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Minimization of Losses in HVDC Distribution System

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1.1 Overview:

Economic development of a country depends on the energy availability and its consumption. In nature energy exists in different form but the most important form is the electrical energy. If the supply of electrical energy halts even for few minutes, many necessary functions of present-day life stop. Electrical energy has played a great role in building up of present day civilization. Electrical energy has made our life easier, comfortable and saves our time. Now there is shorter working day and technology based on electricity resulted in a higher agricultural and industrial production, and better transportation facilities. Even the standard of living of a person is decided by its energy consumption. In fact, the greater the per capita consumption of energy in a country, the higher is the standard of living of its people. Today modern society is so much dependent upon the use of electrical energy that it has become an important part of our life. Earlier it was not so, electricity was used for the basic purpose of light and heat and thus there was little demand for electrical energy and it was easy for the power companies to meet their demand. But in today’s modern world, energy demand is increasing day by day and to meet this ever increasing demand power companies are making every effort to increase the energy availability.

About 30 to 40 % of total investments in the electrical sector go to distribution systems, but nevertheless, they have not received the technological impact in the same manner as the generation and transmission systems. Modern distribution system is constantly being faced with ever growing load demand, this increase in load demand results into increase burden and reduced voltage. The distribution network has also a typical feature that the voltage at buses reduces as it moves away from substation.

This decrease in voltage is mainly due to insufficient amount of reactive power. Thus, to improve voltage profile and to voltage collapse reactive compensation is required. It is seen that distribution losses are high as compared to transmission system. To improve efficiency of power delivery in distribution system various arrangements can be worked out like network configuration, shunt capacitor placements etc. As these shunt capacitors supply reactive power demand which in turn reduces current and MVA in lines. Installation of capacitors helps in reducing energy losses, peak demand losses and improvement in voltage profiles, power factor of the system and system stability. However, to achieve these objectives, sizes and location of capacitors and economy should be decided.

Electric power is normally generated at 11 kV in a power station. As the load centers are located at a far distance from the generating station, thus there is a need to transmit the electric power from generating station to the load centre. To transmit power over long distances, it is then stepped-up to 400kV, 220kV or 132kV as per requirement. Power is carried through a transmission network of high voltage lines. Usually, these lines run into hundreds of kilometers and deliver the power into a common power pool called the grid. The grid is connected to load centers (cities) through a sub-transmission network of normally 33kV (or sometimes 66kV) lines. These lines terminate into a 33kV (or 66kV) substation, where the voltage is stepped-down to 11kV for power distribution to load points through a distribution network of lines at 11kV and lower to provide supply to the customers both three phase and single phase as shown in Fig 1.1 [pic]

Fig1.1 Typical Power Transmission and Distribution System
The primary and secondary power distribution network, which generally concerns the consumer, is the distribution network of 11kV lines or feeders downstream of the 33kV substation. Each 11kV feeder which emanates from the 33kV substation branches further into several subsidiary 11kV feeders to carry power close to the load points (localities, industrial areas, villages, etc). At these load points, a transformer further reduces the voltage from 11kV to 415V to provide the last-mile connection through 415V line also called as Low Tension (LT) line to individual customers, either at 240V as single-phase supply or at 415V as three-phase supply. A feeder could be either an overhead line or an underground cable. In urban areas, owing to the density of customers, the length of an 11kV feeder is generally up to 3 kms. On the other hand, in rural areas, the feeder length is much larger even up to 20 kms. A 415V line should normally be restricted to about 0.5-1.0 km. Distribution networks are typically of two types radial or interconnected. A radial network leaves the station and passes through the network area with no normal connection to any other supply.

