SUBSTATION DESIGN PRJ 100 SAIDI FELIX JUMA F17/9366/2002 SUPERVISOR: DR.CYRUS WEKESA EXAMINER:DR. M.K. MANG OLI
OBJECTIVE To design an algorithm that can be used for planning the location of distribution substations in a network.
ELECTRICAL SUBSTATION DEFINITION A subsidiary station of an electricity generation, transmission and distribution system where voltage is transformed from one level to another using transformers
TYPES OF SUBSTATIONS Transmission SS Distribution substation - transfers power from the transmission system to the distribution system of an area
CHOICE OF GA Substation location is an optimization problem. As the location varies so do the lengths of conductors which immediately connect the SS to the network nodes GA is a search technique used in computing to find exact or approximate solutions to optimization problems. It is an optimization tool.
GENETIC 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
CROSSOVER
MUTATION
SIMPLE GA PSEUDOCODE Choose initial population Evaluate the fitness of each individual in the population Repeat the steps below until termination select best ranking individuals to reproduce Breed new generation through crossover and/or mutation and produce offspring Evaluate the individual fitnesses of the offspring.
CONSTRAINTS OF PROBLEM All load points must be supplied Each load point is supplied by just one substation A substation can supply more than one load
STRUCTURE 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 structure
PROCEDURE IN MATLAB The creation function (SP_Create) was designed. This creates the first population used by the GA. Fitness function was created. This assigned a fitness score that s inversely proportional to the difference between the solution and the value a chromosome represents.
Procedure cont. A multipoint crossover function and the mutation function was written to carry out the generations. Behaviour of fitness function was checked in the GA TOOL and the M file (SP_main) created. This was then made to take in data from excel file DATA and also the output was given in excel file RESULTS
GENETIC ALGORITHM TOOL
working Program uses DATA.xls as its input. It has the following worksheets; 1. Input load data 2. Present substations and their capacities 3. Candidate points
Inputting data
Sample loads and coordinates
Current substations
Candidate points
Running program Once data has been entered the program is run by invoking the following in MATLAB 1. DSP.fig 2. DSP 3. SP_main
GUI
PROGRESS
PLOT OF SS LOCATION AND CAPACITY
RESULTS FILE Contains the following worksheets 1. Transformer capacities 2. Transformer powers 3. Substation coordinates 4. Load (X-Y) and substation (X-Y) links
Load and Substation links
CONCLUSION A genetic algorithm for optimal location of distribution substations and determination of their locations was generated
RECOMMENDATIONS The algorithm developed was attaining local optima. Combination of GA and other optimization methods should be explored. This is due to the fact that although GA finds good local solutions, its quite inefficient in finding the last mutations to find absolute optimum
Recent research suggests use of more than one parent can yield better quality chromosomes. This should be explored.
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