SUBSTATIONSUBSTATION DESIGNDESIGNnn PRJ 100PRJ 100nn SAIDI FELIX JUMASAIDI FELIX JUMAnn F17/9366/2002F17/9366/2002

nn SUPERVISOR: DR.CYRUS WEKESASUPERVISOR: DR.CYRUS WEKESAnn EXAMINER:DR. M.K. MANG’OLIEXAMINER:DR. M.K. MANG’OLI

OBJECTIVEOBJECTIVE

nn To design an algorithm that can be To design an algorithm that can be used for planning the location of used for planning the location of distribution substations in a network.distribution substations in a network.

ELECTRICAL SUBSTATION ELECTRICAL SUBSTATION DEFINITIONDEFINITION

nn A subsidiary A subsidiary station of an station of an electricity electricity generation, generation, transmission and transmission and distribution system distribution system where voltage is where voltage is transformed from transformed from one level to one level to another using another using transformers transformers

TYPES OF SUBSTATIONSTYPES OF SUBSTATIONS

nn Transmission SSTransmission SSnn Distribution substation Distribution substation -- transfers transfers

power from the transmission system power from the transmission system to the distribution system of an areato the distribution system of an area

CHOICE OF GACHOICE OF GA

nn Substation location is an optimization Substation location is an optimization problem. As the location varies so do the problem. As the location varies so do the lengths of conductors which immediately lengths of conductors which immediately connect the SS to the network nodesconnect the SS to the network nodes

nn GA is a search technique used in GA is a search technique used in computing to find exact or approximate computing to find exact or approximate solutions to optimization problems. It is an solutions to optimization problems. It is an optimization tool.optimization tool.

GENETIC ALGORITHM WORKINGGENETIC ALGORITHM WORKING

INITIALIZATION – many individual solutions are randomly generated to form an initial population

SELECTION – proportion of initial population chosen to breed a new generation. Individual solutions chosen on a fitness based process. Roulette wheel selection is a common selection method.

REPRODUCTION – crossover and mutation genetic operators are used to create next generation. Average fitness of next generation is better than for previous

TERMINATION – generation process is repeated until a termination condition is reached

CROSSOVERCROSSOVER

MUTATIONMUTATION

SIMPLE GA PSEUDOCODESIMPLE GA PSEUDOCODE

Evaluate the individual fitnesses of the offspring.

Breed new generation through crossover and/or mutation and produce offspring

select best ranking individuals to reproduce

Repeat the steps below until termination

Evaluate the fitness of each individual in the population

Choose initial population

CONSTRAINTS OF PROBLEMCONSTRAINTS OF PROBLEM

nn All load points must be suppliedAll load points must be suppliednn Each load point is supplied by just Each load point is supplied by just

one substationone substationnn A substation can supply more than A substation can supply more than

one loadone load

STRUCTURESTRUCTURE

For optimization of the problem, the solutions are encoded in a matrix structure (CHROMOSOMES)

In these chromosomes the number of rows equals to the number of substations (involving the existing and candidate ones) where En is the number of existing substations and Nn is the number of candidate substations

Chromosome structureChromosome structure

PROCEDURE IN MATLABPROCEDURE IN MATLABnn The creation function (SP_Create) The creation function (SP_Create)

was designed. This creates the first was designed. This creates the first population used by the GA.population used by the GA.

nn Fitness function was created. This Fitness function was created. This assigned a fitness score that’s assigned a fitness score that’s inversely proportional to the inversely proportional to the difference between the solution and difference between the solution and the value a chromosome represents. the value a chromosome represents.

Procedure cont.Procedure cont.

nn A multipoint crossover function and A multipoint crossover function and the mutation function was written to the mutation function was written to carry out the generations.carry out the generations.

nn BehaviourBehaviour of fitness function was of fitness function was checked in the GA TOOL and the M checked in the GA TOOL and the M file (file (SP_mainSP_main) created.) created.

nn This was then made to take in data This was then made to take in data from excel file DATA and also the from excel file DATA and also the output was given in excel file output was given in excel file RESULTSRESULTS

GENETIC ALGORITHM TOOLGENETIC ALGORITHM TOOL

workingworking

nn Program uses DATA.xls as its input. Program uses DATA.xls as its input. It has the following worksheets;It has the following worksheets;

1.1. Input load dataInput load data2.2. Present substations and their Present substations and their

capacitiescapacities3.3. Candidate pointsCandidate points

Inputting dataInputting data

Sample loads and coordinates Sample loads and coordinates

Current substationsCurrent substations

Candidate pointsCandidate points

Running programRunning program

nn Once data has been entered the Once data has been entered the program is run by invoking the program is run by invoking the following in MATLABfollowing in MATLAB

1.1. DSP.figDSP.fig2.2. DSPDSP3.3. SP_mainSP_main

GUIGUI

PROGRESSPROGRESS

PLOT OF SS LOCATION AND PLOT OF SS LOCATION AND CAPACITYCAPACITY

RESULTS FILERESULTS FILE

nn Contains the following worksheetsContains the following worksheets1.1. Transformer capacitiesTransformer capacities2.2. Transformer powersTransformer powers3.3. Substation coordinatesSubstation coordinates4.4. Load (XLoad (X--Y) and substation (XY) and substation (X--Y) Y)

linkslinks

Load and Substation linksLoad and Substation links

CONCLUSION CONCLUSION

nn A genetic algorithm for optimal A genetic algorithm for optimal location of distribution substations location of distribution substations and determination of their locations and determination of their locations was generatedwas generated

RECOMMENDATIONSRECOMMENDATIONS

nn The algorithm developed was The algorithm developed was attaining local optima. Combination attaining local optima. Combination of GA and other optimization of GA and other optimization methods should be explored. This is methods should be explored. This is due to the fact that although GA due to the fact that although GA finds good local solutions, its quite finds good local solutions, its quite inefficient in finding the last inefficient in finding the last mutations to find absolute optimummutations to find absolute optimum

nn Recent research suggests use of Recent research suggests use of more than one parent can yield more than one parent can yield better quality chromosomes. This better quality chromosomes. This should be explored.should be explored.

thanks for your attentionthanks for your attention