This is typical of long rural lines with isolated load areas. An interconnected network is generally found in more urban areas and will have multiple connections to other points of supply. These points of connection are normally open but allow various configurations by the operating utility by closing and opening switches. The benefit of the interconnected model is that in the event of a fault or required maintenance a small area of network can be isolated and the remaining kept on supply. In existing distribution systems, the voltage at buses reduces when moved away from the substation, also the losses are high. The reason for high losses is the use of low voltage for distribution as the current is high in the low voltage system and thus more losses. Thus by using high voltage for distribution we can reduce the losses as current in high voltage distribution system (HVDS) is low. In the existing system pilferage is very easy because of lengthy bare LT conductor, and thus many unauthorized connections are tapped from the bare LT conductor. 1.3.1 LOSSES IN THE DISTRIBUTION SYSTEM

The losses prevailing in the existing power distribution network can be classified as:

1. Technical losses
2. Non Technical losses

1. Technical losses
Technical losses on distribution systems are primarily due to heat dissipation resulting from current passing through conductors and from magnetic losses in transformers. Technical losses occur during transmission and distribution involves substation, transformer, and line related losses. These include resistive losses of the primary feeders, the distribution transformer losses (resistive loses in windings and the core losses), resistive losses in secondary network, resistive losses in service drops and losses in KWh meter. These losses are inherent to the distribution of electricity and cannot be eliminated but can be reduced.


2. Non -technical losses

Non-Technical losses (NTL) include electricity theft. Electricity theft is defined as a conscience attempt by a person to reduce or eliminate the amount of money he will owe the utility for electric energy. It can be done by tampering with the meter to create false meter reading i.e. create false consumption information used in billings, meters not read, non performing and under performing meters, making unauthorized connections and direct tapping. Non-payment, as the name implies, refers to cases where customers refuse or are unable to pay for their electricity consumption. It is estimated that electricity theft costs in our country is in crores in a year.

Both the technical and non-technical losses are together termed as T&D (transmission and distribution) losses. In India, average T&D losses are estimated as 23% of the electricity generated. But in actual practice these losses are as high as 50% in some states of India. In addition to above two types of losses, there is also a loss in revenue due to non realization of revenue billed and the aggregate of all these losses is termed as AT&C (aggregate technical and commercial) losses. For this issue, Electricity Board is trying to draw attention to the need for reforms in electricity transmission and distribution sector, create mass awareness about transmission losses due to theft and misuse of electric energy. Also effective checks and balances in power distribution at various levels are imperative and to strictly implement timely revenue collection. 1.3.2 REASONS FOR HIGH T&D LOSSES

To understand the method to reduce the losses, it is necessary to look for various reasons responsible for higher losses in the existing system. The main reasons are: i. Lengthy distribution lines

In rural areas the 11 kV and 415 volts lines are hurriedly extended over long distances to feed loads scattered over large areas. This results in high line resistance and therefore, high resistive losses i.e. i2r losses in the line.

ii. Inadequate size of conductors
The size of the conductors should be selected on the basis of KVA X KM capacity of the standard conductor for a required voltage regulation. As the rural loads are usually scattered and generally fed by the radial feeders, the inadequate size of conductors lead to the overloading of conductor and thus more losses. iii. Over-rated distribution transformers and hence their under utilization It is revealed from the study of 11 kV feeders that the rating of DTs is much higher than the maximum KVA demand on the feeder. Over rated transformers draw an unnecessary iron losses as well as high capital costs has locked up in over rated DTs. iv. Low power factor

It is found that the power factor ranges from 0.65 to 0.75 in most of the LT distribution circuits. A high current is drawn for low power factor for a given load and consequently the losses proportional to i2r losses will be more. v. Poor HT/LT ratio

Ideally the HT/LT ratio should be 1:1. But, due to the consequent expansion of LT lines because of the extensive electrification of the domestic sector in the State, ratio is now 1:6.25. vi. Low voltage appearing at transformers and consumers terminals Performance of the motor is affected whenever the voltage varied from the rated voltage. For a voltage drop of 10%, the full load current drawn by the induction motor increases by about 10% to 15% the starting torque decreases by nearly 19% and the line losses in the distributor increases by about 20%. vii. Distribution transformer not located at load center

Often DTs are not located centrally with respect to consumers. Consequently, the farthest consumers obtain an extremely low voltage even though a reasonably good voltage levels maintained at the transformers secondary and this leads to higher losses due to decreased voltage and increased current at the consumer end. viii. Poor quality of equipments

