Research Proposal for high voltage network

7/27/2019 Research Proposal for high voltage network

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A METAHEURISTIC LOAD SHEDDING ALGORITHM USING

VOLTAGE AND FREQUENCY PARAMETERS

CHARLES MWANIKI

A research proposal submitted to the Faculty of Engineering in Partial fulfillment of the

requirements for the Doctor of Philosophy in Electrical Engineering Jomo Kenyatta

University of Agriculture and Technology.

J.K.U.A.T

July, 2013


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DECLARATION

I declare that this research proposal is my original work and it has not been presented for an

award of a degree, diploma or certificate in this or any other university.

CHARLES MWANIKI SIGN:_________________DATE: _____________

RECOMMENDATION/APPROVAL

This research proposal has been submitted with my approval as the university supervisor.

DR CHRISTPHER MAINA MURIITHI SIGN_______________DATE: ________

This research proposal has been submitted with my approval as the university co-supervisor.

DR NICODEMUS ABUNGU SIGN___________________DATE: ______________


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TABLE OF CONTENTS

DECLARATION ....................................................................................................................... 2

RECOMMENDATION/APPROVAL ....................................................................................... 2

TABLE OF CONTENTS ........................................................................................................... 3

1 INTRODUCTION .............................................................................................................. 4

1.1 Background .................................................................................................................. 4

1.2 Problem Statement ....................................................................................................... 4

1.3 Relevance of Study/Justification ................................................................................. 5

1.4 Objectives .................................................................................................................... 6

1.4.1 Main objective ...................................................................................................... 6

1.4.2 Specific objectives ................................................................................................ 7

1.4 Scope of Study ............................................................................................................. 7

1.5 Importance of the study ............................................................................................... 7

2 LITERATURE REVIEW ................................................................................................... 8

2.1 Under voltage Load Shedding ..................................................................................... 8

2.2 Under frequency Load Shedding ............................................................................... 10

2.3 Under voltage under frequency Load Shedding .......................................................... 17

3 METHODOLOGY ........................................................................................................... 20

3.1 Metaheuristics algorithm ........................................................................................... 20

3.2 Harmony Search Algorithm ....................................................................................... 20

3.3 Cuckoo Search Optimization ..................................................................................... 21

4 THESIS DEVELOPMENT SCHEDULE......................................................................... 23

5 BUDGET .......................................................................................................................... 24

6 REFERENCES ................................................................................................................. 25


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1 INTRODUCTION1.1 BackgroundThe developing industries and their growing infrastructure have stressed the power industry to

supply sufficient power. The generation capacity should increase in proportion to the increase

in the number of loads. Large power transfers across the grid lead to the operation of the

transmission lines close to their limits. Additionally, generation reserves are minimal and

often the reactive power is insufficient to satisfy the load demands. Due to these reasons

power systems become more susceptible to disturbances and outages. Some of the

disturbances experienced by the power system are faults, loss of a generator and or

transmission line and sudden switching of loads [1-3]. These disturbances vary in their

intensity. As a result it is necessary to study the system and monitor it in order to prevent it

from becoming unstable.

The two most important parameters to monitor are the system voltage and frequency, both of

which must be maintained within prescribed limits standards to ensure that the system

remains stable. The frequency is mainly affected by the active power, while the voltage is

mainly affected by the reactive power. Specifically, the frequency is affected by the difference

between the generated power and the load demand. This difference is caused due to

disturbances which reduce the generation capacity of the system. For example, due to the loss

of a generator, the generation capacity decreases while the load demand remains constant. If

the other generators in the system are unable to supply the power needed, then the system

frequency begins to decline. To restore the frequency within the prescribed limits a load

shedding scheme is applied to the system.

In addition, the reactive power demand of the load affects the voltage magnitude at that

particular bus. When the power system is unable to meet the reactive power demands of the

loads, the voltages become unstable. In such situations, capacitor banks are switched on to

supply the reactive power to the loads. However, when these capacitor banks are unable to

restore the voltage levels within their upper and lower limits, the system resorts to load

shedding.

1.2 Problem StatementLoad shedding is an emergency control action to ensure system stability, by curtailing system

load. The emergency load shedding would only be used if the frequency or voltage falls

below a specified frequency/voltage threshold. Typically, the load shedding protects againstexcessive frequency or voltage decline by attempting to balance real and reactive power


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supply and demand in the system. The number of load shedding steps, amount of load that

should be shed in each step, the delay between the stages, and the location of shed load are the

important objects that should be determined in a load shedding algorithm.

