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DEVELOPMENT OF A POWER MANAGEMENT ALGORITHM FOR ELECTRICAL POWER
FACULTY
Ebere Omeje
CHUKWUAGU .M. IFEANYI
PG/M.ENG/12/64249
DEVELOPMENT OF A POWER MANAGEMENT ALGORITHM FOR ELECTRICAL POWER
NETWORK
FACULTY OF ELECTRICAL ENGINEERING
DEPARTMENT OF ELECTRICAL ENGINEERING
Ebere Omeje Digitally Signed by: Conte
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
i
DEVELOPMENT OF A POWER MANAGEMENT ALGORITHM FOR ELECTRICAL POWER
OF ELECTRICAL ENGINEERING
DEPARTMENT OF ELECTRICAL
Digitally Signed by: Content manager’s Name
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
ii
DEVELOPMENT OF A POWER MANAGEMENT ALGORITHM FOR ELECTRICAL POWER NETWORK
BY
CHUKWUAGU .M. IFEANYI
PG/M.ENG/12/64249
A PROJECT REPORT SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING, UNIVERSITY OF NIGERIA, NSUKKA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF ENGINEERING (M.ENG)
IN ELECTRICAL ENGINEERING
DEPARTMENT OF ELECTRICAL ENGINEERING UNIVERSITY OF NIGERIA,
NSUKKA
SUPERVISOR: VEN. PROF. T.C. MADUEME
DECEMBER, 2015
iii
APPROVAL PAGE
Development of a power management Algorithm for electrical power network
BY
CHUKWUAGU M. IFEANYI
PG/M. ENG /12/64249
A PROJECT REPORT SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING, UNIVERSITY OF NIGERIA, NSUKKA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF ENGINEERING (M.ENG)
IN ELECTRICAL ENGINEERING
Chukwuagu M. Ifeanyi ______________ ______________ (Student) Signature Date Engr. Prof. T.C. Madueme ______________ ______________ (Project Supervisor) Signature Date Engr. Prof. E.C. Ejiogu ______________ ______________ (Head of Department) Signature Date Engr. Prof. M.O. Omoigui ______________ ______________ (External Examiner) Signature Date Engr. Prof. S. E. Obe _______________ ______________ (Faculty PG Rep.) Signature Date
iv
CERTIFICATION
This is to certify that this project work titled “DEVELOPMENT OF A
POWER MANAGEMENT ALGORITHM FOR ELECTRICAL POWER
NETWORK” was carried out by CHUKWUAGU, MONDAY IFEANYI,
WITH REG. NO.: PG/M. ENG /12/64249 in the Department of Electrical
Engineering, University of Nigeria, Nsukka and meets the regulations
governing the Award of Degree of Master of Engineering (M.Eng) of the
University of Nigeria, Nsukka.
Engr. Prof. T.C. Madueme ______________ ______________ (Project Supervisor) Signature Date Engr. Prof. E.C. Ejiogu ______________ ______________ (Head of Department) Signature Date Engr. Prof. M.O. Omoigui ______________ ______________ (External Examiner) Signature Date Engr. Prof. S. E. Obe _______________ ______________ (Faculty PG Rep.) Signature Date
v
DEDICATION
This project is dedicated to God Almighty.
vi
ACKNOWLEDGMENT My profound gratitude to God Almighty for his providence in a special
way, I give great thanks to our lecturers in the Department of Electrical
Engineering, University of Nigeria Nsukka and most especially my able an
erudite supervisor, Prof. T.C Madueme, of his expanse wealth of knowledge.
Also I gratefully acknowledge the efforts of my parents Mr. and Mrs. Felix
Chukwuagu and my guardians Mr. and Mrs. F.I Ukoha, relations, friends,
Nwachukwu Nwamaka, Ebubechukwu Onyido, and Chukwudubem Onyido and
all who contributed in one way or the other to see that this project and my study
are realized. I say thank you and may God bless you.
vii
ABSTRACT
This project work is aimed at developing an efficient Algorithm for the
management of Electric Power network using fuzzy logic. The fuzzy logic
model functions as a system operator in making decision for load shedding and
transfer switching. The new technique uses the system data frequency variation,
load variation and voltage variation and the experience of the system operators
to formulate fuzzy rules, which are then simulated using fuzzy logic toolbox in
MATLAB.
The fuzzy controller for the load shedding management of power system, was
modeled and developed. Data collected from the New Haven Electric Power
Distribution Substation was used to formulate the fuzzy logic interference rules.
Simulation results indicates a remarkable improvement in the performance of
the load shedding management at the power plants. Using the fuzzy controller
the delay in load shedding transfer switching was reduced from 600 s to
0.02316 s, representing 99.99% reduction in load shedding transfer switching.
The fuzzy logic controller achieved a power management efficiency of 90.57%.
viii
TABLE OF CONTENTS
Title page - - - - - - - - - i
Approval Page - - - - - - - - - ii
Certification - - - - - - - - - iii
Dedication - - - - - - - - - - iv
Acknowledgment - - - - - - - - - v
Abstract - - - - - - - - - - vi
Table of contents - - - - - - - - - vii
List of Figures - - - - - - - - - x
List of tables - - - - - - - - - xi
List of abbreviation - - - - - - - - xii
CHAPTER ONE: INTRODUCTION
1.1 Background - - - - - - - - - 1
1.2 Statement of the Problem - - - - - - 3
1.3 Objective of the Study - - - - - - - 4
1.4 The Scope of the Study - - - - - - - 4
1.5 Significance of the Study - - - - - - 5
1.6 Organization of the Study - - - - - - 5
CHAPTER TWO: LITERATURE REVIEW
2.1 Load Shedding System - - - - - - - 8
2.2 Power Management by Energy Harvesting System - - - 8
ix
2.3 Interface Circuit Impedance Matching - - - - 9
2.4 Process of Power Network Management - - - - 12
CHAPTER THREE: DESIGN METHODOLOGY
3.1 Design Strategy - - - - - - - - 14
3.2 Fuzzy Logic theory - - - - - - - 14
3.3 Hardware and software approach - - - - - 17
3.4 Fuzzy Logic Based Power Management - - - - 18
3.5 Methods of Data Analysis - - - - - - 22
3.6 Modeling the Power Management fuzzy logic Controller inference rules - - - - - - 22
3.7 Fuzzy Inference Rules - - - - - - - 24
3.8 Objectives of the Network Reinforcement - - - - 26
3.9 Frequency and Load Control - - - - - - 27
3.10 The Line Bias Control - - - - - - - 29
CHAPTER FOUR: IMPLEMENTING THE POWER MANAGEMENT
4.1 Implementing the Power Management - - - - - 33
4.2 Sources of data- - - - - - - - - 35
4.3 Fuzzy logic controller model - - - - - - 36
4.4 Simulating Power Supply Variation and Evaluating the Load Shedding Fuzzy Controller - - - - - 39
x
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION
5.1 Conclusion - - - - - - - - - 50
5.2 Recommendation- - - - - - - - - 51 References - - - - - - - - - 52 Appendix - - - - - - - - - 57
xi
LIST OF FIGURES
Figure 2.1: Flow chart for the fuzzy logic scheme - - - - 9
Figure. 2.2 : Maximum Efficiency of energy transfer to the road - 10
Figure 2.3: Maximum power transfer to the road - - - - 11 Figure 3.1 Flow Chart for the fuzzy logic scheme - - - - 19 Figure 3.2: Hierarchy of objectives for distribution planning - - 27 Figure 4.3: Power profile of ht e145 MW generators used in the
simulation study - - - - - - - - 40
Figure 4.4 Signal profile at the load centre feeders at excitation of the 145 MW - - - - - - - 41
figure 4.5: Showing a programmed step fall for available energy at 0.5 seconds, 0.8 seconds and 1.1 seconds - - - - 41
Figure 4.6: Showing signal from load disconnected at 0.5254.
