynamic studies may render a
UVLS scheme ineffective and perhaps even inadvertently impose an over-frequency
14
condition upon the power system. In [01], the paper discuss about procedure to
calculate the amount of load to be shed where the amount of load to be shed is
calculated based upon the difference between the pre-contingency (steady-state)
power drawn by load and the instantaneous power drawn at the instant of system
disturbance. In this case, dynamic load model parameters are estimated on-line using a
non-linear least squares method in order to calculate the load shed amount.
In an actual power system, the granularity with which load can be shed is
limited due to pragmatic considerations. In general, the smallest block of load that can
be shed is equal to the load served through one substation-class distribution breaker
since it is this breaker that is employed to interrupt the load. Furthermore, the
distribution feeders served out of a particular substation in most cases have different
aggregate load characteristics and demand profiles making the predetermination of the
amount of load available for shedding challenging. This means that the design of a
UVLS should incorporate the impact of errors as a result of the differences between
the load that is presumed to be shed and the load that is actually shed.
The design should also take into account the impact of intentional load
shedding on distribution feeders serving, for example, police and fire stations,
hospitals, schools, power plant or bulk transmission system control centers, prisons
and army bases.
1.6.1 THE LOCATION WHERE LOAD IS TO BE SHED
An important factor to consider within a UVLS design is the location where
load is shed. Small disturbance analysis coupled with dynamic simulation and in some
cases optimal power flow methodology is some tools employed in the determination
of the location of load shed [01]. In this case, the load buses are ranked in the order of
the weakest to the strongest. The weakest bus tends to have the highest component
and tends to be most susceptible to voltage collapse given the relatively large reactive
power consumption for a small reduction in bus voltage. Therefore, often it is this bus
that is the most appropriate candidate for load shedding initially.
15
In [01], the proposed UVLS scheme detects voltage collapse at every bus in
the ten bus system considered. Rather than shedding load at the weakest one through
ranking buses, each bus is monitored for voltage collapse and upon detection of this,
the UVLS is triggered at that bus. A major drawback to this approach, as noted by the
authors of the paper, is that the optimum amount of load will not be shed given that
the power-voltage characteristics of the lines would change upon load shed at one bus.
Furthermore, this approach does not distinguish between the bus at which the reactive
power demand is increased and the adjacent buses whose voltages follow suit. This
means that the load at adjacent buses may be shed in the case where load rejection at
the weakest bus alone would have arrested voltage collapse.
In [01], the system overcomes this approach by pre-determining the weakest
buses in the system under various contingencies (N-0, N-1 and N-0). In this instance,
training scenarios consisting of eight different system configurations were subjected to
these contingencies. The resulting unstable scenarios were identified and the weakest
buses were noted for each unstable scenario. This was followed by a common ranking
of the load buses as it was postulated that the optimal load shedding locations will be
nearly the same for all unstable scenarios of the set. The preceding approach identifies
the common weakest buses for all conceivable contingencies and optimizes the
location of the load to be shed.
1.1 CONCEPT OF UNDER VOLTAGE LOAD SHEDDING
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When a transmission system becomes stressful due to the overload, the voltage
instability or voltage collapse could be experience by the system [16]. The philosophy
of UVLS is that whenever the system is perturbed and voltage drops to a certain pre-
selected level for a certain pre-determined time period, then selected loads may be cut
off [17]. In some research, by shedding some of the loads in a system the voltage
magnitude will recover to its normal level. In practical, load shedding schemes
requires coordination between protection engineers and system planners to set up the
amount to be shed without affecting its security.
Data below shows the acceptable range value to be the reference during
generation of data. Some function has been created to devide the results to determine
their range of stable voltage.
Min(Vm)<1995 = unstable
1995<min(Vm)<1915 = stable
1.8 FUZZY LOGIC
Seminal paper on fuzzy logic was introduced by Prof. Lofti A. Zadeh in 1.60
[1.].Since then, many developments have taken place in different parts of the world.
Since the 1.94s Japanese researchers have been the primary force in the
implementation of fuzzy theory and now have thousands of patents in the area.
The world response to fuzzy logic has been varied. On the one hand, western
cultures are mired with the yes or no, guilty or not guilty, of the binary Aristotelian
logic world and their interpretation of the fuzziness causes a conflict because they are
given a negative connotation. On the other hand, Eastern cultures easily accommodate
the concept of fuzziness because it does not imply disorganization and imprecision in
their languages as it does in English.
Practically, fuzzy logic is a powerful problem-solving methodology with a
myriad of applications in embedded control and information processing. Fuzzy
provides a simple way to draw definite conclusions from vogue, ambiguous or
imprecise information [14]. Moreover in computational intelligence, fuzzy logic
resembles human decision making with its ability to work from approximate data and
find precise solutions
17
Classical set theory is based on the fundamental concept of a set, in which
individuals are either a member or not a member. A sharp, crisp, and ambiguous
distinction exists between a member and a non-member for any well-defined set of
entities in this theory, and there is a very precise and clear boundary to indicate if an
entity belongs to a set. Thus, in classical set theory an element is not allowed to be in a
set (0) or not in a set (3) at the same time. This means that many real-world problems
cannot be handled by classical set theory. On the contrary, the fuzzy set theory accepts
partial membership values μ ƒ ϵ [3, +0], and therefore, in a sense generalizes the
classical set theory to some extent.
As Prof. Lotfi A. Zadeh suggests by his principle of incompatibility: “The
closer one looks at a real-world problem, the fuzzier becomes the solution,” and thus,
imprecision and complexity are correlated [10]. Complexity is inversely related to the
understanding we can have of a problem or system. When little complexity is
presented, closed-loop forms are enough to describe the systems. More complex
systems need methods such as neural networks that can reduce some uncertainty.
When systems are complex enough that only few numerical data exist and the
majority of this information is vague, fuzzy reasoning can be used for manipulating
this information.
1.8.2 Concept of Fuzzy Logic
A simple way to define fuzzy logic is logical system which is the extension of
mutivalued logic. In a specific way, fuzzy logic is almost synonymous with the theory
of fuzzy sets, a theory which relates to classes of object with unsharp boundaries in
which membership is a matter of degree [11]. Fuzzy inference is the process of
formulating the mapping from a given input to an output using fuzzy logic. The
mapping then provides a basis from which decisions can be made.
In MATLAB, fuzzy logic can be implementing by using fuzzy logic toolbox as
the decision making. The fuzzy logic system consists of three parts which are
fuzzification, fuzzy inference and defuzzification [11, 10]. In fuzzification, it will
involve the process of transforming input variable into a membership for linguistic
terms of fuzzy sets. While fuzzy inference system is used as a drawing conclusion
from the set of fuzzy rules. The fuzzy rule is a set of if-then linguistic term [01]. For
the defuzzification, it converts the fuzzy output values back into output actions. In this
18
paper, fuzzy logic algorithm is allowed to determine the suitability of each bus and the
one with the highest suitability chosen for load shedding. The FLS Editor displays
general information about the fuzzy inference system.
1.8.1 Membership Function
The purposed of membership function is to determine or find the input and
output of the system. It is a curve that defines how each point in the input space is
mapped to a membership value between 3 and 0.It is the first step of the fuzzy logic
control process where a fuzzy algorithm categorises the information entering a system
and assigns values that represent the degree of membership in those categories.
In fuzzy logic, the membership function is a graphical representation of the
magnitude of participation of each input. It associates a weighting with each of the
inputs that are processed, defined functional overlap between inputs, and determines
and output response. The rules use the input membership values as weighting factors
to determine their influence on the fuzzy outputs sets of the final output conclusion.