Despite being successful to a great extent, the conventional load shedding schemes have

certain disadvantages. The amount of a load step is, at times, large which causes excessive

load to be shed. Most schemes do not have the flexibility to increase the number of load

shedding steps, thereby introducing transients in the system.

The most LS schemes proposed so far used voltage and frequency parameters, separately and

also, the under-frequency and under-voltage relays are working in the power system without

any coordination. The individual use of these indices may be also not reliable/effective, and

may even lead to the over load shedding problems. Studying on the under-frequency load

shedding is often done using the system frequency response models. The impact of voltage

variation on the frequency deviation is not considered in these models. Furthermore, the

UVLS methods that are proposed so far for adjusting the under-voltage relays, does not

consider the frequency behavior. These two parameters (voltage and frequency) are not

independent and the coordination between UFLS and UVLS schemes is therefore crucial. The

dependency between voltage and frequency will affect LS performance.

Economical considerations need to be considered before shedding the load since certain loads

cannot be kept offline. Further, Load shedding is an emergency control operation and should

be on a priority basis, which means shedding less important loads, while expensive industrial

loads are still in service.

Therefore, this study focuses on developing an algorithm that is more reliable and effective

than the conventional schemes, that uses voltage and frequency parameters simultaneously for

making load shedding decisions. This will help in power system planning, operation and

control.

1.3 Relevance of Study/JustificationThe increase in electrical power consumption is directly proportional to the increase in people

population. As of 10th July, 2009, the world population was estimated by the United States

Census Bureau at 6.77 billion. This figure is expected to reach about 9 billion by the year

2040. Many environmental and economic constraints prevent the construction of new or

upgrading of the existing generation and transmission capacities. Additionally, generation

reserves are minimal and often the reactive power is insufficient to satisfy the load demands.


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Given this trend, power systems are expected to be more heavily loaded and moving closer

and closer to stability limit and more susceptible to disturbances and outages.

Blackouts of power systems always have been a historical problem in interconnected power

systems. However in recent years by improving monitoring and protection techniques, it is not

possible to completely prevent of blackouts [1-3]. Sudden and large changes in generation

capacity such as the outage of a generator can produce a sever imbalance between generation

and load demand. This may lead to a rapid decline in frequency, because the system may not

respond fast enough. If voltage and frequency get out from permissible range the system is in

unstable condition. In this condition the system controller's operate and attempt to restore the

voltage and frequency in the permissible range. If the disturbance is so large the controller's

cant restore the voltage and frequency in the permissible range. In this condition the last

solution to avoid the power system breakdown has been load shedding strategy.

Recent blackouts have brought our attention to the issues of voltage stability in the system.

Voltage decline can be a result of a disturbance. Its main cause, however, is insufficient

supply of reactive power. This has led researchers to focus on techniques to maintain voltage

stability. The loss of a generator causes an unbalance between the generated power and the

load demand. This affects the frequency and voltage. Load shedding schemes must consider

both these parameters while shedding load. By shedding the correct amount of load from the

appropriate buses, the voltage profile at certain buses can be improved [4].

While considering the amount of load to be shed and the step size, it is also important to take

into account the reactive power requirements of each load. Quite often, disturbances such as a

generator loss cause the voltage to decline. An effective way to restore voltage is to reduce the

reactive power demand. Thus when loads absorbing a high amount of reactive power are first

shed; the voltage profile can be improved.

1.4 Objectives1.4.1 Main objectiveThe main objective of the research is to investigate the applicability of a metaheuristic load

shedding algorithm using both frequency and voltage parameters.


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2 LITERATURE REVIEWDifferent methods for load shedding and restoration have been developed by many

researchers. Currently there are various under frequency and under voltage load shedding

techniques used in the power industry worldwide.

2.1 Under voltage Load Shedding

Lopes et all [5] suggests a method which carries out load shedding in case of two conditions.

One, where the load shedding occurs due to a post disturbance low voltage condition and

secondly, where the load shedding results due to the inability of the system to achieve a stable

operating condition during post disturbance. This method uses the load flow in order to decide

the buses from which to shed load. The initial set of control actions are first carried out. These

actions are capacitor switching, tap changing transformer and secondary voltage control.