For 25% fall in main energy supply at 0.5sec - 42 Figure 4.7: Signal for load disconnect at 0.8243 for 50% fell in
main power supply at 0.8 secs - - - - - 43 Figure 4.8: Showing signal for disconnect at about 1.1198sec for
75% power drop at 1.1sec. - - - - - 44
xii
LIST OF TABLES
Table 3.1: Parameters for carrying out load shedding by power
Engineers of the New Haven Nigerian power switchyard - 23 Table 3.2: Summary of Fuzzy Inference Rules - - - - 25
Table 4.1: Reduction in load shedding transfer switching delay by
The fuzzy logic controller - - - - - 45 Table 4.2: Using parameter values for the estimation of the
Controller load shedding energy efficiency - - - 46
xiii
LIST OF ABBREVIATIONS
SCC: System Control Centre
TCN: Transmission Company of Nigeria
SCADA: Supervisory Control and Data Acquisition
FM: Fuzzy model
DSP: Digital Signal Processor
PLC: Programmable logic controller
FIS: Fuzzy logic Interference System
NEDC: Nigeria Electricity Distribution Company
PHCN: Power Holding Company Nigeria
FLOPS: Fuzzy Logic Production System
FL: Fuzzy Logic
MATLAB: Matrix Laboratory
PLL: Phase Lock Loop
EEDC: Enugu Electricity Distribution Company
EMF: Electromagnetic force
DILS: Distributed Interruptible Load Scheduling
LSS: Load Shedding Scheme
1
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Future development and present operation of electric power system along
with other large system must pursue a number of different goals. Above all, the
power system should be economically efficient, if it should provide reliable
energy supply and should not have any detrimental impact on the environment.
In addition to these global goals there is a number of supplementary goals,
objectives and criteria. At the same time, operation and development of the
system network is influenced by a variety of uncertain and random factors. As a
result, the development strategy can be chosen from a large number of possible
alternatives. Obviously, among a set of possible alternatives the developer
attempts to find the best, or in accordance with accepted term, the optimal
alternative. Thus, the complexity of the problem related to power systems
planning is mainly caused by presence of multiple objectives, uncertain
information and large number of variables.
In this research work, effort is devoted to consideration of the methods
for development management of electric power network. However, a lot of
problems arising during the elaboration of methods for strategic planning of
power system objects are common apart from the features of the object (voltage
2
level, size) etc. Therefore, methods and approaches treated in this work could be
applied in any subsystem of electric power system.
The history of this method for network management development comes
along with the history of electric power industry. As the significance of the
electric power for the national economy was increasing, more and more efforts
were put to find the optimal network development strategies. Because of that,
there are methodologies applied in practice, which result in feasible and decent
solutions. However, it is evident, that these methods can be improved. The
development of more and more efficient methods for the management of
electric power network is constantly significant. This can be explained by the
high investments involved in reinforcement and operation of the networks. In
industrialized world nearly half of the investment in power industry is
channeled towards this.
Furthermore, in recent years there is a worldwide wave of considerable
changes in power industries, including the operation of system network
deregulation, open market, alternative and local energy sources, new energy
conservation and communication technologies, these are the major factors,
which on one hand increase the uncertainty level and on the other hand provide
the alternative solution to the power management problem. New conditions
persuade the search for new comprehensive method for the management of
3
electric power network objects, including generation, transmission and
distribution networks [1].
Then again, the powerful tools for solution of the tasks of the given type
become available. Computational capacities increase exponentially and the new
mathematical methods and algorithms are developed.
1.2 Statement of the Problem
Though there has been great increase in the power consumption, the total
power generated by the Nigerian Power System still remain low. This calls for a
means of effectively managing available supply.
Electricity supply has always been difficult in quality, quantity,
accessibility and reliability with poor transmission and distribution system, a
sizeable part of generated power is not available to end users. Massive load-
shedding is frequently employ as a way of forcing the demand to be brought
within power holding company of Nigeria ability to supply. Hence, this project
research work will develop a power management algorithm that will be used in
the Nigeria power system.
Finally, this work aims at using fuzzy logic controller to minimize load
shedding transfer switching delay.
4
1.3 Objectives of the Study
The main goal of this work is to show that artificial intelligence
techniques such as use of fuzzy logic are versatile for the control management
of power systems. The specific objectives are therefore to:
i. Model a fuzzy logic controller for the load shedding power management,
using data from the New Haven Nigeria power switch yard for
developing the fuzzy logic controller inference rule.
ii. Carry out simulation study on the operational performance of the power
management fuzzy logic controller.
iii. Carry out evaluation of the performance of the power management fuzzy
controller. The improvement of the load shedding transfer switching and
the efficiency of the power management control system would be
evaluated.
iv. Proffer a strategy for the management of the electric power network using
artificial intelligence that has the ability to improve on the shortcomings
of the conventional methods.
1.4 Scope of the Study
This work covers the development of a more intelligent power
management algorithm based on soft computing technique. It includes the
development of fuzzy inference rules for carrying out of automatic load
shedding. However, it does not include the issue of power control and
5
generation control (i.e. management of generation to match varying load
demands).
1.5 Significance of the Study
This research work seeks to provide alternative and improve solutions to
the challenges inherent in the application of conventional method of managing
electric power network. Uncertainty, random factors and ambiguity of power
system network conditions pose serious challenges and a lot of shortcoming
when we apply conventional methods. This challenge is compounded by the
fact that system is becoming complex with new technologies without adequate
training for the system operators or managers. The study will be useful to utility
operators to improve the reliability and efficiency in power system networks so
as to improve revenue generation and customer satisfaction.
1.6 Organization of the Study
This research work is organized in five chapters. Chapter one presents a
general introduction and background information about power management
development in electric power network, the research objective, limitations and
relevance of the work. A detailed review of related literature is presented in
chapter two. This assigns the power network as an important part of the electric
power system-one of the most complicated systems created by mankind. The
chapter states the main planning objectives to include minimizing power losses,
capital investment and maintenance cost and energy not supplied due to
6
interruptions in the network. It is declared that the complexity of the stated task
of system management is caused by multiple objectives, large number of
variables, uncertainty of initial information and dynamic nature of the problem.
Development of new techniques provides the extended opportunities for
improvement of network operation, but simultaneously complicates the
management process. The design methodology (‘hardware and software’
strategy), sources of data and flow diagram of design implementation are
presented in chapter three. The measurement and data presentation and
simulation, using appropriate software techniques along with the basis for the
design are outlined in chapter four. Results and data analysis are also presented
in chapter four. Lastly, chapter five concludes the work in the form of
conclusion and recommendation.
7
CHAPTER TWO
2.0 LITERATURE REVIEW
A power management system is a system of devices interacting through
special software for the measurement, monitoring and control of desired aspects
of an electrical network in such as way as to ensure optimal operation of the
components of the network [2]. There are many techniques that have been
developed [3–9] to minimize the load curtailment without violating the system
security constraints. The emergency state in the power System with distributed
generations has been formulated and the load shedding is solved as an
optimization problem [10]. Ying Lu et al. [11] has proposed a load shedding
scheme that works with various load models, such as single-motor model, two-
motor model, and composite model. Armanda et al. [12] have adopted a
Distributed Interruptible Load Shedding (DILS) program according to
programmed plans of emergency. Andrzej Wiszn- iewski [13] formulated a new
method for estimating the voltage stability margin, which utilizes local
measurements and applied criterion which is based on the definition of the
voltage stability. Zhiping Ding et al. [14] developed an expert-system-based
load shedding scheme (LSS) for ship power systems. Thalassinakis et al. [15]
built computational methodology that can be used for calculating the strategy
for load shedding protection in autonomous power systems. But the method
suffers large dimensionality coupled with the fact that it takes time to train the
8
ANNs according to Onah et al [16]. In this work a fast method for management
of Electric Power system is developed using fuzzy logic rules.