In rural areas poor quality of equipment are used in agricultural pumping and in urban areas poor quality of cooler, air-conditioners and industrial load results in high power losses.

ix. Unbalanced phases
Since the load points are randomly distributed and it is not possible to divide the load equally among all the phases. This unbalanced phases causes the current to flow in the neutral as well which leads to power losses. x. Direct tapping by the non-customers

In certain areas mainly in domestic and agricultural categories, direct tapping of power by non-customers is widely prevalent. Since it is often not possible to find out culprit, the stolen energy cannot be measured and thus cannot be charged to anyone. Stolen energy is, therefore, considered as a part of line losses. xi. Too many stages of transformations

While transmitting the electrical power from generating station to consumer end, it undergoes too many transformation stages and the losses occur during each transformation stage. As a result high power losses occur in the existing system. xii. Transformer Losses

Distribution transformer losses include resistive loses in windings and the iron losses in the core. Today the majority of transformer’s core is made of CRGO (conventional silicon steel) which leads to an increase in copper as well as iron losses in the transformer. xiii. Bad workmanship

Increase in distribution losses is also due to bad workmanship. As the power loss occurs at joints and bad workmanship resulting in poor contacts at joints and connections which leads to pilferage of energy. xiv. Defective metering, billing and collection functions

It is due to the wilful burning of meters, errors in meter reading and recording, and improper testing and calibration of meters. These losses are due to the dishonest workers and lack of adoption of technology in the department and contribute to the loss in revenue. xv. Pilferage by the existing customers

Pilferage or theft by the existing consumers is the predominant cause of loss of revenue to the electrical utilities. It is mostly done by direct bypassing the meter and also by tampering the meter. Tampering can be done by mechanical jerks, placement of powerful magnets or disturbing the disc rotation with foreign matters. 1.3.3 LOSS REDUCTION TECHNIQUES

The various loss reduction approaches are:
i. Network reconfiguration and Phase Load Balance
Network reconfiguration includes the formation of new links within a feeder to form a tree structure and bifurcation of existing feeder to form parallel paths of power flow. Erection of interlinking lines to change the area of feed from one substation to another and balance the load among the substation.

ii. Automatic voltage booster

Automatic voltage booster (AVB) boosts the voltage in discrete steps at its point of location and it results in improvement of voltage profile. It also reduces the losses in the section beyond the point of location of automatic voltage booster towards receiving end. iii. Network reconductoring

Network reconductoring is the replacement of the existing conductor on the feeder with optimal conductor size for optimal length of feeder. In developing country like India where load growth is high and the conductor sizes are chosen to minimize the initial capital investment, network reconductoring is extremely fruitful to minimize the losses and improves the voltage profile. iv. Reactive Power Compensation

The load on the distribution system is mostly inductive and requires large reactive power. Shunt capacitor provide reactive power compensation at its location, independent of the load. Series capacitor introduces negative reactance in line and improves the voltage which in turn also reduces the power losses.

v. Distribution Transformers Locating and Sizing
DTs should be located as near to the load center as possible and replacement of large transformers by the transformers of small rating such that one transformer serves four or five consumers. vi. High-efficient Transformer

Use of high-efficient transformer i.e. using amorphous core transformers instead of CRGO transformer will reduce core losses (magnetizing or no load losses). vii. High voltage distribution system (HVDS)

HVDS is most effective method in reducing the technical losses and improving the quality of supply in power distribution system. In this system high voltage lines are extended to as nearer to the loads as possible and erect small size transformers. This system aims at LT less system or less LT and the unavoidable short LT lengths to be covered by insulated wires like ABC (Aerial Bunched Cables). viii. Aerial Bunched Cables (ABC)

Aerial Bunched Cable (ABC) is a very novel concept for over head power distribution. ABC provides higher safety and reliability, lower power losses and eliminates the hooking. This system is ideal for rural distribution and especially attractive for installation in difficult terrains such as hilly areas, forest areas, coastal areas etc. In congested urban areas with narrow lanes and by-lanes, the best choice for power distribution is ABC. From the above points it is seen that there are number of ways to reduce the losses but, in this thesis optimization and network configuration technique is used.


