Modified Particle Swarm Optimization Algorithm Applied In Power System Reactive Power Optimization

Rational distribution of the power system reactive power ensures the voltage quality and reduces the power loss, which is significant to the security, stability and economic operation of the entire power system. Reactive power optimization is a hybrid optimization problem with continuous and discrete variables, which is nonlinear with multiple constraints and high dimensions. The traditional optimization algorithms treat the discrete variables as continuous variables approximately, which leads to a certain error. Besides, the approximate model is complicated to solve, that reduces the computational efficiency. In recent years, with their simplicity and robustness, intelligent algorithms get a lot of attention and research. Meanwhile, they have been used in the area of power system reactive power optimization.Particle swarm optimization (PSO) is one of the intelligent algorithms with superior performance, which has many advantages such as:high convergence speed, high precision and easy to implement. However, when dealing with the actual multidimensional problems such as the optimization of reactive power, convergence speed and accuracy can not be guaranteed. Given this background, against the characteristics of the reactive power optimization problem, the minimization of the active power loss is chosen as the objective function in this thesis, and the traditional PSO is improved. At first, on the basis of the linear decreasing weight PSO (LDW-PSO), a self-adaptive weight chaos PSO (SAW-CPSO) is proposed. Linear decreasing inertia weight is replaced by adaptive inertia weight, in order to increase convergence speed. Chaos mechanism is introduced to improve the performance, judging premature. Cubic chaos is used for population initialization, increasing the population diversity and avoiding premature. Secondly, the existing topologies of the PSO are introduced and analyzed. Based on the ring topology, a new PSO algorithm with dynamic topology (D-PSO) is proposed. This method uses clustering methods in the initial stage to accelerate the convergence speed; based on the convergence states of the particle clusters, a neighborhood dynamic adjustment mechanism is carried out; in the later iteration, a ring topology is formed to improve the accuracy of the algorithm and the global search ability of the algorithm.Finally, in Matlab software platform, the proposed algorithms are applied to solve the reactive power optimization problem, using IEEE-14and IEEE-30systems. Optimization results show that the proposed methods are feasible and effective in dealing with the reactive power optimization problem.