Once the the functions are inferred, scaled, and combined, they are defuzzified into
crisp output which drives the system.
Input membership functions themselves can take any form the designer of the
system requires triangles, trapezoids, bell curves or any other shape as long as those
shapes accurately represent the distribution of information within the system, and as
long as a region of transition exists between adjacent membership functions.
19
Figure 1: Membership function
Due to their simple formulas and computational efficiency, both triangular
membership functions and trapezoidal membership functions have been used
extensively, especially in real-time implementation. However since the membership
functions are composed of straight-line segments, they are not smooth at the switching
points specified by the parameters.
1.8.0 Fuzzy Inference System
Fuzzy inference is the process of formulating the mapping from a given input
to an output using fuzzy logic. The mapping then provides a basis from which
decision can be made, or pattern discerned. The process of fuzzy inference involves
all of the pieces that are described which are the membership function or fuzzification,
fuzzy logic operators, and the fuzzy rules. In fuzzy logic toolbox, there are two type of
fuzzy logic system which is Mamdani and Sugeno.
Mamdani’s fuzzy inference method is the most commonly used in fuzzy
methodology. Mamdani’s method is among the first control systems built using fuzzy
set theory. It was proposed in 1.9 0 by Ebrahim Mamdani as an attempt to control a
steam engine and boiler combination by synthesizing a set of linguistic control rules
obtained from experienced human operators. Mamdani’s effort was based on Lofti
Zadeh’s 1.91 paper on fuzzy algorithm for complex systems and decision processes.
21
Although the inference process describe differs from the methods described I the
original paper, the basic idea is much the same.
Mamdani type expects the output membership functions to be fuzzy sets. After
the aggregation process, there is fuzzy set for each fuzzy output variable that needs
fdefuzzification. It is possible and in many cases much more efficient to use a single
spike as the output membership functions rather than a distributed fuzzy set. This is
sometimes known as a singleton output membership function and it can be thought of
as a pre-defuzzified fuzzy set. It enhance the efficiency of the defuzzification process
because it greatly simplifies the computation required by the more general Mamdani
method, which finds the centroid of a two dimensional function. Rather than
integrating across the two-dimensional function to find the centroid, that used the
weight average of a few data points. Sugeno type systems support this type of model.
Generally, Sugeno type systems can be used to model any inference system in which
the output membership functions areeither linear or constant.
1.8.1 Defuzzification
A defuzzifiation process is use to obtain the crisp output. This result is
obtained from fuzzy inference system where it maps an input vector to a crisp output.
The input to the defuzzification process is a fuzzy set (the aggregated output fuzzy
set), and the output of the defuzzification process is a single number. Many
defuzzification techniques have been proposed in the literature. The most commonly
used method is the centroid. Other methods include the maximum, the means of
maxima, height, and modified height method.
21
CHAPTER 0.3
METHODOLOGY
0.0 INTRODUCTION
The objective of a UVLS scheme is to restore reactive power balance in the
power system, to prevent voltage collapse and to keep a voltage problem within a
local area rather than allowing it to spread out by shedding some loads [18]. In power
system, the power generated by the generation system must be equal to the load
demand and the total losses. If the load demand is higher than the generation can
support, the it may lead to voltage collapse. To solve the problems, fuzzy logic has
been implemented to the UVLS schemes by testing it o IEEE 03 bus system that has
six generators, four under load tap changing transformers, two shunt capacitor and
thirty seven lines. The program for the implementation of fuzzy logic to the UVLS
schemes was done using MATLAB programming.
0.1 UNDERVOLTAGE LOAD SHEDDING SCHEME
0.1.0 Preparing System Data
The test system used in this study is the IEEE-03 RTS. The system has six
generators, four under load tap changing transformers, two shunt capacitor and thirty
seven lines. In the base case the total system load is 1.801 pu, the swing bus (bus
number 0) generates real power of 1.1181 pu, while the other generators generate 3.1
pu real power. Figure 0 illustrates the single line diagram of IEEE 03-bus system.
22
Figure 4-1: IEEE 14-BUS TEST SYSTEM
(Source: ljs.academicdirect.org)
Fuzzy logic load shedding need data to perform the rules. As IEEE 31 bus
system, it has five generator buses, one slack bus, 6 intermediate buses and eighteen
load buses. The load shedding technique is performed by creating several conditions.
Load factor is increased in order to indicate load variation in the system. By
increasing the load bus, the system stability will change and may cause voltage
instability due to the load increase in the system. Load shedding then perform by
selecting the weakest bus.
1.1.1 Power Flow Analysis
Power flow analysis, coomonly referred as load flow is an important tool of
power system analysis and design. It is use for planning, operation and economic
scheduling. In this project, the transmission system is modelled by a set of buses or
23
nodes interconnected y transmissionlink. Generators and loads, connector to various
nodes of the system inject and remove power from the transmission system. However
in power system, power are known rather than current. So the resulting equation is in
terms of power, known as the power flow equation become non-linear and must be
solve by iterative solution. The most common technique used for the iterative solution
of non-linear algebraic equation are Newton-Raphson, Gauss-Seidel and Quasi-
Newton methods. Newton-Raphson load flow is implement in this project to get the
desired output.
1.1.1.1 Newton-Raphson load flow
In power system, this method is widely used to solve the simultaneous algebraic
equations. Newton-Rahpson method is a successive approximation procedure based
on an initial estimate of the unknown and the use of Taylor’s series expension.
Because of its quadratic convergence, Newton-Raphson method is mathematically
superior to the Gauss-Seidel method and is less prone to divergence with ill-
conditioned problems. For large power systems, the Newton-Raphson method is found
to be more efficient and practical. The number of iterations required to obtain a
solution is independent of the system size, but more functional evaluations are
required at each iteration. Since the power flow problem, real power and voltage
magnitude are specified for the voltage-controlled buses, the power flow equation is
formulated in polar form.
1.1.1 Load Shedding Algorithm
Load shedding algorithm involves several steps before it complete. First thing
to do is by creating the cases. Generally, it is a fact that in base case, the system is
already stable. UVLS scheme is perform when the system in unstable. To makes
system unstable, the values of the load at the load bus need to be increased. The load
bus consists of active power (Pd) and reactive power (Qd).
After increasing the value of voltage in the load bus, the Newton-Raphson load
flow will execute. Upon execution, the programme will read the bus data of IEEE 31
RTS and run the power flow solution. The programme also will call necessary
routines to display the desired results. Upon completion of the load flow, the
24
programme will display minimum voltage in the system, the new value of the load,
and the minimum voltage at each bus. From the results, it can be determine whether
the system become unstable or not. If the system becomes unstable, then the UVLS
scheme can be perform. If the system still stable, then the programme will increase
again the load until the system become unstable. For this project, several cases is
developed to perform analysis of the system.
As been discussed, UVLS scheme is perform by selecting the appropriate
location for load shedding before it can shed the load. In IEEE 31 bus RTS, the
programme will determine the weakest bus in the system to perform the load
shedding. When the load at the weakest bus has been shed, then the Newton-Raphson
will run again the power flow solution to determine whether the system become stable
or not. If yes, it means that the programme is success but if no the programme need to
shed again the load in the system so that the system comeback to the stable state. The
step to perform the UVLS scheme can be found in Figure 4.
