Reactive power optimization of distribution network with DG based on improved whale optimization algorithm

Whale optimization algorithm (WOA), as a new artificial intelligence algorithm, has been successfully applied in many fields, but it has many shortcomings in solving reactive power optimization problems. In view of the shortcomings of Whale Optimization Algorithm in dealing with the problem about the optimization of reactive power, such as low convergence accuracy, falling into local optimality easily and converging slowly, this paper improves the basic WOA from four aspects: initial population, inertia weight, convergence factor and spiral update. An Improved Whale Optimization Algorithm (IWOA) is proposed to improve the searching ability and convergence speed of the algorithm. The reactive power Optimization model was established by introducing penalty function to minimize the active power network loss of the system. The simulation was carried out using IEEE-33 nodes, and compared with Whale optimization algorithm, Particle Swarm Optimization (PSO) and Gray Wolf optimization (GWO). The results verify the feasibility and effectiveness of IWOA in addressing the reactive power optimization problem.