Restoration switching analysis with genetic algorithm

摘要

A method for generating switching plans to restore power to out-of-service areas after fault isolation through back feeding. A chromosome architecture is defined to create chromosomes representing candidate post-restoration systems. The chromosomes are evaluated are repeatedly genetically altered until an acceptable solution is identified. The solution identifies a plurality of switching operations that back feed power to the out-of-service areas in the most optimal manner.

主权项

1. A method for determining back-feed paths to one or more out-of-service load areas in a network after fault isolation, the method comprising:
i. defining a chromosome architecture for creating a plurality of unique chromosomes, each said chromosome being a string of characters, each said character representing one of a list of actions for a normally open switch in said network, said list of actions including (1) remaining open, (2) closing, or (3) swapping with a normally closed switch selected from a group of corresponding normally closed switches; ii. initializing a chromosome list; iii. using said chromosome architecture, creating initial chromosomes for a first chromosome population, for each said initial chromosome, if valid and not in said chromosome list, adding said initial chromosome to said initial chromosome population; for each said initial chromosome created, adding said initial chromosome to said chromosome list; v. generating fitness function values for each chromosome in said initial chromosome population; vi. sorting said chromosomes by fitness function value, said chromosome having the lowest fitness function value being the best candidate chromosome; vii. determining if the fitness function value of said best candidate chromosome is below a threshold fitness value, and if so, outputting a network configuration corresponding to said best candidate chromosome; viii. if said fitness function value of said best candidate chromosome is not below said threshold fitness value, using genetic manipulation to create new chromosomes for a new chromosome population, during said creation of said new chromosome population, rejecting any new chromosomes already in said chromosome list and adding said new chromosomes in said new population to said chromosome list; ix. generating fitness function values for each chromosome in said new population; and x. repeating steps vi-ix until the fitness function value of said best candidate chromosome is below said threshold fitness value or until a predetermined number of new populations are created, whereupon a network configuration corresponding to said best candidate chromosome is output.