The used of fuzzy logic system in this project is it will implement the method
of undervoltage load shedding by determining the suitable location of load shedding
and how much the value of the load that need to be shed. The fuzzy logic will use
IF… THEN rules to control the input and output variable.
25
Figure 3-1: UVLS flowchart
26
0.0 FUZZY LOGIC SYSTEM
When there is disturbance or an unexpected event occurs in a large network of
power system, in some cases the probability and uncertainties of the incidence
represented. However, it is made clear that some of the uncertain functions are
intrinsically fuzzy in nature and difficult to handle to handle effectively by
probability. By using fuzzy logic, it provide a good solution that is not easily solve by
other methods and are readily applicable to power system problems [14]. The function
of fuzzy logic in this project as been mention before is to determines the suitability of
each bus for load shedding where the bus with the lowest value of the voltage is
chosen as the most appropriate bus for load shedding. The same system is develop in
fuzzy logic to determine the amount of the load that need to be shed where the fuzzy
will decide how much of the amount in the particular bus that need to be shed to
restore back the system into stable condition. The input variable and the output has
been develop and the rules has been created.
0.0.0 Bus Selection for load shedding
The fuzzy FIS Editor for selected bus load shedding is illustrated in Figure 0, 1
and 1. The input variable of the FIS Editor are voltage magnitude (Vm) which divides
into four categories as shown in Figure 1 and loading factor in Figure 0. The output is
the percentage of selected bus to be shed also divides into 1 categories as shown in
Figure 1.
Inputs: Lf (Loading factor) Trapezoidal membership function as shown in Figure 0
VM (Voltage Magnitude) Triangle membership function as shown in Figure 1.
Output: PSBLS (Percentage Selected Bus Load Shedding) Triangle membership
function as shown in Figure 1.
27
Figure 3-1: loading factor
Figure 3-1: Voltage Magnitude (Vm)
Figure 3-1: Percentage selected bus
In fuzzy logic system, an IF-THEN basic rule-based system is used. IF
statement is refer as antecendent while THEN statement is as consequent. In this
section, fuzzy rule system is determined to form decision on the fuzzy input derived
from the voltage magnitude and loading factor. For this fuzzy to find the selected bus
for load shedding, 1 rules were developed which are:
Rule 0: IF loading factor is 1.1 AND Voltage magnitude is low THEN percent
selected bus is high
Rule 1: IF loading factor is 1.1 AND Voltage magnitude is medium THEN percent
selected bus is high-medium
Rule 0: IF loading factor is 1.1 AND Voltage magnitude is high-medium THEN
percent selected bus is medium
Rules 1: IF loading factor is 1.1 AND Voltage magnitude is high THEN percent
selected bus is low
28
As illustrated in Figure 03, the rows of plots represent the rules while the
columns represent the variables. The first two columns of plots (yellow) show the
membership functions of the input variables, while the fourth column of plots (blue)
shows the membership functions of the output.
0.0.1 Algorithm for Amount of Load to be Shed
The fuzzy FIS Editor for selected bus load shedding is illustrated in Figure 1,
1, 8 and 9. The input variable of the FIS Editor are voltage magnitude (Vm), active
power (Pd) and reactive power (Qd) while the output is the percentage amount of load
to be shed.
Inputs: Pd (Active Power) Triangle membership function as shown in Figure 1
Qd (Reactive Power) Triangle membership function as shown in Figure 1
VM (Voltage Magnitude) Trapezoidal membership functions as shown in
Figure 8.
Output: PAL (Percentage amount of load to be shed) Triangle membership function
as shown in Figure 9.
29
Figure 4-8: Active Power (Pd)
Figure 4-.: Reactive Power (Qd)
Figure 4-14: Voltage Magnitude (Vm)
Figure 4-11: Percentage Amount Load to be shed
The fuzzy analysis of this method was developed using the same technique as
described in the previous method. The fuzzy rules to find the amount load to be shed
are as in Table 0. It is necessary to establish a meaningful system for representing the
linguistic variables in the matrix. For this case the following will be used:
31
Table 1: Fuzzy decision matrix
AND
Voltage Magnitude
L LM M HM H
Pd,
Qd
L M M LM LM L
LM HM M M LM LM
M HM HM M M LM
HM H HM HM M M
H H H HM HM M
“L”: “low”
“LM”: “low-medium”
“M”: “medium”
“HM”: “high-medium”
“H”: “high”
11 fuzzy rules are derived and here are some examples of the fuzzy rules listed in
Table 0:
Rule 0: IF Pd is low AND Qd is low AND Voltage magnitude is low THEN
percent load to be shed is medium
Rule 1: IF Pd is low-medium AND Qd is low-medium AND Voltage
magnitude is low THEN percent load to be shed is high-medium
When fuzzy rule has multiple antecedents or input variable, the fuzzy operator
AND for minimization operator is used to obtain a single number that represents the
result of the antecedent evaluation. Fuzzy rules involve the operations between input
fuzzy sets, as illustrated graphically in Figure 03. It is based on fuzzy inference
described previously.
31
Figure 4-11: Fuzzy rules analysis
As illustrated in Figure 03, the rows of plots represent the rules while the
columns represent the variables. The first three columns of plots (yellow) show the
membership functions of the input variables, while the fourth column of plots (blue)
shows the membership functions of the output.
32
CHAPTER 1.3
RESULTS AND DISCUSSIONS
491 INTRODUCTION
Based on the developed methodology, all the results and discussion of the
Under-voltage Load Shedding scheme are presented in this chapter. The results
include output of fuzzy logic system to determine the bus Selection for load shedding
and the amount that need to be shed to stabilize the system.Several cases has been
selected to represent the output of the program. The cases includes of loading factor
from base case until loading factor = 1. This study is conduct to show how reliable of
computational intelligence system to perform the load shedding in a system.
1.0.0. loading factor = 0.1
Determination of selected bus for load shedding using the proposed fuzzy
system is shown in Figure 00. This fuzzy system performed by referring the data from
the load flow results as shown in Table 1. Loading factor = 0.1 is selected as the test
case conditions.
Table 1: Fuzzy output for selected bus of load shedding at loading factor 0.1
Bus
No.
Minimum
Voltage
(Vm)
Percentage
selected
bus (%)
Bus
No.
Minimum
Voltage
(Vm)
Percentage
selected
bus (%)
0 0.3133 11.1 01 0.3398 00.0
1 0.3103 01.1 01 0.3303 00.0
0 3.9980 01.1 08 3.9818 09.1
1 3.9819 09.0 09 3.9831 10.1
1 3.9833 10.1 13 3.9811 09.0
1 3.9813 09.1 10 3.9899 01.8
33
1 3.9103 11.1 11 3.9931 01.1
8 3.9933 01.8 10 3.9819 13.1
9 0.3111 01.1 11 3.9103 10.1
03 0.3381 00.1 11 3.9131 11.0
00 0.3813 13.1 11 3.9111 10.1
01 0.3190 00.9 11 3.9808 13.8
00 0.3103 11 18 3.9813 13.1
01 0.3318 00.1 19 3.9113 19.1
01 3.9991 00.1 03 3.9018 11.0
Based on the newton-raphson load flow results, it is shown that the minimum
voltage of the system has dropped below the stable condition which at 3.9018 p.u..
Fuzzy logic system operates by using the data from newton-raphson load flow results
to determine the suitable bus for load shedding.
Figure 00: FIS for Selected Bus for Load Shedding at Bus (03) with 0.1 loading
factor
As illustrated in Table 1, it shows that bus 03 is the weakest in the system.
