Application Of Seeker Optimization Algorithm In Optimal Power Flow Problem

With the rapid development of electric power system, the grids become larger and larger. How to guarantee the power system running economically, safely and stably become a focus in the world. In order to realize the optimal distribution of the power flow, different controlling variables are chosen to do the adjustment. The increase of variables and non-linear constraints along with the complicated relationship between them makes the optimal power flow (OPF) problem a complicated, large run mathematical programming one. The swarm intelligence optimization algorithm has the obvious advantages in searching the best solution for the large run, non-linear problem, offering new ways to solve the OPF problem.The traditional OPF always takes the economical run of the electric power system as the destination, and gets the OPF results through adjusting the generator voltages, transformer taps and capacitor tanks. However, the voltage stability is often neglected. In fact, in the condition of the electric power market at present, considering the influence of environmental and economical elements, the power system is apt to run closely to the margin status, resulting in the insufficient voltage stable redundancy. Therefore the voltage collapse will happen sometime. Power losses and voltage stability are both taken into account when creating the OPF model in the thesis. Due to the different dimension and valuation criteria, the normalization was carried out so as to convert the multi-objective optimization problem into a single one.In the thesis, the review of the OPF research was carried out, including summarizing the different objective functions and mathematical models of OPF and comprising the different algorithms used in the OPF solution. Furthermore, seeker optimization algorithm (SOA) is a novel method in the swarm intelligence computation and appears its good performance in some fields, which was introduced in the thesis. The background, mathematical model and the optimization process of SOA were described. And the comparisons with PSO-W (Particle swarm optimizer with inertia weight), PSO-CF (particle swarm optimizer with constriction factor), and CLPSO (Comprehensive learning particle swarm optimizer) were also done. Then the OPF model used in the thesis was established taking the power losses and voltage stability into consideration. The four algorithms were used in optimizing the IEEE30 and IEEE57 standard systems. After the computation and comparison, the conclusion was drawn that seeker optimization algorithm (SOA) features parallel handing and good robustness, which is useful in the non-linear programming problem. Because the searching direction and step length are determined independently, the performance of SOA is better than the other three methods with the excellent optimum search. So it is suitable for the OPF problem.Finally, a summary was described in the thesis. Some suggestions about the OPF study in the future were pointed out.