2.1 Load Shedding System.
This is what to layman (customer) calls “rationing of light.”This is carried
out when the energy generated by the Authority is very much less than the
expected target. Load shedding is required when the electrical load demand
exceeds the capacity of available power sources subsequent to the loss of power
sources or network disintegration. The load-shedding system has to ensure the
availability of the electrical power to all essential and, most importantly, critical
loads in the plant. This is achieved by switching off the non-essential loads in
case of a lack of power in the electrical network or parts of the electrical
network. Loads shedding is an interim measure taken to save the life of a
transformer due to overload resulting from the increase in the energy
consumption of a particular area [17].
2.2 Power Management by Energy Harvesting System
In most low-power systems, power management is generally thought of as being
an ability to switch certain parts of a system off or put them in a low power state
when they are not required, and to manage the charging of a battery. Whilst
these are important aspects of low power electronics powered by energy
harvesters, there are much more fundament reasons for requiring power
9
electronic in an energy harvesting system than simply managing a battery and
conserving energy [18].
(1) In order to achieve high power density from the energy harvester, there
should be some form of impedance match between the energy source and
transducer and the electrical system. This requires control of the input
impedance of the circuit which interfaces to the transducer.
(2) The output voltage and current from the energy harvester are rarely
directly compatible with load electronics and thus some form of voltage
regulation is required.
2.3 Interface Circuit Impedance Matching
In a large scale electrical energy generation plan such as a coal fired
power station, where large amounts of power are produced and where fuel must
be purchased, it is important that as much of the energy contained in the original
fuel source as possible is converted into useful electrical power. This firstly
requires a high efficiency of conversion of the energy stored in the fuel to a
mechanical form, secondly a high conversion efficiency of that mechanical
TRANSDUCER INTERFACE
CIRCUIT
ENERGY
STORAGE
OUTPUT
VOLTAGE
REGULATION
LOAD
ELECTRONICS
Energy harvested
from light/
vibration/heat
Power processing stages
Fig 2.1 Power Electronics Topology for Energy Harvesting Systems
10
energy to electrical energy and finally a high efficiency of power transfer from
the electrical generator to a load. In order to ensure that the energy produced in
the electrical generator is efficiently transferred to the load, there is a well
known and fundamental requirement that the impedance of the load should be
significantly larger than the impedance of the generator. However, whilst this
arrangement (Figure 2.2) achieves maximum electrical efficiency (and prevents
the generator from thermal destruction), it does not achieve maximum power
transfer from source to load. Maximum power transfer occurs in the case where
the load impedance is equal to the source impedance, as illustrated in Figure
2.3. In the case of an AC energy source, the load should provide a conjugate
match to the source. If the diagrams of Figure 2.3 and 2.4 were taken as a very
basic representation of a conventional electromagnetic electrical generator
supplying a load resistance, Rsource would represent the generator winding
resistance and Vsource the EMF produced by time varying flux linkage with those
windings.
Figure 2.2: Maximum efficiency of energy transfer to load
11
Figure 2.3: Maximum power transfer to load
In the case of energy harvesting systems, the fuel supply is effectively
free and leads to desire to be able to transfer maximum power into the load,
rather to accomplish this at high efficiency. In addition, if the power generated
is low, an impedance match has no any thermal implication on the system.
In an energy harvesting generator, the definition of the impedance of the
source to which the load should be matched is not generally as trivial as
matching the load to a single electrical impedance. The source impedance will
be dependent upon the type of energy harvester used and the conditions under
which the harvester is operating. In some circumstances, for example harvester
operating mode, it may not be optimal to match the impedance of the load to
that of the source due to constraints, however for energy harvesters studied in
this chapter, there is always a clearly defined transducer load impedance which
results in maximum power extraction from the transducer. It may therefore be
more accurate to specify that the input impedance of the interface circuit to the
12
transducer must be controllable, rather than always matched to the source,
although in many case the input impedance of the interface circuit will be set to
match that of the source [19].
2.4 Process of Power Network Management
A Starting point of reinforcement planning and power network
management is the existing network under the influence of external factors.
Once it has been identified that network performance during the planning period
is in anyway inadequate, it is time to start the management process [20].
Inadequacy of performance may be induced by internal or external changes such
as increasing or decreasing existing loads or appearances of new loads;
appearance of new local generation source and obsolescence of equipment.
Furthermore, new requirement to network performance criteria, such as
improved reliability, decreased operation and maintenance cost and decreased
losses may also require additional reinforcements. Information about
inadequacy of the performance can be obtained from several sources, mainly
from monitoring calculations, but from customer complaints, direct
measurements and observations by the utility staff [21].
Management actions may include addition or reduction of network loads.
Each problem may have several possible solutions. For example, monitoring
calculations indicate that in five years voltage level will be too low in some
parts of the network. Possible management actions may include for example,
13
building of new lines, selecting between overhead line and cable, providing
alternative network configurations, installation of capacitors or reactors, change
of transformers, enlargement of conductor cross-section or transition to the
alternative voltage level. More so, appearance and development of new
technologies may suggest alternative or additional option, which should also be
considered in the management and planning process.
The planning and management process consists of several steps including
identification of possible alternatives, their evaluation according to selected
performance criteria and selection of the most suitable alternatives, which form
the development strategy.
14
CHAPTER THREE
DESIGN METHODOLOGY
3.1 Design Strategy
Generally, the most common advantage of fuzzy logic system in power
management, in comparison with other conventional methods is that their
designs are carried out by human linguistic knowledge. Fuzzy logic power
management can be assumed as the emulation of a skilled human operator.
There are different methods to design fuzzy logic management [22].
1. Formulate the rule base by an expert interview
2. Model directly the management actions of the operator by means of
numerical data and system performance.
3. Estimate a fuzzy model (FM) of the process and design a fuzzy manager
by simulation studies.
4. Generate the rules by a self-organizing mechanism.
For this research work, an artificial intelligence based method of power
management was developed and used
3.2 Fuzzy Logic Theory
Fuzzy logic was a controversial subject before 1960s, before then; there
have been comments on the issue. According to Plato, there is region of answers
existing between true and false [23]. However, for many years prior to 1960,
15
many scholars at various universities gave many concepts of fuzzy logic; even
though their contributions were fuzzy.
The concept of fuzzy logic (FL) was conceived by Professor Lotfi Zadeh
in 1960 as a way of processing data by allowing partial membership. In
conventional logic, a statement is either true or false, was formulated by
Aristotle some years ago as the law of “the excluded middle” .i.e. two valued
logic rather than crisp set membership or non-membership.
Dr. Zadeh Lofti of university of California and other researchers have since then
given many works, papers and tutorial on the subject [23-25].
Along the various developments on the fuzzy logic, developing countries
especially Asian countries have also modeled many hardware for fuzzy
computations.
In fact the word fuzzy means vagueness or unclearness, fuzzy logic hence
is used to solve problems whose answers and requirements are more than simple
Yes or No, true or False, on or off. Fuzzy logic also takes care of the forbidden
state of digital circuits (0.8V-2V) which is the main stream of information
technology.
Its applications are numerous; namely in chemical process control,
electrically controlled machines, frequency converters manufacturing industries,
video machines, automobiles expert systems and even in power system for
heuristic optimization.
16
It resembles human reasoning in its approximation of information and
uncertainty to generate decisions. It was specially designed to mathematically
generate decisions. It was specifically designed to mathematically represent
uncertainty and vagueness and provide formalizing tools for dealing with the
imprecision to many problems. By contrast, traditional computing, demands
precision down to each bit. People do not require precise numerical information
input, and be capable of highly adaptive control [26]. If feedback controllers
could be programmed to accept noisy, imprecise input, they would be much
more effective and perhaps easier to implement. Since knowledge can be
expressed in a more natural way by using fuzzy sets, many engineering and
decision problems can be greatly simplified.
