Improved Immune algorithm for reactive power optimization

Based on the full understanding of the current status of the reactive power optimization study, we propose an improved type of immune algorithm to solve the reactive power optimization problem by introducing the immune clonal selection algorithm (ICSA) genetic manipulation, affinity of mature, cloning and memory mechanism, and use the appropriate operator to ensure that the algorithm can quickly converge to the global optimal solution to improve the efficiency of the algorithm solving and solution accuracy, avoiding the "curse of dimensionality" and precocious problems. ICSA algorithm is proposed to improve the convergence speed simultaneously. Better maintain the diversity of the population. Effectively overcome the premature convergence of evolutionary computation itself is difficult to solve the problem. Four different examples of calculation results show that this method has superior computational efficiency and convergence capability, high quality and are solved, very suitable for solving large-scale power system reactive power optimization problem, with a strong practical value.