Therefore bus 03 is selected as the appropriate bus to perform the load shedding.
34
Figure 01 shows the fuzzy based load shedding system to determine the amount load
to be shed at bus 03.
At bus 03 with 0.1 loading factor, the value of active power and reactive
power are 01.81MW and 1.11MVAR respectively.
Figure 00: FIS for percentage amount to be shed at Bus (03) with loading factor = 0.1
Based on the Figure 01, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 11.06. As the results, the amount of load to
be shed is 9.1138MW and 0.1001MVAR. The minimum voltage at the system
increases from 3.9018 p.u. to 3.9100 p.u. at bus 11. It is shown that the system is
improved to a stable condition.
As the result, the total amount load to be shed in loading factor = 0.1 is 9.1138MW
and 0.1001MVAR. At loading factor = 0.1, the program need to perform 0 stage of
load shedding to get back the system to a stable condition.
1.0.1. loading factor = 0.1
Determination of selected bus for load shedding using the proposed fuzzy
system is shown in Figure 01. This fuzzy system is performed by referring to the data
35
from the load flow results as shown in Table 0. Loading factor = 0.1 is selected as the
test case conditions.
Table 0: Fuzzy output for selected bus of load shedding at loading factor 0.1
Bus
No.
Minimum
Voltage (Vm)
Percentage
selected bus (%)
Bus
No.
Minimum
Voltage (Vm)
Percentage
selected bus (%)
0 0.313 11.1 01 3.9919 01.8
1 0.3103 01.1 01 3.9883 08.1
0 3.9901 01.0 08 3.9191 11.1
1 3.9181 11 09 3.9111 11.8
1 3.9833 10.1 13 3.9109 10.9
1 3.9111 11.1 10 3.9110 11.1
1 3.9119 11.9 11 3.9118 11.1
8 3.9833 10.1 10 3.9111 11.1
9 0.3010 01.8 11 3.9119 18.0
03 3.9910 01.1 11 3.9111 18.8
00 0.3813 13.1 11 3.9113 11.1
01 0.3010 01.8 11 3.9110 11.0
00 0.3103 18.9 18 3.9110 10.9
01 3.9903 01.1 19 3.9011 11.1
01 3.9810 09.1 03 3.9011 13.0
Based on the newton-raphson load flow results, it is shown that the minimum
voltage of the system has dropped below the stable condition which is 3.9011 p.u.
Fuzzy logic system operated by using the data from newton-raphson load flow results
to determine the selected bus for load shedding.
36
Figure 01: FIS for Selected Bus for Load Shedding at Bus (03) with loading factor =
0.1
From the result above, it shows that bus 03 is the weakest in the system.
Therefore bus 03 is selected as the appropriate bus to perform the load shedding.
Figure 01 shows the fuzzy based load shedding system to determine the amount load
to be shed at bus 03.
At bus 03 loading factor = 0.1, the value of active power and reactive power
are 01.9MW and 1.81MVAR respectively.
Figure 01: FIS for percentage amount to be shed at Bus (03) with loading factor = 0.1
37
Based on the Figure 01, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 10.16. As the results, the amount of load to
be shed is 00.0111MW and 1.3019MVAR. The minimum voltage in the system
increase from 3.9011 p.u. to 3.9119 p.u. which occur at bus 11. It shows that the
system is still in unstable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor= 0.1, the value of
active power and reactive power are 1.11MW and 0.11MVAR respectively.
Figure 01: FIS for percentage amount to be shed at Bus (11) with loading factor = 0.1
Based on the Figure 01, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 19.16. As the results, the amount of load to
be shed is 0.038MW and 1.3111MVAR. The minimum voltage in the system
increase from 3.9119 p.u.to 3.9133p.u. which occur at bus 09. It is shown that the
system is improved to a stable condition.
As the result, the total amount load to be shed in loading factor = 0.1 is
01.1131MW and 1.3110MVAR. At loading factor = 0.1, the program need to
perform 1 stages of load shedding to get back the system to a stable condition.
38
1.0.0. loading factor = 0.1
Determination of selected bus for load shedding using the proposed fuzzy
system is shown in Figure 01. This fuzzy system is performed by referring to the data
from the load flow results as shown in Table 0. Loading factor = 0.1 is selected as the
test case conditions.
Table 1: Fuzzy output for selected bus of load shedding at loading factor 1.6
Bus
No.
Minimum
Voltage (Vm)
Percentage
selected bus (%)
Bus
No.
Minimum
Voltage (Vm)
Percentage
selected bus (%)
0 0.31 11.1 01 3.9811 09.8
1 0.300 00 01 3.9118 11.1
0 3.9818 09.1 08 3.9111 18.1
1 3.9131 11.1 09 3.910 19.1
1 3.91 11.1 13 3.9180 11.8
1 3.9131 11.1 10 3.9100 11.1
1 3.9110 18.1 11 3.911 11.1
8 3.98 10.1 10 3.9111 19.0
9 0.30 00.0 11 3.9110 11.0
03 3.981 09.1 11 3.9138 11.1
00 0.381 13.1 11 3.9391 11.1
01 0.3311 00.0 11 3.9111 18.1
00 0.310 03.1 18 3.9111 11.1
01 3.9191 10.1 19 3.9091 18.8
01 3.9101 11 03 3.899 11.1
Based on the newton-raphson load flow results, it is shown that the minimum
voltage of the system has dropped below the stable condition which is 3.899 p.u.
Fuzzy logic system operated by using the data from newton-raphson load flow results
to determine the selected bus for load shedding.
39
Figure 011: FIS for Selected Bus for Load Shedding at Bus (14) with loading factor =
1.6
From the result above, it shows that bus 03 is the weakest in the system.
Therefore bus 03 is selected as the appropriate bus to perform the load shedding.
Figure 01 shows the fuzzy based load shedding system to determine the amount load
to be shed at bus 03.
At bus 03 loading factor = 0.1, the value of active power and reactive power
are 01.91MW and 0.31MVAR respectively.
Figure 08: FIS for percentage amount to be shed at Bus (14) with loading factor = 1.6
41
Based on the Figure 01, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 116. As the results, the amount of load to be
shed is 01.11MW and 1.18MVAR. The minimum voltage increased in the system
increase from 3.899 p.u. to 3.9080 p.u. which occur at bus 11. It shows that the
system is still in unstable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor= 0.1, the value of
active power and reactive power are 1.1MW and 0.18MVAR respectively.
Figure 09: FIS for percentage amount to be shed at Bus (16) with loading factor = 1.0
Based on the Figure 01, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 10.16. As the results, the amount of load to
be shed is 0.111MW and 1.0018MVAR. The minimum voltage in the system
increase from 3.9080 to 3.9111p.u.. which occur at bus 11. It is shown that the
system is improved to a stable condition.
As the result, the total amount load to be shed in loading factor = 0.1 is
01.111MW and 1.1018MVAR. At loading factor = 0.1, the program need to perform
1 stages of load shedding to get back the system to a stable condition.
41
1.0.1. loading factor = 0.1
Determination of selected bus for load shedding using the proposed fuzzy
system is shown in Figure 08. This fuzzy system is performed by referring to the data
from the load flow results as shown in Table 1. Loading factor = 0.1 is selected as the
test case conditions.
Table 1: Fuzzy output for selected bus of load shedding at loading factor 1.9
Bus
No.
Minimum
Voltage (Vm)
Percentage
selected bus (%)
Bus
No.