Fuzzy logic offers a better way of representing reality or grading of
things. In fuzzy logic, a statement is true to various degrees ranging from
completely true- through half-truth to completely false. The idea of multi-valued
logic gives a new approach to the mathematics of thinking; it is a change of
paradigm to Aristotelian logic.
The computer tool used in expert system development is “FLOPS” which
means Fuzzy Logic Production System. FLOPS is based on fuzzy system
theory, fuzzy sets, fuzzy logic and fuzzy numbers [24]. The use of fuzzy
mathematics gives FLOPS the advantage to reason in terms of words such a
small, medium, fast slow and so on, rather than in terms of numbers. Hence
17
ambiguities and contradictions can be easily handled and uncertainties pose no
problems.
Fuzzy set theory implement classes or groupings of data with boundaries
that are not sharply defined (i.e. fuzzy) any theory implementing “crisp”
definitions such as classical set theory, arithmetic, and programming may be
“fuzzified” by generating the concept of a crisp set to a fuzzy graph theory and
fuzzy data analysis, though the term fuzzy logic is often used to describe them.
Truth of a statement is defined, as is correct. Truth is measured
numerically in most fuzzy systems literatures, as ranging from zero (false) to
one (true). A typical fuzzy system consists of a rule-base, membership functions
and inference procedure. Today fuzzy logic is found in a variety of control
applications including chemical process control, manufacturing and in some
consumer products like washing machine, video cameras and automobiles.
3.3 Hardware and Software Approach
A fuzzy logic system consists of mainly three parts namely: a
fuzzification of the input signals, an inferencing mechanism and a defuzzication
process and hardware combination available for implementation. A typical
example is the TMS320C30 Digital Signal processor (DSP) chip from Texas
instruments, with powerful instrument set [27] which can be installed in a
computer to realize the objective of the fuzzy controller. The fuzzy controller
can also be realized with some software packages like, C languages and
18
MATLAB fuzzy logic toolbox etc. In this work, the software implementation
using MATLAB fuzzy logic toolbox is applied for the simulation [28].
3.4 Fuzzy Logic Based power Management:
Below is a series of steps showing the solution procedure followed in
designing our scheme.
Step I: Choose appropriate input conditions: Frequency, voltage and load
in this case
Step II: Determine the fuzzy knowledge base and draw the membership
functions (for our design, MATLAB codes are used).
Step III: Convert the input condition variables to fuzzy sets (fuzzification)
Step IV: Design the fuzzy inference-decision making (Rule base) and
simulate with appropriate program, (like MATLAB).
Step V: Devise an appropriate transformation of fuzzy logic management
action (defuzzifications)
Step VI: Model the power management controller using the MATLAB
SIMULINK fuzzy Logic toolbox.
Step VII: Integrate the fuzzy logic power management controller with the
MATLAB SIMULINK model of the case study power system.
Step VIII: Carry out simulation and evaluate the controller performance.
These steps were diligently applied here as shown in the flowchart of figure 3.1
19
START
Choose input conditions (i.e. input variables): Available – Energy, feeder priority, period-of-supply, Reliability, Number of feeder, voltage, Load
The Gaussian membership function is used to carryout the fuzzification that converts the input conditions to fuzzy numbers (i.e. to fuzzy sets)
Specify the fuzzy Logic Inference rules
Constitute the fuzzy Logic knowledge base
The centroid area method is used for the defuzzification strategy. This is to generate crisp values for determining control signals for transfer switching
A
Model the fuzzy logic power management controller using the MATLAB SIMULINK fuzzy logic toolbox
Integrate the fuzzy logic power management controller with the simulink model of the case study power system as shown in figure 4.2.
Setup and carryout simulations using the SIMULINK 3 – phase programmable voltage source block (figure 4.2) to simulate the variations in the supplied power in order to test the control actions of the fuzzy logic power management controller.
Evaluate the performance of the power management controller
STOP
Figure 3.1 Flowchart for the fuzzy logic scheme
20
The flowchart of figure 3.1 graphically depicts the flow (the steps) for the
design and testing of the proposed fuzzy logic power management controller.
As indicated, the system input variables used for the modeling of the fuzzy
inference system have to be first specified. The variables are chosen partly as
load shedding policy variables at New Haven Nigeria switchyard management,
which was used as a case study. These variables are used for the specification of
the fuzzy inference rules as reported in section 3.6.
Since the input variables are entered as numbers (numeric values),
fuzzification is required to translate these values to fuzzy linguistic variables
(fuzzy sets). To carryout this, the Gaussian membership function is given as
[29]:
�������� �; �, � = �� � − � �
�− − − −�3.1�
From the equation (3.1), it can be seen that the Gaussian membership function
is specified by two parameters {�, � }, where C represents the membership
function’s center and � determines the membership function’s width.
A control decision making structure is required for the power
management control, hence the inference rules for the fuzzy logic controller is
specified as carried out in section 3.6.
The inference rules are organized systematically within a storage data
structure. This systematic organization and storage of the inference rules
21
constitutes the fuzzy logic knowledge base. The MATLAB SIMULINK
toolbox’s rule editor is used to create this knowledge base.
Since the system output variables will be in fuzzy linguistic terms, it is
necessary to convert these fuzzy values to numeric (crisp) values in order to
carryout power management actions (such as sending control signal to effect
transfer switching for load shedding). To achieve this step, a defuzzification
method is used. The defuzzification method used in the design is the centroid
area method given as [29]:
���� = � � ���!"!#
� � ���"!# − − − − − − − �3.2�
Where � ��� is the aggregated output membership function, Z is the fuzzy set
output of the rule processing and ZCOA is the representative crisp value obtained
via the centroid of area defuzzification strategy.
The MATLAB SIMULINK fuzzy logic toolbox is used to model the
power management controller. The toolbox has the facility for specifying the
fuzzy logic membership function, it has the tool for constructing and editing the
inference rules, for the specification of the centroid area defuzzification method.
These actions automatically generate the power management controller program
code.
22
The integration of the controller with the simulink model of the case
study power system and the carrying out of the simulation is discussed in
chapter four.
3.5 Methods of Data Analysis
The data (conditions) analysis is accomplished with computer
simulations, using the fuzzy logic toolbox in MATLAB, a product of Math
works incorporated.
3.6 Modeling the Power Management fuzzy logic controller inference
rules
Table 3.1 shows data obtained from power engineers at New Haven Nigerian.
Power switch is used for formulating the fuzzy inference system. The fuzzy
logic controller linguistic variables are;
1. Available –energy
2. Feeder priority
3. Period-of-supply
4. Reliability
5. Number-of-feeders
The available energy is the total energy available from the supply source as
measured by simulink V-I measurement block. The feed-priority is the priority
value (or weighting) assigned to feeders based on power management policy of
the power system management. Period of supply is power sharing supply
23
duration allotted to different load centers in situation of load shedding.
Reliability is the numerical rating assigned to different load centers by power
management decision to indicate feeders that are likely to waste energy as a
result of non-usage resulting from high likelihood of fault. Number of feeders,
based on power management decision, determines load centers likely or not
likely to be supplied power on rotation basis, as a result of load shedding
decision.
The fuzzy sets are specified as HIGH and LOW.
Based on power management policy currently being implemented at the
case study power system switchyard, the following fuzzy inference rules are
formulated for the power management fuzzy controller.