Jianfeng et al [6] have developed a method with risk indices in order to decide which buses

should be targeted for load shedding to maintain voltage stability. The buses with a high risk

of voltage instability are considered first. This is estimated from the probability of a voltage

collapse occurrence. The risk indices are the products of these probabilities and impact of

voltage collapse.

Another method [7][31] dealing with the particle swarm approach for under voltage load

shedding has been researched. The particle swarm Optimization concept is a group or cluster

of particles in which each particle is known to have individual memory like an animal in its

herd or flock. The flock is initiated with some initial velocity and the particles move in

different directions to come up with the best solution. The best solution is shared with every

particle of the group so that they can move from there on based on this new acquired

knowledge. This same idea is used for under voltage load shedding to recognize the best

possible load shedding scheme considering the system conditions and disturbance particular

to that situation.

Ladhani and Rosehart [8] propose load modeling for an under voltage load shedding scheme.

They also suggest offering economic incentives to customers for discontinuing the use of

power during load control periods. This way the brunt of a sudden load shed is not borne by

the customer alone. Also, systematic load control will lead to the stability of the system even

when it is not faced with a disturbance.


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Yorino et al [9] suggests a new planning method for planning the VAR allocation using the

FACTS devices. Here, the total economic cost for a voltage collapse along with its corrective

control and load shedding are taken into account to come up with the optimum VAR planning

scheme. Thus, the objective function is to minimize the cost while keeping in mind the

voltage stability of the system.

Mozino [10] discusses the currently existing under voltage load shedding schemes.

They are divided into two categories; decentralized and centralized. The decentralized load

shedding involves setting relays at buses with loads to be shed and tripping the respective

relays. The centralized scheme is more advanced. The relays are installed at the key bus

locations and the information regarding which relays are to be tripped is sent to these relays

from a main control centre. Thus the required load is shed from appropriate buses. Many of

these schemes are referred to as special protection or wide area schemes. The two

categories mentioned above are widely used as under voltage load shedding relays. These

relays require logic and have to perform efficiently and accurately. Also, these relays must

avoid false operation. Thus to satisfy the above requirements digital relays are being used for

under voltage load shedding.

Single Phase UVLS Logic measures voltages on every phase. This scheme distinguishes

between voltage collapse and fault induced low voltages. The voltage collapse is a balanced

phenomenon, hence results in a reduction of voltage on all the three phases. Except for a three

phase fault all the other faults are unbalanced. The relays trips when it identifies a voltage

collapse and blocks the relay for a fault induced low voltage. Unbalanced faults usually

induce negative sequence voltages which are detected and used for blocking the relay.

Positive sequence UVLS logic checks the positive sequence voltage with the set point value.

Since the voltage collapse is balanced for all the three phases, the positive sequence voltage is

equal to the three phase voltages. In case of a fault condition, the negative sequence voltage is

utilized to block the relay.

A load shedding scheme against long term voltage instability is proposed by Van Cutsem et al

[11]. It uses distributed controllers which are delegated a transmission voltage and a group of

loads to be controlled. Each controller acts in a closed loop, shedding loads that vary in

magnitude based on the evolution of its monitored voltage. Each controller acts on a set of


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Another method [14] triggers the under frequency relays based on a dynamically changing

intelligent load shedding scheme. The main components of this scheme are the knowledge

base, disturbance list and the ILS computation engine.

Fig. 1 Block Diagram of the ILS scheme

The generalized structure of the ILS scheme is shown in figure 1. The knowledge base is the

most important block. It is connected to the computation engine which sends trip signals to

relays. The network models can be accessed by the knowledge base while monitoring the

system. The knowledge base is trained and its output consists of system dynamic scenarios

and frequency responses during disturbances. This trained knowledge base also monitors the

system continuously for all operating conditions. The disturbance list consists of pre-specified

system disturbances. Based on the inputs for the system and the continuous system updates,

the knowledge base notifies the ILS engine to update its load shedding list. Thus it ensures

that the load shed is always minimum and optimum.

Wee-Jen Lee [15] discuss about another intelligent load shedding based on microcomputers.

The unique feature about this scheme is the built in frequency setting and the time delay

setting. The frequency setting in the relay counters system re collapse situation. (Consider a

generator loss which triggers a load shedding step. This causes the frequency of the system to

recover. During this recovery period if another generator trips it results in a system re

collapse). Typical frequency relays will not trip until the second generator loss causes

sufficient frequency decay. The ILS system automatically adjusts the frequency settings such

that load is shed immediately without delay.