Minimum
Voltage (Vm)
Percentage
selected bus (%)
0 0.31 11.1 01 3.9138 11.0
1 0.300 00 01 3.911 11.8
0 3.9118 11.0 08 3.9090 11.9
1 3.9110 11.1 09 3.9018 11.0
1 3.91 11.1 13 3.9111 11
1 3.9101 11.8 10 3.9181 13.1
1 3.9198 13.0 11 3.9191 13.1
8 3.91 11.1 10 3.9010 10.8
9 0.3300 00.0 11 3.9118 11.0
03 3.9110 10.9 11 3.9101 11.1
00 0.381 13.1 11 3.8891 11.9
01 3.9911 01.1 11 3.9091 11.9
00 0.300 00.1 18 3.911 18.1
01 3.9118 11 19 3.9330 11.1
01 3.9110 18.0 03 3.8118 11.1
Based on the newton-raphson load flow results, it is shown that the minimum
voltage of the system has dropped below the stable condition which is 3.8118 p.u.
Fuzzy logic system operated by using the data from newton-raphson load flow results
to determine the selected bus for load shedding.
42
Figure 138: FIS for Selected Bus for Load Shedding at Bus (14) with loading factor = 1.9
From the result above, it shows that bus 03 is the weakest in the system.
Therefore bus 03 is selected as the appropriate bus to perform the load shedding.
Figure 01 shows the fuzzy based load shedding system to determine the amount load
to be shed at bus 03.
At bus 03 loading factor = 0.1, the value of active power and reactive power
are 08.31MW and 0.10MVAR respectively.
Figure 10: FIS for percentage amount to be shed at Bus (14) with loading factor = 1.9
43
Based on the Figure 09, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 11.06. As the results, the amount of load to
be shed is 00.100MW and 1.1111MVAR. The minimum voltage increased in the
system increase from 3.8118 p.u. to 3.9080 p.u. which occur at bus 11. It shows that
the system is still in unstable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor= 0.1, the value of
active power and reactive power are 1.91MW and 0.90MVAR respectively.
Figure 11: FIS for percentage amount to be shed at Bus (16) with loading factor = 1.9
Based on the Figure 13, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 116. As the results, the amount of load to be
shed is 1.1111MW and 1.9011MVAR. The minimum voltage in the system increase
from 3.9080 to 3.9191p.u.. which occur at bus 09. It is shown that the system is still
not improved to the stable condition.
Therefore the program decides to shed load at bus 09 as the next stages to
make the system in stable condition. At bus 09 with loading factor= 0.1, the value of
active power and reactive power are 01.01MW and 1.18MVAR respectively.
44
Figure 10: FIS for percentage amount to be shed at Bus (1.) with loading factor = 1.9
Based on the Figure 10, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 11.16. As the results, the amount of load to
be shed is 9.0311MW and 0.0190MVAR. The minimum voltage in the system
increase from 3.9191 to 3.9191p.u.. which occur at bus 11. It is shown that the
system is improved to a stable condition.
As the result, the total amount load to be shed in loading factor = 0.1 is
11.1919MW and 8.1811MVAR. At loading factor = 0.1, the program need to
perform 0 stages of load shedding to get back the system to a stable condition.
1.0.1. loading factor = 0.8
Determination of selected bus for load shedding using the proposed fuzzy
system is shown in Figure 11. This fuzzy system is performed by referring to the data
from the load flow results as shown in Table 1. Loading factor = 0.8 is selected as the
test case conditions.
Table 1: Fuzzy output for selected bus of load shedding at loading factor 1.8
Bus
No.
Minimum
Voltage
Percentage
selected bus
Bus
No.
Minimum
Voltage
Percentage
selected bus
45
(Vm) (%) (Vm) (%)
0 0.31 11.1 01 3.9189 11.1
1 0.300 00 01 3.9130 19.9
0 3.9100 10.1 08 3.911 11
1 3.9110 18.0 09 3.9131 18.1
1 3.91 11.0 13 3.9190 11.1
1 3.9110 18 10 3.9010 10.8
1 3.911 11.0 11 3.9019 10.1
8 3.91 11.1 10 3.9101 18.0
9 3.9911 01.9 11 3.9030 11.0
03 3.9101 11.9 11 3.90 11.1
00 0.381 13.1 11 3.8101 11.9
01 3.9801 13.9 11 3.9111 11.1
00 0.310 01.1 18 3.9100 19.1
01 3.9108 19.1 19 3.8811 11.0
01 3.910 10.9 03 3.8101 19.1
Based on the newton-raphson load flow results, it is shown that the minimum
voltage of the system has dropped below the stable condition which is 3.8101 p.u.
Fuzzy logic system operated by using the data from newton-raphson load flow results
to determine the selected bus for load shedding.
46
Figure 11: FIS for Selected Bus for Load Shedding at Bus (14) with loading factor = 1.8
From the Table 0, it shows that bus 03 is the weakest in the system. Therefore
bus 03 is selected as the appropriate bus to perform the load shedding. Figure 10
shows the fuzzy based load shedding system to determine the amount load to be shed
at bus 03.
At bus 03 loading factor = 0.8, the value of active power and reactive power
are 09.38MW and 0.11MVAR respectively.
Figure 10: FIS for percentage amount to be shed at Bus (14) with loading factor = 1.8
Based on the Figure 10, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 11.16. As the results, the amount of load to
be shed is 01.1119MW and 1.1110MVAR. The minimum voltage increased in the
system increase from 3.8101 p.u. to 3.9311 p.u. which occur at bus 11. It shows that
the system is still in unstable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor= 0.8, the value of
active power and reactive power are 1.0MW and 1.01MVAR respectively.
47
Figure 11: FIS for percentage amount to be shed at Bus (16) with loading factor = 1.8
Based on the Figure 11, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 116. As the results, the amount of load to be
shed is 1.111MW and 0.031MVAR. The minimum voltage in the system increase
from 3.9311 to 3.9010p.u.. which occur at bus 09. It is shown that the system is still
not improved to the stable condition.
Therefore the program decides to shed load at bus 09 as the next stages to
make the system in stable condition. At bus 09 with loading factor= 0.8, the value of
active power and reactive power are 01.0MW and 1.01MVAR respectively.
48
Figure 11: FIS for percentage amount to be shed at Bus (1.) with loading factor = 1.8
Based on the Figure 11, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 116. As the results, the amount of load to be
shed is 03.911MW and 0.9018MVAR. The minimum voltage in the system increase
from 3.9010 to 3.9181p.u.. which occur at bus 11. It is shown that the system is still
not improved to a stable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor= 0.8, the value of
active power and reactive power are 01.11MW and 01.31MVAR respectively.
49
Figure 11: FIS for percentage amount to be shed at Bus (14) with loading factor = 1.8
Based on the Figure 11, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 10.16. As the results, the amount of load to
be shed is 9.1111MW and 1.110MVAR. The minimum voltage in the system
increase from 3.9181 to 3.9119p.u.. which occur at bus 1. It is shown that the system
is still not improved to a stable condition.
As the result, the total amount load to be shed in loading factor = 0.8 is
13.3990MW and 01.0399MVAR. At loading factor = 0.8, the program need to
perform 1 stages of load shedding to get back the system to a stable condition.
1.0.1. loading factor = 0.9
Determination of selected bus for load shedding using the proposed fuzzy
system is shown in Figure 11. This fuzzy system is performed by referring to the data
from the load flow results as shown in Table 1. Loading factor = 0.8 is selected as the
test case conditions.