Table 3.1: Parameters for carrying out load shedding by power engineers of the New
Haven Nigerian power switch yard
Feeders Priority No of Feeders Reliability load
Kingsway II High 6 70% 17.50 MW
Kingsway I Low 5 70% 19.50 MW
Amechi road Low 1 30% because long distance tension
down on (low)
13.6MW
Ituku-Ozalla High 3 80% 15.10MW
Government house High 1 90% 8.0MW
Independence layout High 4 90% 10.6MW
New NNPC Low 2 30% 19.0MW
Thinkers Corner Low 4 90% 19.5MW
Emene Low 1 60% 8 MW
Source: Enugu Electricity Distribution Company [EEDC]
24
3.7 Fuzzy Inference rules
- If available-Energy is LOW and Feeder-priority is HIGH and period-of-
supply is LOW THEN supply is ON.
- If available-Energy is LOW AND Feeder-priority is HIGH AND
PERIOD-OF-Supply is HIGH then Supply is OFF.
- If available-energy is LOW AND Feeder-priority is LOW AND Number-
of-Feeders is HIGH AND Reliability is HIGH AND Period-of-supply is
low THEN supply is ON.
- If available-Energy is LOW AND Feeder-priority is LOW AND Number-
of-Feeders is HIGH AND Reliability is HIGH AND Period of-supply is
LOW THEN Supply is ON.
- If Available-Energy is LOW and Feeder-priority is LOW And Number-
of-feeders is LOW AND Reliability is HIGH AND period-of-supply is
LOW THEN supply is OFF.
- If available-Energy is LOW AND Feeder-priority is LOW and Number-
of-feeders LOW AND Reliability is LOW AND period-of-supply is
LOW THEN Supply is OFF.
- If available-Energy is LOW AND Feeder-priority is LOW AND Number-
of-feeder is HIGH AND Reliability is HIGH AND period-of-supply is
HIGH THEN Supply is OFF.
25
- If Available-Energy is LOW and Feeder-priority is LOW AND Number-
of-feeders HIGH AND Reliability is HIGH and period-of-supply is
HIGH THEN Supply is OFF.
- If available-Energy is LOW AND feeder-priority is LOW AND Number-
of-feeders is LOW AND Reliability is HIGH and period of Supply is
HIGH THEN supply is ON.
- If Available-Energy is LOW AND Feeder-priority is LOW and Number-
of-feeders is LOW AND Reliability is LOW AND period of supply is
HIGH THEN supply is ON.
- If Available-Energy is HIGH Supply is ON
Table 3.2: Summary of Fuzzy Inference Rules
s/n Available
Energy
Feeder
priority
number
of feeders
Reliability
supply
Period
of
supply
Supply
or
output
1 Low High _ _ Low ON
2 Low High _ _ High OFF
3 Low Low High High Low ON
4 Low Low High High Low ON
5 Low Low Low High Low OFF
6 Low Low Low Low Low OFF
7 Low Low High High High OFF
8 Low Low High High High OFF
9 Low Low Low High High ON
10 Low Low Low Low High ON
11 High _ _ _ _ ON
26
3.8 Objectives of the Network Reinforcement
Objectives of the network reinforcement may vary considerably from one
utility to another and from one plan to another within the utility. However, it is
possible to formulate the common objectives for the planning task in general in
terms of planning attributes, which have to be minimized.
The approximate hierarchy of objectives for distribution network
planning is presented in figure 3.2. More or differently formulated objectives
can be added, i.e. voltage quality or environmental impact.
The rectangles in figure 3.2 contain the attributes, which are common to the
distribution planning problems, and are suggested for application in planning
software presented in this dissertation. As a result, there are the following three
general attributes to be minimized.
Attributes 1: Power loses: cost of power losses is calculated for the whole
planning period. Different loading conditions may be
modeled by duration of every mode.
Attribute 2: Investments: Investments and operation and
Maintenance (O & M) costs are combined with the single
attributed.
Attributes 3: Reliability: Either energy not supplied or customer outage
costs are used depending on the information available for the
particular task.
27
The objectives, which are the subject of optimization, are open-ended. No
matter how good the planner is he is always challenged to do better. By
contrast, the operational constraint must only be met, not exceeded [30].
3.9 Frequency and Load Control
Electric power systems use some methods of continually adjusting system
generation to match changing loads to maintain frequency within desired limits.
The speed drop characteristics of generator governors, which results in some
increase in generation with dropping frequency and reduction in generation with
increasing frequency, acts to regulate frequency, but not for the exact limits
required by modern industrial processes, time clocks, etc.
On an isolated system (not interconnected with other systems), flat
frequency control of frequency is practicable. In this method, continuous
measurement is made of frequency, and a signal proportional to the difference
Satisfy growing and changing
system load demand
economically reliably and
safety
Minimize power
losses
Minimize costs Improve reliability
of supply
Satisfy operational
constraints
Minimize
investment cost Minimize O & M
cost
Minimize energy
not supplied
Minimize
customers’ outage
Level 2:
Level 1:
Figure 3.2: Hierarchy of objectives for distribution planning
28
between existing frequency and set (desired) frequency is transmitted to one or
more generating plants to adjust generation in a direction to eliminate the
frequency difference [31].
When a small system is interconnected with a large system or systems,
the smaller system may rely on frequency control by the larger system (s) and
control only the power flow between itself and the larger system(s). In this
method, called flat tie line control, the total set flow on the interconnecting
circuits is continually measured and compared with a set (desired) net flow; a
signal proportional to the difference is transmitted to one or more generating
stations to adjust generation to eliminate the difference. In this method, the
generation is being continually adjusted to match the desired net interconnecting
flow plus the varying load in the local system.
Flat tie line control is effectively used on Hydro’s 25 cycle subsystem in
Nigeria sub-Region. In this case, the 25 cycle frequency is effectively controlled
by the 50 cycle frequency, via the frequency changers; Beck No. 1 25 cycle
generation is frequently on automatic load control from the System Control
Centre to maintain the frequency changer “tie line” flow at a set value. The set
value is based on maximizing frequency changer transfers for economic
utilization of water without impairing 25 cycle system security due to
contingencies such as loss of a frequency changer.
29
Larger power systems interconnected with other systems utilize tie line
bias control; this is the normal control mode used on Hydro’s 50 cycle system.
In this mode, both system frequencies (compared with desired or set frequency)
and net interconnection MW flows (compared with set or desired) are
simultaneously controlled. Tie line bias control means control of tie line net
flow biased by deviation from desired frequency.
3.10 The Line Bias Control
The MW flow in each of the tie lines between Hydro and interconnected
utilities aretelemeter to the SCC by frequency shift telemetering (PLC or
microwaves). Telemeter receiver for each quantity produces a milliamp output
for the associated graphic meter and a milliamp output for the control
computing circuit, the output being proportional to tie line flow. In the
computing circuit each milliamp output is converted to a millivolt quantity and
the millivolt outputs are connected in series to produce a quantity proportional
to net tie line flow. An addition millivolt quantity, proportional to the desired
net interchanged as set into the control, is introduced into the computing circuit,
thereby producing net millivolts proportional to the difference between set and
actual net interchange.
This net millivolt quantity is biased by the introduction into the
computing circuit of millivolts proportional to the difference between actual and
set frequency.
30
A final total millivolt quantity is produced which is proportional to the sum of:
1. The difference between set and actual net interchange
2. The difference between set and actual frequency.
This final quantity, which initiates all corrective control action, is the area
requirement, which is indicated in terms of MW on a centre-zero area
requirement graphic methods at the Control Centre. Mathematically, the area
requirement graphic meter is represented as.
Area Requirement = ∆W + K ∆ f
K, the frequency bias setting in MW per tenth of a cycle set into the control by
adjustment of the frequency bias rheostat. This setting in percent of the system
capacity in MW, is the same for each interconnected system. Usually 1.5
percent of the annual peak load in MW is used in each system. The setting used
results in a MW contribution to area requirement due to abnormal frequency
which approximates the natural frequency-load characteristics of the system.