The time delay settings cause the load scheme to initiate during situations when a disturbance

causes the frequency to drop and hold at a value less than the rated. The number of load

shedding steps can be increased without a limit. The advantage of having large number of


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load shedding steps is that it prevents large amount of transients. It also prevents over

shedding.

Denis Lee Hau Aik, [16] suggests a method using the System Frequency Response SFR and

the Under Frequency Load Shedding UFLS together to get a closed form expression of the

system frequency such that the UFLS effect can be included in it. On doing this, the system

and UFLS performance indicators can be calculated. Thus these indicators can be used

efficiently in any further optimization techniques of SFRUFLS model. One such method

has been discussed using the regression tree by Chang et al [17]. The regression tree is

utilized to interpolate between recorded data to give an estimate of the frequency decline after

a generator outage. It is a non parametric method which can select the system parameters and

their relations which are most relevant to the load imbalance (due to generator outage) and the

frequency decline. The case considered here is only a generator outage but this method can be

applied to other forms of disturbances as well.

A Kalman filtering-based technique by A.A. Girgis et al [18] estimates frequency and its rate

of change which is beneficial for load shedding. The noisy voltage measurements are used to

estimate the frequency and its rate of change. A three-state extended Kalman filter in series

with a linear Kalman filter is used in a two stage load shedding algorithm. The output of the

three stage Kalman filter acts as the input to the linear Kalman filter. It is the second filter

which identifies linear components of the frequency and its rate of change. The amount of

load to be shed is calculated using the linear component of the estimated frequency deviation.

Another method uses Kalman filtering [19] to estimate the frequency and its rate of change

from voltage waveforms. The buses are ranked based on their rate of change of voltage

(dV/dt) values. The disturbance magnitude is calculated from the swing equation. The rate of

change of frequency required for this equation is calculated using the Kalman filter. Once the

total amount of load to be shed is estimated then the load to be shed from each bus is

determined based on the PV analyses.

An optimization technique for load shedding [20] with distributed generation was developed.

This technique converts differential equation into algebraic ones using the discretization

method. Two cases are considered here; one with the distributed generation switched on to the

system as a static model and the other case without the distributed generation on the grid.Both cases resulted in successful shedding of appropriate quantity of load.


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Li Zhang suggests a method [21] which designs under frequency relays using both the

frequency and the rate of change of frequency (df/dt). The scheme has been designed for a 50

Hz Northeast China power system. Traditional schemes required only the frequency decay

information. Here the rate of change of frequency is used as auxiliary information. The plots

for the rate of change of frequency are oscillatory in nature. Hence a new scheme is devised in

this paper which considers the integration of the rate of change of frequency (df/dt) to indicate

the frequency drop. By integrating one is effectively measuring the area between two

frequencies, fi-1 and fi. The schemes is made up of five load shedding steps for a 50 Hz system.

These steps are from 50 to 49.2 Hz, 49.2 to 49 Hz, 49 to 48.8Hz, 48.8 to 48.6 Hz, 48.6 to 48.4

Hz. The amount of load to be shed in each step is decided by integrating the df/dt value in

each step. The simulation results when compared with the old scheme with just the frequency

decay show a definite improvement in system frequency due to the inclusion of rate of change

of frequency (df/dt) in the new scheme.

The main idea in the paper proposed by Xiong et al [22] is the inclusion of on line load

frequency regulation factors. Loads with smaller frequency regulation factors are shed first,

followed by the ones with larger frequency regulation factors. The active power and load

frequency relation is established in the form of the following equation.

Where, fN is the nominal frequency. PLN is the rated active power and ai (i=1,2n) is the

percentage of the total load associated with the i-th term of the frequency. The per unit form

of the above equation is differentiated to get the change in load power as frequency changes

(dPL/df) which is theKL factor or regulation factor. The higher order terms are neglected.

Thus it is preferable to shed load for smaller regulation factors. Hence the loads are

distinguished based on their individual regulation factors and accordingly load shedding

schedules are planned based on their respective K factors.


