Reactive Power Optimization Based on Immune Genetic Algorithm

A modified immune genetic algorithm for power system reactive power optimization is presented. Immune factor has been added to the basic genetic algorithm, which can effectively speed up the convergence. By using the concept of entropy and the expectation of antibody in the selection operation, the algorithm can ensure the population diversity and reduce the possibility of falling into local optimum. The algorithm also uses adaptive crossover rate, mutation rate and the strategy of saving the best individual to accelerate the calculation speed meanwhile maintaining strong local search ability. Finally, the standard IEEE30 bus system simulation results show that the algorithm´s accuracy and convergence are better than genetic algorithm.