At present, the Power System is large, complex and critical. Three phase unbalance is a serious issue in distribution feeder. The severity is due to the availability of three types of phases i.e., single, two and three phases in the distribution feeder. The variation of phases is due to industrial, commercial and household customer. Customer demand is responsible for varying the feeder loading and it affects the load forecasting of a particular area. It actually depends on the nature of electricity consumption of that locality and it is totally dependent on the quantity and quality of residents or consumers of that area. Therefore an optimized scheme should be incorporated in the system to have minimum loss with economical benefits. This micro level objective can be approached by doing capacitor allocation at the sensitive load buses with optimum value. Network reconfiguration can be done with capacitor allocation to have more benefits. Reconfiguration of the network balances the conglomerated load.

If there is a lump of load at a certain bus, then by shifting the load efficiently to another light load bus can reduce the active power loss. It also stabilizes the system and maintains the nominal voltage at the buses. Though with the prevailing condition of Power System capacitor allocation and network reconfiguration is very much tough and cumbersome approach to reduce the line loss. But it is a useful and less hazardous way to minimize the line loss economically. However physically it is a well understood fact that capacitor allocation and network reconfiguration is necessary for loss minimization. But it requires a mathematical study for searching the exact configuration i.e., sensitive bus locations and optimal values of capacitor. These days, optimization techniques mainly soft computing techniques are accepted as mathematical tool for searching the best configuration. Soft computing techniques especially Metaheuristic Techniques such as Genetic Algorithm, Particle Swarm Optimization, Fuzzy logic approach, Ant Colony optimization approach, etc. are recently used for searching optimal configurations. Basic overview of optimization technique and application of it that used in this work for real loss minimization are described in the chapter 3.



Tanuj Manglani, Y.S Shishodia describes various capacitor placement techniques inrefrence[1]. The paper focuses on both classical and artificial intelligence (AI) methods. The objective of this paper is to survey on various methods and it has concluded after survey that classical methods are simpler but have some demerits like poor handling of qualitative constraints and slow computation with variables. Also, they are expensive for large and non linear systems. Whereas, AI methods are fast and versatile. These methods are convenient and suitable for large and nonlinear systems.

Shunt capacitor placement for radial distribution systems is explained in reference [2]. The objective of this method is to present a graph search algorithm that determines the number, sizes, locations, types and switching times for capacitor to be placed on distribution system so that savings increased due to reduction in peak power and energy losses. It can handle standard capacitor sizes and costs, and the resulting non differentiable objective function with ease. The mathematical simplicity of the method used in the paper , makes it possible to include many features in the algorithm that would be rather difficult. This graph search algorithm is used for placement of shunt capacitors on distribution system of large electric utility.

The optimal location and sizes of capacitors on a radial distribution systems to improve voltage profile and to reduce the active power loss is described in reference [3]. In this paper, K.Prakash, uses Loss sensitivity factor (LSF) and Particle Swarm Optimization (PSO) for capacitor placement and sizing respectively. PSO is used for estimation of required level of shunt capacitive compensation to improve voltage profile. The main advantage of this proposed method is that it decides the location and size of capacitors to realize the optimal sizable reduction in active power loss and significant improvement in voltage profiles. This method is tested on 10, 15 and 34 bus radial distribution systems and results are very promising. The method places capacitor at less number of locations with optimum sizes and offer saving in intial investment and regular maintenance.

Hamouda and Zeher presents analytical formulation of the reactive energy compensation which are characterised by their radial configuration on distribution lines. The objective of this paper to determine sizes and locations of a given number of fixed capacitor banks placed on a non-homogenous radial line with non constant voltage. In this paper, an iterative method called voltage drop method is applied to calculate voltage rms values and phase angles at all the nodes and on the capacitor banks. For reactive and active power losses, the mathematical models of the current distributions are made. Also, new models are used for reactive optimisation process and for the power and energy loss. With these models, various capacitors sizes are found but not of standard size ; then determine the effect of all the capacitors in the calculation of the loss reductions due to particular one. The result obtained then, are promising.