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where is the disturbance magnitude in per unit. Now another variable is defined. If

a disturbance occurring at the weakest generator is less than this value then absolute

frequency of that generator is within the permitted limits. For a situation where the

disturbance magnitude, is less than no load shedding is required. The maximum

load shedding magnitude is equal to the difference between the disturbance magnitude and

Pthr - Pthr. The load to be shed is distributed inversely proportional to the generator

inertia to make the load shedding most effective. The equation (4) represents this distribution.

Based on this equation the layers of the load shedding scheme are designed. Both the steps

shed one third of the remaining load. These are in steps. They are presented in a table 1 with

the first step being at 59.3 Hz.

TABLE 1: SCADA Based Load Shedding Formula

An adaptive load shedding scheme which includes a self healing strategy is presented by

Vittal et al [26]. The proposed scheme is tested on a 179 bus 20 generator test system. This

self healing strategy comes into play when the system vulnerability is detected. The system

then divides into self sustaining islands. After this islanding, load shedding based on the rate


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of change of frequency is applied to the system. Due to this division, it becomes easier to

restore load. A Reinforcement Learning scheme is discussed in the paper.

The first is the controlled islanding which is done using the two-time scale method. It deals

with the structural characteristics of the power systems and determines the interactions of the

generators and their strong or weak coupling. The Dynamic Reduction Program 5.0

(DYNRED) is the software in which simulations are run to implement this technique.

Through this software coherent group of generators can be obtained on the power system.

Islanding causes two types of islands to be formed, the generation rich islands and the load

rich islands. The load rich islands may have a further decline of frequency. This may result in

the generator protection to trip the generators thus further declining the islands frequency.Thus a two layer load shedding strategy is employed for the load rich island. The first layer is

based on the frequency decline approach. The second layer considers the rate of change of

frequency. Due to the longer time delays and lower frequency thresholds for a frequency

based scheme inadvertent load shedding is avoided. When the system disturbance is large and

exceeds the signal threshold, the second layer comes into play. It sends a signal to discontinue

the first layer of operation and continues with the load shedding based on rate of change of

frequency. This layer will shed more load at the initial steps to prevent cascading effects. The

magnitude of the disturbance is found based on the formula

If we sum up all the equations for i=1 to n then the final equation obtained is

Where, m0 is defined as df/dtwhich is the average rate of frequency decline.

Rearranging the above equation we get a new equation which relatesPL to m0 .


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SinceHi is constant, the magnitude ofm0 can be directly proportional to the rate of frequency

decline. Hence the rate of change of frequency df/dt ) can be a measure of the disturbance.

Once the disturbance threshold value, PL , for the second layer of load shedding is decided,the m0 value is calculated. The mi at each bus is calculated and compared with m0 . Ifmi m0

then the second layer is activated, otherwise the conventional load shedding scheme is used.

This new shedding scheme increases the stability of the system by shedding fewer loads as

compared to the conventional scheme.

Application of Neural Network in load shedding and some Predictable functioning of load

shedding methods has been proposed [28 -30][32]. In this method the identification of the

variables like inputs and outputs is an important step for a successful application of this

technique. Sometimes a pre-processing stage is needed to choose the most significant

variables to be used as inputs of a NN. Some of the meaningful variables that have been used

as inputs of the NN

Active real power generation Active load generation Amount of active load being shed Percentage of exponential type loads being shed Damping factor Power factor

These variables provide the NN with valuable information, such that it can make the required

assessment with respect to how much the generation load disproportion has been corrected

and the influence each load type has on the resulting frequency response.

2.3 Under voltage under frequency Load Shedding

A load shedding scheme that incorporates, the frequency and the bus voltages, for deciding

the instant, the amount and the location of the load to be shed is proposed [27]. The scheme

developed consists of a stepwise approach. This has been represented in the form of an

algorithm.

The first step of the load shedding procedure is the measurement and calculation of the rate of

change of frequency. Depending on the relay, the frequency measurements or the rate of

change of frequency are recorded in the system. The total load mismatch between the


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generated power and load power is determined. For a single machine, the swing equation [1]

is given by

where, f0is the nominal frequency of the system and Pdiffis the difference in the generatedpower and the load power. In the above equation is replaced by f since = 2f. Thus arelation between the frequency and the power mismatch is obtained. This relation establishes

the estimated magnitude of the disturbance. The inertia constant in the above equation is the

kinetic energy Wkover the system base MVA. The inertia constants of all the machines in thesystem are on the base MVA.

