This repository contains an implementation of a genetic algorithm for multi-objective reconfiguration of balanced and unbalanced distribution systems in a fuzzy framework. The project is based on the concepts described in the paper:
A Genetic Algorithm for Multi-Objective Reconfiguration of Balanced and Unbalanced Distribution Systems in Fuzzy Framework
The project consists of the following modules:
- Chromosome.cs: Defines the representation of a candidate solution (chromosome) that encodes the network configuration, distributed generation (DG) locations, and sizes.
- FuzzyMembership.cs: Implements fuzzy membership functions to fuzzify the objective functions (e.g., power loss minimization, voltage deviation, load balancing) into a normalized range.
- GAReconfiguration.cs: Contains the main genetic algorithm loop. It handles population initialization, evaluation (using fuzzy satisfaction), selection, crossover, and mutation.
- GeneticOperators.cs: Provides implementations for genetic operators such as selection, crossover, and mutation.
- NetworkModel.cs: Simulates the electrical network model and calculates objective values (e.g., power loss, voltage deviation) for a given configuration.
- Program.cs: The entry point that sets up the problem, runs the genetic algorithm, and outputs the optimal network configuration.