Examples of the effect on area requirement of four types of disturbances are as
follows [31]:
3. Drop in frequency-(case of External Generation Deficiency): The drop in
frequency results in a minor reduction in load, and an increase in
generation due to governor droop characteristics. The resulting over
generation on the Hydro system (load frequency characteristics) equals
∆W that flows out on the interconnecting circuits to assist the power
31
system in difficulty. With appropriate selection of the value K, the K ∆ f
product equals and cancels ∆W; the Area requirement is zero and no
control action takes places on the Hydro System that is the natural
assistance to the power company in trouble is maintained until it adjusts
its own imbalance.
4. Drop in frequency-(Case of Internal Generation deficiency): In this case,
∆W is the initial generation deficiency less the generation increase due to
governor droop action on low frequency less the load reduction due to
low frequency that is,
∆ W approximates initial generation deficiency ∆W and the area
requirement, initiating control raise impulses to Hydro’s generating
stations on control to correct its own deficiency. This is expressed as:
Initial generation deficiency ∆ W + K ∆ f= initial generation deficiency
5. Increase in frequency-(Case of External generation surplus): In this case,
Hydro’s generation reduces slightly due to governor droop action on high
frequency and Hydro’s load increases slightly due to high frequency. This
results in an inflow of Hydro, ∆ W; ie, Hydro assists in absorbing the
excess generation until the system with the excess takes control action. ∆
W and K ∆ f are approximately equal and self cancelling: the Area
Requirement is zero and Hydro takes no control action.
32
6. Increase in Frequency-(Case of Internal Generation surplus): This case is
similar to (2). ∆W is Hydro’s initial surplus less the frequency load
characteristics of Hydro’s system. The Area requirement, initiating
control lower impulses to Hydro’s generating stations on control to
correct its own generation surplus, is equal to the initial generation
surplus.
33
CHAPTER FOUR
4.1 Implementing the Power Management
The circuit diagram employed in this research work is shown in figure 4.1. The
Onitsha/New Haven 330/132/33kV single line diagram which is implemented in
simulink with fuzzy logic controller integrated is shown in figure 4.1.
34
40
35
4.2 Sources of Data
The work is a case study of 330/132/33kV transmission station, New Haven
Enugu which belongs to the Transmission Company of Nigeria (TCN); so the
TCN is the primary source of data.
1. System Operation Department: This department is responsible for
monitoring the performance and control of the system network. Engineers
with expert knowledge in this department were interviewed to obtain
relevant operational data and information.
2. Communication/SCADA AND SYSTEM LINES DEPARTMENTS
This department is responsible for the communication system and SCADA. The
system lines department is responsible for the protection and maintenance of the
line. Engineers from these departments were interviewed for the performance
evaluation of the system as it affects network power management vis-a- viz the
existing conventional methods of power management and the relevant technical
data/line designs and parameters.
(3) Internet Resources: - Various search engines like www.google, yahoo, ask,
mamma and lec were used to gather valuable information from related
publications, which and herein, well referenced.
36
4.3 Fuzzy Logic Controller Model
The MATLAB simulink fuzzy logic controller tool box is used to model
the load shedding power logic management fuzzy logic controller. The fuzzy
logic membership function editor is used to create fuzzy linguistic variables as
specified in chapter three. The layout of the input/output of the controller as
illustrated within the simulink fuzzy logic FIS editor is shown in figure 4.2.
37
38
The MATLAB simulink model of the test case power system with the
power management fuzzy logic controllers integrated is depicted figure 4.2.
As the source is set to be 145MW part of power management load shedding is
done to prevent the power system from total collapse.
The power management fuzzy controller systematically sheds loads in the
system in order to systematically rotate power among the load center.
For the simulation set up, the load shedding is simulated by automatic fuzzy
logic controlled transfer switchgear operation.
The setup is to trip and reclose circuit breakers for connecting and dropping
loads on the feeders.
The fuzzy controller uses its inference rules as modeled in chapter three
to control the feeders whose loads are dropped (OFF) or connected. The fuzzy
logic control is based on available power, the specified priorities of the number
of feeders, the reliability rating of the feeders and the duration of having power
supplied (period-of-supply).
39
4.4 Simulating Power Supply Variation and Evaluating the Load
Shedding Fuzzy Controller
To be able to simulate variation in a case where a demand would exceed
generation, the simulink 3 phase programmable voltage source block is used.
Referring to figure 4.2, the simulink 3-phase programmable voltage source
block is used as the variable energy source energizing the power system.
This block property in the simulink 3-phase programmable voltage is used to set
the variation timing. The block is set to amplitude variation timing, the variation
type is step and the time variation is set at intervals of 0.3 seconds. At step times
indicated, the 3-phase programmable power source, based on the step function,
varies the MVA (MW) and MVAR of the power source downwards at intervals.
This simulates the drop in available energy. As indicated in figure 4.2, the
generator (variable power source) is connected to the feeder network through
the simulink three-phase V-I measurement block. This block provides the
instrumentation feeds (inputs) required by the discrete 3-phase PLL and the
discrete 3-phase PLL-Driven positive sequence Active of Reactive power block
to dynamically measure.
Evaluation is carried out to estimate average reduction in load shedding transfer
switching delay as compared to the current manual method. Furthermore the
energy distribution is also evaluated.
40
At the set interval, the programmable power source varies its power
downwards by 25% descent i.e following the trend 100%, 75%, 50% and 25%
corresponding to 145MW, 108.75MW, 72.5MW and 36.25MW the active (i.e in
Mega Watts) and reactive power supplied (available) to the network.
As indicated this measurement is fed as input (available-energy) to the
power management fuzzy logic controller. The fuzzy logic controller uses this
value in its inference rule to determine the feeders to drop or connected during
the load shedding operation. As indicated the required fuzzy linguistic
variables: feeder-priority, Number of-Feeders, Period-of-Supply are input into
the fuzzy logic controllers through the simulink cost and blocks.
Based on these variables, the fuzzy logic controllers send trip signals to
the circuit breakers to trip loads (turn off) on the appropriate feeders.
At initialization of the programmable power source at 145MW, the profile of
the active power excitation of the generator is shown in figure 4.3
Figure 4.3: Power profile of the 145 MW generators used in the simulation study Time(s)
41
While the sample signal profiles at the feeders is shown in
Figure 4.4
The step descent of the active power output of the programmable source at set
intervals of 0.3 sec. is shown in figure 4.5
The diagram shows stepwise descent of the available power. This step fall in
available energy causes the fuzzy controller, using the inference rules to
systematically drop loads.
At programmable source transmission at 0.5sec (carries providing to a 25%
drop of main power supply which is a fall from around 145 MW to 108 MW), the
Figure 4.4: Signal profile at the load centre feeders at excitation of the 145 MW
Figure 4.5: Showing a programmed step fall of available energy at 0.5 seconds, 0.8 seconds, and 1.1 seconds.
Time(s)
Time(s)
42
signal and profile of the feeders: Emene and Amechi road and New NNPC as
shown in figure 4.6. The graph shows that these feeders have been switched off
by the fuzzy controller.
Figure 4.6: showing signal from load disconnected at 0.5254 for 25% fall in main energy supply at 0.5sec.
The decision to shed loads on these feeders is based on the fuzzy logic
controller inference rule as specified in chapter three. The other loads on the
other remaining six feeders are still connected.
At source energy change (descent) at 0.8sec (corresponding to a 50% drop of
supplied energy, which is a fall approximately around 145MW to 72.5MW), the
supply at the feeders, Thinkers Corner, New NNPC, Emene and Amechi Road
collapsed at around 0.8243 second as shown in fig 4.7. This represents a transfer
switching delay of 0.243 second as against the 10 minutes delay using the
current manual load shedding procedure.