In a large power system where there are many generators which maybe geographically far

away from each other,

Also, the equivalent mechanical and electrical powers are given as;

Pm = individual mechanical shaft power of each machine for all the machines in the system

Pe =

individual electrical power of each machine for all the machines in the system

Once the magnitude of the disturbance is determined using the above equivalent swing

equation, the location and the amount of load to be shed from each bus has to decided. In

order to do this, the buses are ranked according to the dV/dt values at the point of detection of

frequency decline. The bus with the largest dV/dt is listed at the top of the list and then so on

in the decreasing order.

Once the order is decided, the next step is to decide the amount of load to be shed at each bus.

This is decided based on the voltage sensitivity at each bus. Thus the bus with voltage

sensitivity very close to the instability limit will have a maximum load shed based on the

reciprocal of its sensitivity as a fraction of the sum of the reciprocals of all the load bus

sensitivities.


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The load shedding scheme has been tested on the IEEE 39 bus and IEEE 145 bus test systems.

Three studies based on contingency were carried out with each contingency being considered

at a time:

Case study 1: Loss of a generator for the IEEE 39 bus system. Case study 2: Loss of a generator for the IEEE 145 bus system. Case study 3: Loss of a transmission line IEEE 39 bus system

The scheme is simple and does not involve complicated calculations. It proved to be

successful in restoring the frequency within its pre-defined limits. It has also improved the

voltage profile at certain buses which had critically low voltage before load shedding was

applied. However the algorithm did not address some aspects and therefore requires fine

tuning.

Economical considerations need to be considered before shedding the load since certain loads

cannot be kept offline. The study and testing of the scheme in a multiple contingency scenario

like loss of a generator along with a loss of transmission line need to be considered as this

would create a critical situation.

Thus the various conventional schemes, under frequency schemes and under voltage load

shedding schemes have been discussed above. These give an insight about the technological

advancement achieved in this area. The proposed study intends to investigate the applicability

of harmony search algorithm as an optimization tool and how it can be applied to address the

disadvantages faced by the conventional schemes present in the industry.


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3 METHODOLOGY3.1 Metaheuristics algorithmMetaheuristic algorithms are higher-level heuristic algorithms. Here, meta-means higher-

level or beyond, so metaheuristic means literally to find the solution using higher-level

techniques, though certain trial-and-error processes are still used. Broadly speaking,

metaheuristics are considered as higher-level techniques or strategies which intend to combine

lower-level techniques and tactics for exploration and exploitation of the huge space for

parameter search.

There are two important components in modern metaheuristics, and they are: intensification

and diversification. For an algorithm to be efficient and effective, it must be able to generate a

diverse range of solutions including the potentially optimal solutions so as to explore the

whole search space effectively, while it intensifies its search around the neibourhood of an

optimal or nearly optimal solution. In order to do so, every part of the search space must be

accessible though not necessarily visited during the search. Diversification is often in the form

of randomization with a random component attached to a deterministic component in order to

explore the search space effectively and efficiently, while intensification is the exploitation of

past solutions so as to select the potentially good solutions via elitism or use of memory or

both [33-35]. If the intensification is too strong, only a fraction of local space might be

visited, and there is a risk of being trapped in a local optimum, as it is often the case for the

gradient-based search such as the classic Newton-Raphson method. If the diversification is

too strong, the algorithm will converge too slowly with solutions jumping around some

potentially optimal solutions. In this study good balance of these two important components

will be maintained.

Another important feature of modern metaheuristics is that an algorithm is either trajectory-

based or population-based. It is difficult to decide which type of method is more efficient as

both types work almost equally successfully under appropriate conditions. In this study the

focus will be on population based algorithm.

3.2 Harmony Search AlgorithmIn the HS algorithm, diversification is essentially controlled by the pitch adjustment and

randomization -- here there are two subcomponents for diversification, which might be an

important factor for the high efficiency of the HS method. The first subcomponent ofcomposing new music, or generating new solutions, via randomization would be at least at


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the same level of efficiency as other algorithms by randomization. However, an additional

subcomponent for HS diversification is the pitch adjustment characterized by rpa. Pitch

adjusting is carried out by adjusting the pitch in the given bandwidth by a small random

amount relative to the existing pitch or solution from the harmony memory. Essentially, pitch

adjusting is a refinement process of local solutions. Both memory consideration and pitch

adjusting ensure that the good local solutions are retained while the randomization and

harmony memory considering will explore the global search space effectively. The subtlety of

this is that it is a controlled diversification around the good solutions (good harmonics and

pitches), and it almost acts like an intensification factor as well. The randomization explores

the search space more efficiently and effectively; while the pitch adjustment ensures that the

newly generated solutions are good enough, or not too far away from existing good solutions.