An improved local variation algorithm for optimal placement and sizing of fixed and switched capacitor banks in radial distribution networks under non sinusoidal operating conditions is explained in reference[5]. The objective of this paper is to proposed method which is a combination of maximum sensitivities selection and fuzzy theorem and are used to improve convergence characteristics of local variations method for discrete optimization problem of fixed and switched shunt capacitor placement and sizing with different load levels. The objective functions used are saving of energy loss cost due to installed capacitors and cost of switched and fixed capacitors and saving due to reduction in peak losses. In this paper, inclusion of sensitivity analysis for constrains and objective function to improve convergence and decrease computing times; and inclusion of fuzzy combination with objective function prevents the occurance of resonance harmonic.

An efficient and simple approach to decide the optimal locations and sizes of the compensation shunt capacitors in a distributuion system based on the total reactive loss is explained in reference [6]. The approach is implemented in two stages.First, a capacitor rated at the reactive power needed at certain busbar is directly connected at this busbar. This is applied on all buses of the distribution system simultaneously. The second is to connect a single capacitor rated at the total reactive power losses needed by the distribution system at certain bus. Optimization technique is used to determine the candidate bus at which this capacitor should be connected. This approach is tested on two practical distribution systems with 9 and 34-buses respectively. With this approach, the power loss reduction and voltage profile can be improved.

Utilization of fixed capacitors is one of the most important methods in loss reduction and improving the voltage profile of distribution systems [7]. The objective of the paper is to find the optimal locations and sizes of capacitor by using index vector method and by Loss sensitivity factor method and Particle swarm optimization method. And the sizes of capacitors, voltages, and power losses are compared and it has concluded that loss reduction is same but, the amount of reactive power requirements is less in Loss sensitivity method and Particle swarm optimization as compared to index vector method. The voltages obtained by LSF and PSO methods are slightly less, they are in acceptable limits and efficient. This paper also intend that the locations and sizes find by both the methods are different. But , total reactive power used for compensation is closer to each other. The maximum reduction in active and reactive power loss is also same for the given system

A novel method to determine suitable candidate nodes in distribution systems for placement of capacitors is described in reference [8]. The objective of paper to present a power losses based approach to determine appropriate capacitor locations and an Index and genetic algorithm based approach for optimal capacitor sizing. With this approach, efficient and suitable location and the corresponding sizes of capacitor are determined and power loss reduction is more than 50% which is very significant for a radial distribution system.

A computationally efficient methodology for the optimal location and sizing of static and switched shunt capacitors in large distribution systems is described in reference [9]. The problem is formulated as the maximization of the savings produced by the reduction in energy losses and the avoided costs due to investment deferral in the expansion of the network. The proposed method selects the nodes to be compensated, as well as the optimal capacitor ratings and their operational characteristics, i.e. fixed or switched. After an appropriate linearization, the optimization problem was formulated as a large-scale mixed-integer linear problem, suitable for being solved by means of a widespread commercial package. Results of the proposed optimizing method are compared with another recent methodology reported in the literature using two test cases: a 15-bus and a 33-bus distribution network. For the both cases tested, the proposed methodology delivers better solutions indicated by higher loss savings, which are achieved with lower amounts of capacitive compensation.

Sushant paul and Dr. Ward Jewell present a proposed methodology to determine the optimal capacitor locations and sizes for powerloss reduction in a radial distribution system in reference [10] . The objective is to minimize energy loss by considering capacitor cost. In this paper, both the power loss index (PLI)-based approach and the loss sensitivity coefficient-based approach are comparatively studied to determine the optimal capacitor location. The index-based approach combined with a genetic algorithm is used to determine the capacitor sizes. They also discuss about how customer loads and costs change after reactive power compensation. The proposed method were tested on the IEEE 13-bus and 34-bus test systems, and the results are comparatively analyzed and promising.