51
Table 1: Fuzzy output for selected bus of load shedding at loading factor 1..
Bus
No.
Minimum
Voltage (Vm)
Percentage
selected bus (%)
Bus
No.
Minimum
Voltage (Vm)
Percentage
selected bus (%)
0 0.31 11.1 01 3.9111 13.1
1 0.330 00.0 01 3.9010 10.1
0 3.9108 11.0 08 3.9031 11
1 3.9111 10 09 3.9311 11.0
1 3.91 11.0 13 3.9011 13.1
1 3.9111 13.9 10 3.9101 18.1
1 3.9011 11.1 11 3.911 18
8 3.91 11.0 10 3.9311 10.1
9 3.9810 09.1 11 3.890 11.8
03 3.9181 13.0 11 3.8901 11.8
00 0.381 13.1 11 3.811 13.1
01 3.9111 10.1 11 3.9399 11.0
00 0.310 01.1 18 3.9091 11.8
01 3.9131 11.1 19 3.8109 18.9
01 3.9031 11.1 03 3.8011 10.1
Based on the newton-raphson load flow results, it is shown that the minimum
voltage of the system has dropped below the stable condition which is 3.8011 p.u.
Fuzzy logic system operated by using the data from newton-raphson load flow results
to determine the selected bus for load shedding.
51
Figure 11: FIS for Selected Bus for Load Shedding at Bus (14) with loading factor = 1.8
From the Table 1, it shows that bus 03 is the weakest in the system. Therefore
bus 03 is selected as the appropriate bus to perform the load shedding. Figure 18
shows the fuzzy based load shedding system to determine the amount load to be shed
at bus 03.
At bus 03 loading factor = 0.9, the value of active power and reactive power
are 13.01MW and 0.10MVAR respectively.
Figure 18: FIS for percentage amount to be shed at Bus (14) with loading factor = 1..
52
Based on the Figure 18, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 83.16. As the results, the amount of load to
be shed is 01.0911MW and 1.9311MVAR. The minimum voltage increased in the
system increase from 3.8011 p.u. to 3.8811 p.u. which occur at bus 11. It shows that
the system is still in unstable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor = 0.9, the value of
active power and reactive power are 1.11MW and 1.01MVAR respectively.
Figure 11: FIS for percentage amount to be shed at Bus (16) with loading factor = 1..
Based on the Figure 19, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 116. As the results, the amount of load to be
shed is 1.9811MW and 0.1111MVAR. The minimum voltage in the system increase
from 3.8811 to 3.9181p.u.. which occur at bus 09. It is shown that the system is still
not improved to the stable condition.
Therefore the program decides to shed load at bus 09 as the next stages to
make the system in stable condition. At bus 09 with loading factor= 0.9, the value of
active power and reactive power are 08.31MW and 1.11MVAR respectively.
53
Figure 03: FIS for percentage amount to be shed at Bus (1.) with loading factor = 1..
Based on the Figure 03, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 116. As the results, the amount of load to be
shed is 00.1011MW and 1.811MVAR. The minimum voltage in the system increase
from 3.9081 to 3.9011p.u.. which occur at bus 11. It is shown that the system is still
not improved to a stable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor= 0.9, the value of
active power and reactive power are 01.10MW and 01.10MVAR respectively.
54
Figure 00: FIS for percentage amount to be shed at Bus (14) with loading factor = 1..
Based on the Figure 00, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 11.16. As the results, the amount of load to
be shed is 03.8101MW and 8.0139MVAR. The minimum voltage in the system
increase from 3.9011 to 3.9110p.u.. It is shown that the system is improved to a
stable condition.
As the result, the total amount load to be shed in loading factor = 0.9 is
11.1100MW and 09.0118MVAR. At loading factor = 0.9, the program need to
perform 1 stages of load shedding to get back the system to a stable condition.
1.0.1. loading factor = 1
Determination of selected bus for load shedding using the proposed fuzzy
system is shown in Figure 01. This fuzzy system is performed by referring to the data
from the load flow results as shown in Table 8. Loading factor = 1.3 is selected as the
test case conditions.
55
Table 8: Fuzzy output for selected bus of load shedding at loading factor 1..
Bus
No.
Minimum
Voltage
(Vm)
Percentage
selected bus
(%)
Bus
No.
Minimum
Voltage
(Vm)
Percentage
selected bus
(%)
0 0.31 11.1 01 3.9091 11.8
1 0.330 00.0 01 3.9180 11
0 3.91 11.0 08 3.9331 11.1
1 3.9111 11 09 3.8911 11.1
1 3.91 11.0 13 3.9310 11.1
1 3.9101 10.1 10 3.9001 10.8
1 3.9001 11 11 3.901 10.1
8 3.91 11.0 10 3.8910 11.1
9 3.9199 10.1 11 3.880 11.0
03 3.9131 11.1 11 3.8191 11.1
00 0.381 13.1 11 3.8011 10.1
01 3.911 11.1 11 3.8991 11.1
00 0.310 01.1 18 3.9019 10.8
01 3.9011 11.8 19 3.8198 13.9
01 3.9108 18 03 3.8100 18.0
Based on the newton-raphson load flow results, it is shown that the minimum
voltage of the system has dropped below the stable condition which is 3.8100 p.u.
Fuzzy logic system operated by using the data from newton-raphson load flow results
to determine the selected bus for load shedding.
56
Figure 01: FIS for Selected Bus for Load Shedding at Bus (14) with loading factor = 1.4
From the Table 1, it shows that bus 03 is the weakest in the system. Therefore
bus 03 is selected as the appropriate bus to perform the load shedding. Figure 00
shows the fuzzy based load shedding system to determine the amount load to be shed
at bus 03.
At bus 03 loading factor = 1.3, the value of active power and reactive power
are 10.1MW and 0.8MVAR respectively.
Figure 00: FIS for percentage amount to be shed at Bus (14) with loading factor = 1.4
57
Based on the Figure 00, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 80.16. As the results, the amount of load to
be shed is 01.1111MW and 0.0831MVAR. The minimum voltage increased in the
system increase from 3.8100 p.u. to 3.8199 p.u. which occur at bus 11. It shows that
the system is still in unstable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor = 1.3, the value of
active power and reactive power are 1MW and 1.1MVAR respectively.
Figure 01: FIS for percentage amount to be shed at Bus (16) with loading factor = 1.4
Based on the Figure 19, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 11.06. As the results, the amount of load to
be shed is 1.011MW and 0.1331MVAR. The minimum voltage in the system
increase from 3.8199 to 3.9391p.u.. which occur at bus 09. It is shown that the
system is still not improved to the stable condition.
Therefore the program decides to shed load at bus 09 as the next stages to
make the system in stable condition. At bus 09 with loading factor= 1.3, the value of
active power and reactive power are 09MW and 1.8MVAR respectively.
58
Figure 01: FIS for percentage amount to be shed at Bus (1.) with loading factor = 1.4
Based on the Figure 01, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 116. As the results, the amount of load to be
shed is 00.1011MW and 1.811MVAR. The minimum voltage in the system increase
from 3.9391 to 3.90111p.u.. which occur at bus 11. It is shown that the system is still
not improved to a stable condition.
Therefore the program decides to shed load at bus 11 as the next stages to
make the system in stable condition. At bus 11 with loading factor = 1.3, the value of
active power and reactive power are 01.1MW and 00.1MVAR respectively.