Time (s)
Ava
ilabl
e en
ergy
(MW
)
43
Figure 4.7: Signal for load disconnect at 0.8243 for 50% fall In Main Power Supply At 0.8sec.
It takes on average of 10 minutes (data as given by PHCN control unit
engineers at the New Haven Nigerian switchyard).
The remaining five other feeders:
Kingsway 11, Kingsway 1, Ituku-Ozalla, Government house and independence
layout remained energized.
At source drop step change at 1.1sec (corresponding to a 75% drop of supply,
which is a fall of approximately around 145MW to 36.25MW), the supply at the
feeders: Kingsway1, Amechi Road, Ituku-Ozalla, New NNPC, Thinkers Corner,
Emene were disconnected by the fuzzy controller at around 1.1198 seconds as
shown in Fig 4.8.This represents a transfer switching delay of 0.1198 seconds.
The 3-phase programmable source step transition time, the load drop time, and
the transfer switching delay are given in table 4.1.
Time (s)
Ava
ilabl
e en
ergy
(MW
)
44
Figure 4.8: Showing signal for disconnect at about 1.1198sec for 75% power drop at 1.1sec.
Time(s)
45
Table 4.1: Reduction in load shedding transfer switching delay by the fuzzy
logic controller
Source supply Step
Transition time (seconds)
Load drop Time (secs) Transfer switching
(delay)time
0.5 0.5254 0.0254
0.8 0.8243 0.0243
1.1 1.1198 0.0198
%&�'��� "�(�) = 0.0254 + 0.0243 + 0.01983
%&�'��� "�(�) = 0.06953
= 0.02316��
Based on the current manually load shedding transfer switching at the New
Haven switch yard, it takes (according to one of the staff) an average of 10
minutes to carry out the operation.
This means reduction in load shedding transfer switching delay from 600sec to
0.02316sec. This represents a 99.99% reduction in transfer switching delay.
46
Table 4.2 using parameter values for the estimation of the controller load
shedding energy efficiency
Supply fall
transition time
(sec)
Connected feeders Demanded power (MW)
Available power supplied (MW) Losses (MW)
0.5 Kingsway II
Kingsway I
ItukuOzalla
Government house
Independence layout
Thinkers Corner
90.2 108.75 18.55
0.8 Kingsway II
Kingsway I
ItukuOzalla
Government house
Independence layout
70.7 72.5 1.8
1.1 Kingsway II
Government house
Independence layout
36.1 36.25 0.15
TOTAL 197 217.5 20.5
Some amount of energy would be wasted as a result of supply over shoot. This
depends of the controllers computation of available energy at any time as
against demanded energy on the connected feeders (i.e. connect load centers,
that is the load centers that were not disconnected by the fuzzy controller).
Hence evaluating the power management efficiency is in order.
47
Efficiency = 1234 5678671234 9:867 × 100%
= =:>3?@ AB>C
=:>3?@ D688E9>C × 100%
In this power management control case,
Efficiency = =:>3?@ 6B>C F@ G2::>G7>C H>>C>3
=:>3?@ B688E9>C × 100%
Referring to table 4.2
Efficiency = =:>3?@ C>IJ:C>C F@ K2::>G7>C H>>C>3B L MM%
=:>3?@ B688E9>C
197217.5 × 100%
= 90.57%
The power management fuzzy controllers achieved a load shedding efficiency
of 90.57%.
48
For an estimate of the efficiency of the manual load shedding, the data log extract in appendix A (obtained from PHCN transmission) is used.
Using the tabulation on appendix A and table 3.1 for feeder load capacities:
Load demand for time interval 1800 – 2200: 64.5MW
load demand of connected feeder on time interval 1300 − 1200: 45MW
Load demand of conencted feeders on time interval 0900 − 1200: 36.1MW
Load demand of connect feeders on time interval 0600 − 0800: 25.5MW
Total demand within the time intervals being considered: 171.1MW
Total Power supplied during the time interval: 370MW
Efkiciency = 171.1370 × 100% = 46.24%
As can be seen, the manual load shedding method is not as efficient as the
fuzzy controlled method. Apart from the 600 seconds average delay, and
probably as a safety precaution more feeders are disconnected during low power
supply. This is mainly as a result of not being able to quickly (in real time) and
accurately compute and determine the balance of supplied power with the load
demand configuration and the priority of the feeders.
Consequently (since there are no automated load shedding decision
making tool) this results in the practice of first considering the priority of
49
feeders (based on the specified importance of the load centers these feeders are
supplying), also with overload safety measures before disconnecting feeders.
This manual power management method makes it impractical for the engineers
to optimally determine the feeders to disconnect, hence the engineers end up
dropping more feeders than would have been dropped if computerized power
management software were employed.
50
CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
5.1 Conclusion
This research work provides a better strategy that can replace or
complement the conventional methods of Power System Management. This was
borne as a result of deregulation of electricity market, introduction of new
technologies and increase of local generations so as to reduce capital investment
and power losses and improve reliability and power quality delivery.
The problem of optimal power management development in power network is a
multi-criteria and dynamic task with a large number of state and decision
variables. This task must be formulated taking into consideration possible
influence of random and uncertain parameters that constantly vary. Solution of
such a problem is associated with considerable mathematical computation and
informational difficulties. The development of power management was
described by the following principal factors and parameter: Deterministic,
probabilistic, fuzzy and truly uncertain.
Fuzzy Logic based method was used in the development of power
management Algorithm. The approach was effective in implementing a simple
fuzzy procedure to solve a problem that required rigorous methods when the
conventional approach is used. Only the system frequency or voltage is
sufficient to implement this technique. The system simulation shows that the
51
proposed approach is able to make decision and serve as logic for system
stability, which acts to protect or save the network.
From the work, the following two main conclusion can be drawn;
1) Modern mathematical and computational tools provides the possibility to
solve the management problems of large power network in its general
formulation, accounting for
- Uncertain and random factors
- Multiple criteria
- Dynamic development process
2) Practical application of the suggested methods and algorithms would
require considerable efforts to achieve elaborate, powerful and user friendly
software that will be useful for providing:-
- Data gathering and processing
- Presentation of results
5.2 Recommendation
It is recommended that the energy management companies in Nigeria
should implement a strategy for the management of the electric power network
using artificial intelligence techniques such as use of fuzzy logic controller for
the load shedding power management.
52
REFERENCES
[1] V. Neimane, “On Development planning of electricity Distribution
networks,” Doctoral Dissertation, Royal institute of Technology
Department of Electrical Engineering, Electric power systems, Stockholm
publisher, pp1-16, 2001.
[2] A. E. Olufemi, “Power management systems in electrical networks,”
(SMIEE) (CRESTECH ENGINEERING LTD)held at Digital Bridge
Institute, Abuja 2007.
[3] L. P. Hajdu, J. Peschon, W. F. Tinney, and D. S. Piercy, “Optimum load-
shedding policy for power system,” IEEE Tran, PAS-87, No. 3, pp. 784–
794, 1968.
[4] D. K. Subramanian, “Optimum load shedding through programming
techniques,” IEEE Trans, PAS-90, pp. 89–94, 1971.
[5] S. M. Chan and F. C. Schweppe, “A generation rellocation and load
shedding algorithm,” IEEE Trans., PAS-98,No. 1, pp. 26–34, 1979.
[6] M. M. Adibi and D. K. Thorne, “Local load shedding,” IEEE Trans. On
Power System, Vol. 3, No. 3, pp. 1220–1226, 1988.
[7] S. Shah and S. M. Shahldehpour, “A heuristic approach to load shedding
53
scheme,” IEEE Trans, on Power System, Vol. 4, No. 4, pp. 1421–1429,
1989.