A procedure for solving the capacitor placement problem is presented in reference[11]. The objective is to determine the minimum investment required to satisfy suitable reactive constraints. Due to the discrete nature of reactive compensation devices, optimal capacitor placement leads to a nonlinear programming problem with mixed (discrete and continuous) variables. It is solved with an iterative algorithm based on successive linearizations of the original nonlinear model. The mixed integer linear programming problem to be solved at each iteration of the procedure is tackled by applying both a deterministic method (branch and bound) and genetic algorithm techniques. A hybrid procedure, aiming to exploit the best features of both algorithms is also considered. This procedure consists in carrying out a limited µGA search including the incomplete branch and bound solution in the initial population. The hybrid procedure achieved a saving in installation cost of about 16% with respect to the incomplete branch and bound solution.

A fuzzy-based approach for optimal placement and sizing of fixed capacitor banks in radial distribution networks in the presence of voltage and current harmonics in reference[12]. The objective function includes the cost of power losses, energy losses, and capacitor banks. Constraints include voltage limits, number/size and locations of installed capacitors, and the power quality limits of IEEE-519 standard. Candidate buses for capacitor placement are selected using the sensitivities of constraints and the objective function with respect to reactive power injection at each bus. Using fuzzy set theory, a suitable combination of objective function and constraints is generated as a criterion to select the most suitable bus for capacitor placement. The -cut process is applied at each iteration to guarantee simultaneous improvements of objective function and satisfying given constraints. Simulation results for the 18 bus IEEE distorted network show the advantages of the proposed method as compared to the maximum sensitivities selection algorithm.




Modern distribution system in India is consists of largely 3 phase 11 KV main distribution feeders with 3 phase spur lines and 11/0.4 KV three phase distribution transformers. The distribution system on low voltage side is done by 3 phase 4 wire, 3 phase 5 wire, single phase 3 wire, and single phase 2 wire LT lines. This system involves nearly 2:1 ratio of LV and HV line lengths. Large LT networks results in high occurrence of Lt faults leading to frequent interruptions in supply and high incidence of distribution transformers failure due to LT fault currents.

This system is unsuitable for areas like desert, tribal and forests, where the load density is very low and the development of load in these areas is slow. Heavy capital investment on 3 phase 11KV lines with higher rating 3 phase transformer is not economically justified. To improve the quality of supply, one of the recommendations is the implementation of single phase HT distribution system with small capacity single phase transformers. Under this system, HT line is extended up to or as near the load as possible and to erect small capacity distribution transformers i.e. 10 KV, 16 KVA and to extend supply to the consumer through short length of LT lines, preferably insulated overhead cable system.

Due to use of smaller rating transformers, either 3 phase or single phase length of LT line is considerably reduced and power is distributed mainly through HV (11KV) lines. Distribution system employs a suitable mix of 3 phase nd single phase or 3 phase configuration for giving supply either to small rating lines. With the main line being 3 phase, the spur line comprises of either single phase or 3-phase configuration for giving supply to small rating single phase or three phase distribution transformers.


a) Single phase and one neutral (continous neutral from substation) b) 2 phase 2 wire (rigidly earthed natural system )
c) 3 phase small rating transformers with 3 phase

In case of single phase transformers with phase to neutral system, a continous earthed wire is required to be drawn from 33/11 substation and earth wire is to be earthed at all the poles. The neutral of the distribution transformers is also earthed on HV and LV lines. The voltage on the secondary side of transformer is 0-250V.the single phase transformer can be oil-filled or dry type. The failure of single phase distribution transformers is reported to be less as compared to conventional distribution transformers. Only some group of connections with aerial bunch cables are given and no overloading of distribution transformers occurs.

• Reduction of distribution losses by 75%.
• Negligible transformer failures.
• Excellent voltage profile.
• The HVDS is cost effective to electrify remote villages where bringing of 3 phase lines is costly due to low demands • No additional generation capacity is needed for giving new loads due to reduction in power drawl • In view of less LT system and usage of ABC, which has tough insulating cover, direct tapping by unscrupulous consumers is avoided.



Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and natural genetics. A genetic algorithm is a heuristically guided random search technique that concurrently evaluates thousands of postulated solutions. Biased random selection and mixing of the evaluated searches is then carried out in order to progress towards better solutions. The coding and manipulation of searched data is based upon the operation of genetic DNA and the selection process is derived from Darwin’s survival of the fittest. Search data are usually coded as binary strings called chromosomes, which collectively from populations.

Evaluation is carried out over the whole population and involves the application of, often complex ‘fitness’ functions to the string of values within each chromosome. Typically, mixing involves recombining the data that are held in two chromosomes that are selected from the whole population. Evolutionary computing was introduced in the 1960’s by I.Rechenberg in his work “Evolution strategies”. His idea was then developed by other researchers Genetic Algorithms were invented by John Holland at the University of Michigan. The goals of there researches have been two folds: 1. To abstract and rigorously explain the adaptive processes of natural systems 2. To design the artificial system software that retain important mechanism of natural systems. The central theme of research on Genetic algorithms has been robustness, the balance between efficiency and efficacy necessary for survival in many different environments.

GAs Vs Conventional algorithms:
Genetic algorithms are different from normal optimization and search methods in four ways: 1. GAs work with coding of the parameters set, not the parameters themselves. 2. GAs search from population of points not a single point. 3. GAs use pay off (objective function) information not derivatives or other auxiliary knowledge. 4. GAs use probabilistic transition rules not deterministic rules.

The mechanics of simple genetic algorithm involves nothing more complex than copying strings and swapping partial strings. The strings of artificial genetic systems are analogous to chromosomes in biological systems. Total package of strings is called structure. The structures decode to form particular parameter set, solution alternative or point, which correspond to phenotype. Strings are composed of features or detectors, which take on different values. Features may be located at different position on the string.

The GA is a search algorithm that iteratively transforms a set (called a population) of mathematically objects, each with an associated fitness value, into new population of offspring objects using Darwinian principle of natural selection and using operations such as crossover and mutuation. Algorithm begins with set of solutions (represented by chromosomes) called population. Solution from one population are taken and used to form a new population. This is motivated by hope, that a new population will be better than old one. Solutions which are then selected to form a new solutions are selected according to their fitness, the most suitable they are the more chances they have to reproduce. This is repeated until some condition is satisfied. The space of all feasible solution is called search space. Each point in the search space represents one possible solutions. Each possible solution can be marked by its value (or fitness) for the problem. With GA we look for the best solution among a number of solutions.

The problem is that the search can be very complicated. One may not know where to look for solution or where to start. There are many methods one can use for finding a suitable solution, but these methods do not necessarily provide the best solution. A simple genetic algorithm that yields good results in many practical problems is composed of three operators: 1. Reproduction : This operator is artificial version of natural selection based on Darwinian survival of the first fittest string creatures. Reproduction operator can be implemented in algorithmic form in a number of ways. 2. Crossover : It occurs after reproduction or selection. It creates two new strings or population from two existing ones by genetically recombining randomly chosen parts formed by randomly chosen crossover point. 3. Mutation : It is the occasional random alteration of the value of a string position. Mutation creates a new string by altering value of existing string. Steps in basic genetic algorithm :

1. [Start] Generate random population of n chromosomes (suitable solution for the problem). 2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population. 3. [New population] Create a new population by repeating following steps until the new population is complete. a) [Selection] Select two parent chromosomes from a population according to their fitness. b) [Crossover] With the crossover probability cross over the parents to form a new offspring. If no crossover is performed, offspring is the exact copy of parents. c) [Mutation] With mutation probability mutate offspring at each locus (position in chromosome) d) [Accepting] Place new offspring in the new population. 4. [Replace] Use new generated population for a further run of the algorithm. 5. [Test] If the end condition is satisfied, stop, and return the best solution in current population. 6. [Loop] Go to step 2.



Minimization of losses are important in distribution system as it improves efficiency and voltage profiles and to improve voltage collapse reactive compensation. There are various network configuration techniques for reduction of losses like fuzzy logic method, genetic algorithm, etc. But in my work , i used genetic algorithm to reduce losses in HV distribution system. Because GAs are mainly used in optimization and give outstanding performance, GAs are treated as function optimiser. Here, GAs have been used to found optimum size of capacitor and LSF for number of candidate buses for the placement of capacitors. Previously, this work had been done on 11KV balanced distribution system to improve voltage profiles but my work is to reduce losses in unbalanced distribution system.