The intensification is mainly represented in the HS algorithm by the harmony memory

accepting rate raccept. A high harmony acceptance rate means the good solutions from the

history/memory are more likely to be selected or inherited. This is equivalent to a certain

degree of elitism. Obviously, if the acceptance rate is too low, the solutions will converge

more slowly.

Furthermore, the HS algorithm is a population-based metaheuristic, this means that multiple

harmonics groups can be used in parallel. Proper parallelism usually leads to better

implantation with higher efficiency. The good combination of parallelism with elitism as well

as a fine balance of intensification and diversification is the key to the success of the HS

algorithm, and in fact, to the success of any metaheuristic algorithms. These advantages make

it very versatile to combine HS with other metaheuristic through hybridization .This research

will focus on hybridization of Harmony search algorithm with Cuckoo search optimization.

3.3 Cuckoo Search OptimizationCuckoos are brood parasites that lay their eggs in the nests of other birds (such as crows) who

serve as hosts to hatch their eggs. To elucidate, let there be n parasites and equally many

(although not necessarily) hosts. Each parasite individual would be represented by a point (x

in m-dimensional space) and similarly each host individual would be represented by a point (y

in m-dimensions). These points may be randomly generated and would lie in the domain of

the function to be optimized. Each cuckoo would take a Lvy flight and if its post-flight

fitness is better than its pre-flight fitness, it would randomly choose a host nest that has not as

yet been invaded by another cuckoo and the quality of the host eggs are inferior to the cuckooegg. If this condition is not met, it would not lay any egg in the host nest. The egg of a


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successful parasite may, however, be detected (with probability p) by the host and be

destroyed. If not detected, however, it would be hatched in the host nest and eventually join

the cuckoo population. Only the best n cuckoos, however, would enter into the next

generation.

To implement this search scheme, Yang & Deb[37] formulated the following idealized rules:

(a) Each cuckoo lays a single egg into a randomly chosen host nest from among n nests; (b)

The nests with better quality eggs (implying better fitness value of the function concerned), if

not detected, would be hatched to grow into the cuckoo chicks, who would join the next

generation; (c) The number of available host nests is fixed. The host can detect the alien egg

with a probability [0, 1] and, if detected, it will either abandon the nest and build a new nest

elsewhere or destroy the egg; (d) When generating new solutions xi(t+1) from the old one xi

(t),

Levy flight is performed with parameter 1< < 3 and thus

The Lvy flight is a type of random walk which has a power law step length distribution with

a heavy tail.


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4 THESIS DEVELOPMENT SCHEDULE

YEAR 1 YEAR 2 YEAR 3

ACTIVITY

Jul-13

Aug-13

Sep-13

Oct-13

Nov-13

Dec-13

Jan-14

Feb-14

Mar-14

Apr-14

May-10

Jun-14

Jul-14

Aug-14

Sep-14

Oct-14

Nov-14

Dec-14

Jan-15

Feb-15

Mar-15

Apr-15

May-15

Jun-15

Jul-15

Aug-15

Sep-15

Oct-15

Nov-15

Dec-15

Jan-16

Feb-16

Mar-16

Apr-16

May-16

Jun-16

Proposal Editing and Presentation

Literature Review:Harmony search algorithm study

and coding

Load shedding study using harmony

search algorithm

Paper Presentation

Hybridization of harmony search

ith Cuckoo search algorithm

Load shedding study using the

hybrid algorithm

Paper Presentation

Thesis Writing & Presentation

Thesis Examination

Thesis Corrections and editing

Thesis Defence


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5 BUDGET

ITEM COST (KSH)

1

Stationery, Printing, Photocopying for

entire research period 40,000

2

Computer Soft ware ( C++, Matlab and

associated tool boxes) 80,000

3 Computer Hardware: Printer, Laptop 80,000

4 Access to journals: IEEE, Actpress 50,000

5 Transport 50,000

6 Books 40,000

7 Conferences and Paper presentation 360,000

8 Thesis printing and binding 20,000

Total 720,000


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Documents

Research Proposal for high voltage network