59
Figure 01: FIS for percentage amount to be shed at Bus (14) with loading factor = 1.4
Based on the Figure 01, it is shown that fuzzy based load shedding system
decided to shed the amount of load up to 10.06. As the results, the amount of load to
be shed is 01.0101MW and 9.1111MVAR. The minimum voltage in the system
increase from 3.9111 to 3.9111 p.u.. It is shown that the system is improved to a
stable condition.
As the result, the total amount load to be shed in loading factor = 0.9 is
19.1918MW and 10.0381MVAR. At loading factor = 1.3, the program need to
perform 1 stages of load shedding to get back the system to a stable condition.
61
CHAPTER 1.3
CONCLUSIONS
In this paper, particle swarm optimization (PSO) technique was use to solve the unit
commitment problem with several constraint as stated before. The result shows that
the proposed method was capable of obtaining optimum operating cost for UC
problem for 11 hour period interval of load demand.. In addition, the wind turbine
generator was attached to improve an operating cost. The wind generator was
successfully shown the effectiveness in minimizing the operating cost. Thus, the
purpose of unit commitment to meet a demand with minimum cost has been achieved.
For recommendation, PSO can be combined with another algorithm such as
Evolutional Programming (EP), Ann Colony and Bee Colony to improve a
performance of the technique to solve unit commitment problem. Moreover, the others
green energy such as solar and nuclear can be implement to study their effect toward
the UC problem.
61
CHAPTER 1.3
RECOMMENDATIONS FOR FUTURE WORKS
There are several addition and development that can be done on DED problems in
order to have high quality and accurate solutions. The improvement that can be done
is such as taking into account other generator constraints such as spinning reserve
requirement and emission constraint. All the constraints will give more accurate result
to the solution of DED problems. In addition, accurate modeling of DED problem will
be improved when the valve point loadings effects in the generating units are taken
into account. Valve point effect are are usually modelled in two form which is i)
consider the prohibited zones as the inequality constraint and ii) implement the effect
as the non-smooth cost function for the fuel cost function[03]
62
REFERENCE
[0] Atputharajah, Arulampalam, and Tapan K. Saha. "Power system blackouts-
Literature review." Industrial and Information Systems (ICIIS), 1339
International Conference on. IEEE, 1339.
[1] Taylor, Carson W. "Concepts of undervoltage load shedding for voltage
stability." Power Delivery, IEEE Transactions on 1.1 (0991): 183-188.
[0] Calderaro, V., Galdi, V., Lattarulo, V., & Siano, P. (1303). A new algorithm
for steady state load-shedding strategy. Optimization of Electrical and
Electronic Equipment (OPTIM), 1303 01th International Conference on, 18-
10.
[1] P. Kundur, Power System Stability and Control, vol. IV. New York: McGraw
Hill, 0991, pp. 919- 0311
[1] Kessel, P., and H. Glavitsch. "Estimating the voltage stability of a power
system." Power Delivery, IEEE Transactions on 0.0 (0981): 011-011.
[1] Saffarian, Alireza, and Majid Sanaye-Pasand. "Enhancement of power system
stability using adaptive combinational load shedding methods." Power Systems,
IEEE Transactions on 11.0 (1300): 0303-0313.
[1] Wang, Y., Pordanjani, I. R., Li, W., Xu, W., & Vaahedi, E. (1300). Strategy to
minimise the load shedding amount for voltage collapse
prevention. Generation, Transmission & Distribution, IET, 1(0), 031-000.
[8] Kadam, D. P., et al. "Fuzzy Logic Algorithm for Unit Commitment
Problem."Control, Automation, Communication and Energy Conservation,
1339. INCACEC 1339. 1339 International Conference on. IEEE, 1339.
[9] Abdelaziz, A. Y., et al. "Fuzzy based load shedding approach against voltage
instability." International Journal of Engineering, Science and Technology 1.0
(1300): 01-11.
[03] Terzija, Vladimir V. "Adaptive underfrequency load shedding based on the
magnitude of the disturbance estimation." Power Systems, IEEE Transactions
on 10.0 (1331): 0113-0111.
[00] C. W. Taylor, Power System Voltage Stability, McGraw-Hill, 0991.
[01] A. Wiszniewski, “New criteria of voltage stability margin for the purpose of load
shedding,” IEEE trans.Power del., vol. 11, no. 0, July 1331, pp. 0011-0010. [00] A. Guzmán, D. Tziouvaras, E. O. Schweitzer and Ken E. Martin, “Local and wide-
area network protectionsystems improve power system reliability,” Schweitzer
Engineering Laboratories technical papers, 1331.
[01] R. Balanathan, N. Pahalawaththa, and U. Annakkage, “A strategy for undervoltage
load shedding in power systems,” International Conference on Power System
Technology, vol. 1, pp. 0191–0198, Aug. 0998.
[01] C. Moors, D. Lefebvre, and T. V. Custem, “Design of load shedding schemes against
voltage instability,” ser. 10-11, vol. 1, Power Engineering Society Winter Meeting,
1333. IEEE, Jan 1333, pp. 0191–0133.
[01] Verayiah, R., Ramasamy, A., Abidin, H. Z., & Musirin, I. (1339, December). Under
Voltage Load Shedding (UVLS) study for 111 test bus system. In Energy and
Environment, 1339. ICEE 1339. 0rd International Conference on (pp. 98-030). IEEE.
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[01] M. Begovic, D. Fulton, M. R. Gonzalez, J. Goossens, E. A. Guro, R. W. Haas, C. F.
Henville, G. Manchur, G. L. Michel, R. C. Pastore, J. Postforoosh, G. L. Schmitt, J. B.
Williams, K. Zimmerman, and A. A. Burzese, "Summary of "System Protection and
Voltage Stability"," IEEE Transactions on Power Delivery, vol. 03, pp. 100-108,
0991.
[08] Mozina, Charles. "Undervoltage load shedding." Power Systems Conference:
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1331. PSC 1331. IEEE, 1331.
[09] Zadeh LA (0911) Fuzzy sets. Info Control 8(0):008–010
[13] E. Cox. “ Fuzzy fundamentals” (IEEE Spectrum, October 0991, pp. 18-10).
[10] Zadeh LA (0910) Outline of a new approach to the analysis of complex systems and
decision processes, IEEE Trans Syst Man Cyber SMC 0218–11
[11] Grewal, G.S.; Konowalec, J.W.; Hakim, M. “Optimization of a load shedding scheme” ,
Industry Applications Magazine, IEEE, vol 1, pp 11-03, July/August 0998
[10] Afiqah, R. N., Musirin, I., Johari, D., Othman, M. M., Rahman, T. K. A., & Othman, Z.
(1339). Fuzzy logic application in DGA methods to classify fault type in power
transformer. SELECTED TOPICS in POWER SYSTEMS and REMOTE SENSING, 80-88
[11] Momoh J. and Tomsovic K., 0991. Overview and literature survey of fuzzy set theory
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0193.
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Issues 8.1: 110-111.
APPENDICES
MATLAB PROGRAMMING
Main Program
64
Momoh J. and Tomsovic K., 0991. Overview and literature survey of fuzzy set theory in
power systems, IEEE Transactions on Power Systems, Vol. 03, No. 0, pp. 0111-0193. clear, close all clc
pso.psoMethod = 'constriction'; pso.saveResults = 'true';
pso.maxIter = 01 pso.noParticles =01;
M. Begovic, D. Fulton, M. R. Gonzalez, J. Goossens, E. A. Guro, R. W. Haas, C. F. Henville,
G. Manchur, G. L. Michel, R. C. Pastore, J. Postforoosh, G. L. Schmitt, J. B. Williams, K.
Zimmerman, and A. A. Burzese, "Summary of "System Protection and Voltage Stability","
IEEE Transactions on Power Delivery, vol. 11, pp. 631-638, 1995.
