Social-emotional Optimization Algorithm And Its Applied Research In Power System Reactive Power Optimization

Reactive power optimization in power system is an important measure to improve the voltage profile and to reduce the active loss in power. Reactive power optimization is very important to ensure the secure and economy of power system. As the society is developing and the population increase constantly, the load in power system increases dramatically. Therefore, the study in optimal method that can be used to solve the reactive power optimization problem stably is very meaningful.Social emotional optimization algorithm (SEOA) is a novel swarm intelligent population-based optimization algorithm by simulating the human social behaviors. At first, this paper described SEOA in detail: in order to simulate that human can use personal emotion to make decisions precisely, SEOA constructed an emotional gather and a corresponding making-decision gather. In SEOA, the accuracy of individuals'decision can enhance the global searching capability of the algorithm. The algorithm's performance is evaluated on benchmark function. The results show that SEOA do well in the multi-peak and high-dimensional problem.Reactive power optimization is a mixed-variable, multi-constraint and nonlinear planning problem. This paper, using the minimum active loss and voltage deviation as the objective function to construct the mathematical model of reactive power optimization, applied SEOA to solve the reactive power optimization problem. In addition to, the results of the experiments on the IEEE57 bus power system and IEEE118 bus power system proved that using SEOA to solve the reactive power optimization problem is feasible.In order to enhance the global searching capability in SEOA, the paper has improved the algorithm: construct an emotional model to improve emotional set of the SEOA, therefore, the emotional set can simulate the change of the human visually, and the global searching capability is enhanced. The results of the experiments on benchmark function show that SEOA is excellent in the multi-peak and high-dimensional problem. The results of the experiments on the IEEE57 bus power system and IEEE118 bus power system proved that SEOA can solve the reactive power optimization problem effectively.