The objective of the work is to find the optimal location and size of capacitors to be placed in radial distribution systems have the overall economy using genetic algorithm. Loss sensitivity factors have been used for identifying candidate buses for capacitor placement. The sizes of capacitor have been found using Genetic Algorithm, while optimizing the overall economy calculated considering the energy cost and capacitor cost.

The completion of one research project opens the avenues for work in many other related areas. The following is identified for future work:
a) The same can be extended to 69 bus system.



1. Tanuj Manglani, Y.S.Shishodia. “A Survey of Optimal Capacitor Placement Techniques on Distribution Lines to Reduce Losses”, International Journal of Recent Research and Review, Vol. I, March 2012.

2. J. C. Carlisle and A. A. El-Keib. “A Graph Search Algorithm for Optimal Placement of Fixed and Switched Capacitors on Radial Distribution systems” IEEE transactions on power delivery, Vol. 15, No. 1, January 2000.

3. K. Prakash, member IEEE, and M. Sydulu. “Particle Swarm Optimization Based Capacitor Placement on Radial Distribution Systems”,2007.

4. Abdellatif Hamouda , Khaled Zehar. “Improvement of the Power Transmission of Distribution Feeders by Fixed Capacitor Banks”, Acta Polytechnica Hungarica, Vol. No. 4, No.2 , 2007.

5. M.A.S. Masoum, M. Ladjevardi, M. Sarvi,A.Jafarian, “ Application of Fuzzy Theory and Local Variation Algorithm for Optimal Placement of Capacitor Banks in Distorted Distribution Feeders” Department of Electrical Engineering, Iran University of Science & Technology.

6. Mohamed M. Hamada, Mohamed A. A. Wahab, Abou-Hashema M. El-Sayed, and Husam A. Ramadan. “A New Approach for Capacitor Allocation in Radial Distribution feeders”. The Online Journal on Electronics and Electrical Engineering (OJEEE), Vol. No. 1, No. 1.

7. K.V.S. Ramachandra Murthy, M. Ramalinga Raju, G. Govinda Rao, K. Narasimha Rao. “Comparison of Loss Sensitivity Factor & Index Vector methods in Determining Optimal Capacitor Locations in Agricultural Distribution”, 16th National Power Systems Conference, December, 2010.

8. V.V.K. Reddy, M. Sydulu. “Index and GA based Optimal Location and Sizing of Distribution System Capacitors, 2007.

9. H.M. Khodr, Member IEEE, J.M. Yusta, Member IEEE, Zita Vale, Member IEEE and Carlos Ramos, Member IEEE. “An Efficient Method for Optimal Location and Sizing of Fixed and Switched Shunt Capacitors in Large Distribution Systems” , 2008 IEEE.

10. Sushanta Paul, Student, and Dr. Ward Jewell, Senior Member, IEEE. “Optimal Capacitor Placement and Sizes for Power Loss Reduction using Combined Power Loss Index-Loss Sensitivity Factor and Genetic Algorithm”, 2012 IEEE.

11. Maurizo Delfanti, Gianpietro P. Granelli, Member, IEEE, Paolo Marannino, Senior Member, IEEE, and Mario Montagna, Member, IEEE. “Optimal Capacitor Placement Using Deterministic and Genetic Algorithms”, IEEE Transactions on Power Systems, Vol. 15, No. 3, August 2000.

12. M. A. S. Masoum, A. Jafarian, M. Ladjevardi, E. F. Fuchs, and W. M. Grady, “Fuzzy Approach for Optimal Placement and Sizing of Capacitor Banks in the Presence of Harmonics” IEEE Transactions on Power Systems, Vol. 19, No. 2, April 2004.

1. Neutral network , Fuzzy logic method and Genetic Algorithm by “ G.A Pai”. 2. Power system by “J.B Gupta”.

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