[8] R. Billinton and J. Satish, “Effect of rotational load shedding on overall
power system adequacy indices,” IEE Proc. C, Vol. 143, No. 2, pp. 181–
187, 1996.
[9] P. Wang and R. Billinton, “Optimum load-shedding technique to reduce the
total customer interruption cost in a distribution system,” IEE Proc. -
Gener. Transm. Distrib., Vol. 147, No. 1, pp. 51–56, 2000.
[10] D. Xu and A. A. Girgis, “Optimal Load Shedding Strategy in Power
Systems with Distributed Generation,” IEEE, pp. 788–793, 2001.
[11] Y. Lu, W. S. Kao, and Y. T. Chen, “Study of applying load shedding
scheme with dynamic d-factor values of various dynamic load models to
taiwan power system,” IEEE Transactions on Power Systems, Vol. 20,
No. 4, pp. 1976–1984, 2005.
[12] R. Faranda, A. Pievatolo, and E. Tironi, “Load shedding: A new proposal,”
IEEE Transactions on Power Systems, Vol. 22, No. 4, pp. 2086–2093,
2007.
[13] A. Wiszniewski, “New criteria of voltage stability margin for the purpose
54
of load shedding,” IEEE Transactions on Power Delivery, Vol. 22, No. 3,
pp. 1367–1371, 2007.
[14] Z. P. Ding, S. K. Srivastava, D. A. Cartes, and S. Suryanarayanan,
“Dynamic simulation-based analysis of a new load shedding scheme for a
notional destroyer-class shipboard power system,” IEEE Transactions on
Industry Applications, Vol. 45, No. 3, pp. 1163–1174, 2009.
[15] E. J. Thalassinakis, E. N. Dialynas, and D. Agoris, “Method combining
anns and montecarlo simulation for the selection of the load shedding
protection strategies in autonomous power systems,” IEEE Transactions
on Power Systems, Vol. 21, No. 4, pp. 1574–1582. 2006.
[16] J. N. Onah, T. C Madueme and V. C. Ukwueze “Contingency Evaluation
of a Peturbed Electric Network” International Journal of Electrical and
Electronics Engineering (IOSR-JEEE). Volume 10, Issue 5, PP. 136-142.
(Sep – Oct. 2015).
[17] TCN, “Power System Operation Seminar,” Kainji Hydro Electric Plc,
pp. 20-21, March, 2010.
[18] D. Paul Mitcheson, T. TzernToh, “Power Management
Electronics,”Energy Harvesting for Autonomous Systems, Norwood,
MA: Artech House, Inc, pp. 1-3, September, 2010.
55
[19] C. Tim Green, M. Eric Yeatman, D. Paul Mitcheson, “Power Processing
Circuits for Electromagnetic, Electrostatic and piezoelectric Energy
Scavengers,” Microsystems Technology, Vol. 13, pp. 29 – 32, April,
2007.
[20] Z. Krishans,V. Bochkareva, G. Anderson, “Dynamic Model for Planning
of Distribution System Under Uncertainty’’, universities power
Engineering conference(UPEC 97), Manchester, UK, pp 4-5, September
1997,
[21] Z. Krishans, V. Neimane, G. Anderson, “Dynamic Model for Planning of
Reinforcement Investments in Distribution Networks’’, power systems
computation conference (PSSC’ 99), Trondheim, Norway, pp 9-10, June
1999.
[22] I.J. Udej, I.S. Offia, A.C. Ihedioha, “Artificial intelligence based long
Distance Transmission line protection scheme for 300KV line in
Nigeria’’, Department of Electrical and Electronics Engineering, faculty
of Engineering, Enugu state University of science and Technology,
journal of Multidisciplinary Engineering science and
Technology(JMEST), vol.2 issue 7, pp 1 – 2, July, 2015.
[23] L.A. Zadeh; “Fuzzy sets, Fuzzy genetic system and out line of a new
approach to the analysis of complex system”. University of California,
USA, pp 20 – 33, November, 1965.
56
[24] G.J. Klir and T.A. Folger; “Fuzzy sets Uncertainty and Information”
Prentice Hall, Engle wood Cliffs, N.J., pp 34 – 35, February, 1988.
[25] C.C. Lee, “Fuzzy Logic in Control Systems” IEEE Trans. On Systems,
Man and Cybernetics, SMC, Vol.20; pp.35,40, March,1990.
[26] D.K. Steven, “Fuzzy Logic an Introduction part 2” (ONLINE) available
www.searchSeattlerobotics.org , pp 45 – 46, February, 2006.
[27] K. Kishan, M.R. Akbarzadeh, k. Kumbla, E. Tunstel, A.T
John’s,“AdaptaiveNeuro- fuzzy logic controller ona digital signal
processor’’,University of New Mexico, USA,pp 50 – 51, April, 1992.
[28]P. korthmann, “Design of fuzzy logic controller by means of fuzzy
models’’, Ruhr –University, Bochum, pp 54 – 67, September, 1990.
[29] J.Shing Roger Jang, C. Tsai Son, “Neuro – Fuzzy Modeling and Control,”
proceedings of the IEEE, pp 4 – 5, March, 1995.
[30] H.L. Willis, Power Distribution planning Reference Book, Marcel
Dekker, inc,pp 14 – 17, August, 1997.
[31] TCN, “Power system operation seminar’’, Kainji Hydro electric Plc,
pp 1 – 6, October, 2009.
57
APPENDIX A
Log extracted supplied by PHCN (Transmission Department)
Date: 21/02/2016
Time interval (Hours)
Load Demand (MW)
Supply (MW)
Disconnected Feeders
1800 – 2200 120
Kingsway II, Kingsway I Government House Independence Layout, Thinkers Corner
1300 – 1200
100 Kingsway II, Kingsway I Government House
0900 – 1200
90 Kingsway II, Government House, Independence Layout
0600 – 0800
60 Kingsway II, Government House
58
APPENDIX B
[System]
Name='LoadSheddingController'
Type='mamdani'
Version=2.0
NumInputs=5
NumOutputs=1
NumRules=8
AndMethod='min'
OrMethod='max'
ImpMethod='min'
AggMethod='max'
DefuzzMethod='centroid'
[Input1]
Name='Available_Energy'
Range=[0 145]
59
NumMFs=2
MF1='LOW':'trimf',[-117.8 0 117.8]
MF2='HIGH':'trimf',[100 120 145]
[Input2]
Name='Feeder Priority'
Range=[0 8]
NumMFs=2
MF1='LOW':'trimf',[-0.5 1.5 3]
MF2='HIGH':'trimf',[2.5 5 6]
[Input3]
Name='Number_Of_Feeders'
Range=[0 6]
NumMFs=2
MF1='LOW':'trimf',[-0.5 1.5 3]
MF2='HIGH':'trimf',[2.5 5 6]
[Input4]
60
Name='Reliability'
Range=[0 10]
NumMFs=2
MF1='LOW':'trimf',[-4 2.5 5]
MF2='HIGH':'trimf',[4.5 7.5 10]
[Input5]
Name='Period_Of_Supply'
Range=[0 10]
NumMFs=2
MF1='LOW':'trimf',[0 2.2 4]
MF2='HIGH':'trimf',[3.8 5 10]
[Output1]
Name='Supply'
Range=[0 1]
NumMFs=2
MF1='OFF':'trimf',[-0.4 0 0.4]
61
MF2='ON':'trimf',[0.1 0.5 0.9]
[Rules]
1 2 0 1 1, 2 (1) : 1
1 2 0 0 2, 1 (1) : 1
1 1 2 2 1, 2 (1) : 1
1 1 2 2 1, 2 (1) : 1
1 1 1 2 1, 1 (1) : 1
1 1 1 1 1, 1 (1) : 1
1 1 2 2 2, 1 (1) : 1
2 0 0 0 0, 2 (1) : 1