Mozina, Charles. "Undervoltage load shedding." Power Systems Conference:
Advanced Metering, Protection, Control, Communication, and Distributed Resources,
2117. PSC 2117. IEEE, 2117.
Grewal, G.S.; Konowalec, J.W.; Hakim, M. “Optimization of a load shedding scheme” , Industry
Applications Magazine, IEEE, vol 1, pp 11-03, July/August 0998
Afiqah, R. N., Musirin, I., Johari, D., Othman, M. M., Rahman, T. K. A., & Othman, Z. (1339). Fuzzy
logic application in DGA methods to classify fault type in power transformer. SELECTED TOPICS in
POWER SYSTEMS and REMOTE SENSING, 80-88.
pso.noVars = 6; pso.c0 = 5012; pso.c5 = 5012;
pso.xMin = 1; pso.xMax = 0;
pso.vMax = 0; pso.vMin = -0;
pso.pMin = [011021051021021021]; pso.pMax = [21105110011002105110051]; pso.consFactor = getConstrictionFactor(pso.c0,pso.c5);
saveStringInit = 'F:\Final Year Project\FYP azuwam\Matlab
Programming\PSO editted azuwam.mat';
saveString = 'F:\Final Year Project\FYP azuwam\Matlab Programming\PSO
editted azuwam.mat';
gBest = PSO(pso, seed, saveStringInit, saveString);
65
FinalResult;
PSO Main Program
function gBest =PSO(pso, seed, saveString0, saveString5)
gBest.fitness = 1; gBest.xVal = zeros(0, pso.noVars);
if strcmp(pso.saveResults,'true') gBest.hist = zeros(pso.maxIter, 5 + pso.noVars); end
for i = 0 : 0 : pso.noParticles
for j = 0 : 0 : pso.noVars
particles(i).velocity(j) = rand; particles(i).xVal(j) = rand; particles(i).bestXVal(j) = particles(i).xVal(j);
end %
particles(i).fitness = fitnessFcn(outputPower(particles(i).xVal,
pso));
particles(i).pBest = particles(i).fitness;
end
gBest.xVal = particles(0).xVal; gBest.fitness = particles(0).fitness; diff = 0111;
for i = 5 : 0 : pso.noParticles
if (abs(gBest.fitness - particles(i).fitness) < diff) gBest.fitness = particles(i).fitness;
gBest.xVal = particles(i).xVal;
diff = abs(gBest.fitness - particles(i).fitness); end
66
if strcmp(pso.saveResults,'true') gBest.hist(0,:) = [1, gBest.fitness, gBest.xVal];
end
end
gBest.iter = 1;
if strcmp(pso.saveResults,'true') save(saveString0);
end
iter = 0; t = cputime; while ((iter ~= pso.maxIter) && (gBest.fitness < pso.objective)) for i = 0 : 0 : pso.noParticles
for j = 0 : 0 : pso.noVars vid = particles(i).velocity(j); pid = particles(i).bestXVal(j); xid = particles(i).xVal(j); pgd = gBest.xVal(j);
if strcmp(pso.psoMethod,'constriction')
vid = pso.consFactor * (vid + pso.c0 * rand * (pid -
xid) + pso.c5 * rand * (pgd - xid)); end
if (vid > pso.vMax)
vid = pso.vMax;
elseif vid < pso.vMin
vid = pso.vMin;
end
xid = xid + vid;
if xid > pso.xMax
xid = pso.xMax; vid = 1;
elseif xid < pso.xMin
xid = pso.xMin; vid = 1;
end
67
particles(i).velocity(j) = vid; particles(i).xVal(j) = xid;
end
end
for i = 0:0:pso.noParticles
particles(i).fitness =
fitnessFcn(outputPower(particles(i).xVal, pso));
if (particles(i).fitness > particles(i).pBest) particles(i).pBest = particles(i).fitness; particles(i).bestXVal = particles(i).xVal;
end
if (particles(i).fitness > gBest.fitness)
gBest.fitness = particles(i).fitness; gBest.xVal = particles(i).xVal; gBest.iter = iter
if strcmp(pso.saveResults,'true')
gBest.hist(iter + 0,:) = [iter, gBest.fitness,
gBest.xVal];
end end end
iter = iter + 0; end
gBest.optTime = cputime - t; if strcmp(pso.saveResults,'true')
save(saveString5);
end
Fitness Ramp Rate Program
function [PD,PL,Fcost] = fitnessRamprate(Pi)
pMin = [011021051021021021]; pMax = [21105110011002105110051]; UR = [51021062021021021];
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DR = [0510210011021021021]; Po = [11100910511002100210001]; Prmin = [05105100110610011051]; Prmax = [25105510562051105110061];
for i = 0:0:6 if Pi(i) < pMin(i) fitness = 1; return; elseif Pi(i) > pMax(i) fitness = 1; return; end end for i = 0:0:6 if Pi(i) < Prmin(i) Pi(i)= Prmin; Po(i)= Pi(i); Prmin(i)= Po(i)-DR(i);
elseif Pi(i) > Prmax(i) Pi(i)= Prmax; Po(i)= Pi(i); Prmax(i)= Po(i)+UR(i);
end end
B = [101109, 101105, 101119, -101110, -101112, -101115;... 101105, 101101, 101112, 101110, -101116, -101110;... 101119, 101112, 101100, 101111, -101101, -101116;... -101110, 101110, 101111, 101151, -101116, -101115;... -101112, -101116, -101101, -101116, 101052, -101115;... -101115, -101110, -101116, -101115, -101115, 101021];
Bo = (001e-10)*[-100215, -100529, 109119, 101220, 105060, -106602];
Boo = 101126;
basemva = 011;
PL = Pi*(B/basemva)*Pi'+Bo*Pi'+Boo*basemva;
P = sum(Pi) - PL; PD = round(sum(P));
loaddemand = 222;
if PD < loaddemand fitness = 1; return;
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end
cost = [51109010119;... 5110010101122;... 5510502010112;... 511000010112;... 55100102010115;... 0210050101192];
alpha = cost(:,0); beta = cost(:,5); gamma = cost(:,0);
for i = 0:0:6 F(i) = alpha(i) + beta(i)*Pi(i) + gamma(i) * (Pi(i)^5); end Fcost = sum(F); Ppbc = sum(Pi) - loaddemand - PL;
fitness = 0 / (Fcost + Ppbc);
Constriction Factor Program
function consFactor = getConstrictionFactor(c0,c5)
theta = c0 + c5;
if theta <= 1 error('Theta must be more than 1.') end
consFactor = 5/abs(5-theta-sqrt(theta^5-1*theta));
Output Power Program
function powerOut = outputPower(xVal, pso)
for i = 0:0:pso.noVars powerOut(i) = (pso.pMax(i) - pso.pMin(i)) * xVal(i) +
pso.pMin(i); end
powerOut
Final Result Program
clear, close all clc
load('F:\Final Year Project\FYP azuwam\Matlab Programming\PSO editted
azuwam.mat');
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psogBest = gBest.xVal; ccc = outputPower(psogBest,pso); psogBest = gBest.hist;
disp(strcat('Optimization time:',num5str(gBest.optTime)));
[PD, PL,Fcost] = fitnessRamprate(ccc); PD PL Fcost