Solving Reactive Power Optimization Problem Using Genetic Algorithm

This repository contains MATLAB source code that implements a genetic algorithm to solve reactive power optimization problems in electrical power systems. In power systems, reactive power optimization represents a critical challenge that focuses on maximizing system efficiency and stability through strategic adjustment of electrical equipment parameters. The genetic algorithm serves as an effective optimization methodology for addressing this complex problem. The provided MATLAB implementation includes key components such as: - Population initialization with constraint handling - Fitness function evaluation based on power system objectives - Selection operators (roulette wheel or tournament selection) - Crossover and mutation operations with adaptive probabilities - Convergence criteria checking and solution refinement The algorithm follows these implementation steps: 1. Encodes control variables (transformer taps, capacitor banks) into chromosomes 2. Evaluates solutions using power flow calculations 3. Evolves populations through generations while maintaining constraint satisfaction 4. Outputs optimal parameter settings for reactive power compensation devices This codebase provides researchers and engineers with a practical framework for applying evolutionary computation techniques to power system optimization challenges.