Reactive Power Optimization of Power System Based on Intelligent Information Management

With the rapid development of information technology and the continuous improvement of people's living standards, people's demand for electricity is becoming more and more concrete. Intelligent information management can greatly improve the operation efficiency of power system by monitoring various power parameters in the operation of the system. PSO (Particle Swam Optimization) algorithm is a simple and fast convergent evolutionary computation method, but it also has the disadvantages of low convergence accuracy and easy to fall into local extremum. In view of these shortcomings, the original algorithm is improved, and adaptive inertia coefficient and mutation operator are introduced based on intelligent information management. In this paper, a new modified particle swarm optimization (MPSO) algorithm is proposed and applied to reactive power optimization of power system, and the corresponding optimization model is established. At the initial stage of searching, the update of particle position is guided to reduce the randomness of algorithm and improve the searching efficiency. In order to further solve the problem that particles may fall into premature convergence in the later stage of optimization, the chaotic optimization has the characteristic of "strange attractor", and further search in the solution space, and the combination of the two can search for the global optimal solution more effectively. The algorithm specially codes discrete variables, which solves the problem of joint optimization of continuous and discrete variables, reduces network loss and reduces the number of equipment actions. The results of IEEE30-bus system verify the correctness and effectiveness of the proposed algorithm.