Research Of Reactive Power Optimization Based On Intelligent Optimization Algorithm

In the modern power system, in order to improve the voltage quality and reduce the power loss, people find a safe and economic solution that is named power system reactive optimization to solve the problem. Power system reactive optimization problem is a theoretical research and practical application value to coexist.The process of Power system reactive optimization is very complex, and it is a constraint nonlinear mixed programming problem that contains many variables (continuous variables and discrete variables). When the grid load is known, the ultimate goal of reactive power optimization problem is to determine the position of transformer tap, the capacity of reactive power compensation and the size of generator terminal voltage. By integrating a genetic algorithm (GA), immune algorithm (IA) with an interior point method (IPM), a hybrid strategy for the optimal reactive power flow (ORPF) problem is proposed in this thesis. First, the original ORPF problem is converted to a continuous non-linear programming problem by relaxing the discrete variables, and the initial solution is obtained by the IPM. Then according to the discrete feature of the control variables, the original ORPF problem is decomposed into a continuous optimization sub-problem and a discrete optimization sub-problem, which are solved by IPM and immune genetic algorithm (IGA) respectively. By solving the two sub-problems alternately, the optimal solution of the ORPF problem can be obtained. In different phases, the implementing of IPM and IGA are dynamically adjusted according to the characteristic of individuals. IPM and IGA take advantage of each other, so that the efficiency of the hybrid strategy is greatly improved.A mixed strategy of the immune genetic algorithm and interior point method for power system reactive power optimization, practical program is made by Matlab language. The proposed algorithm in this paper is applied to the IEEE30-bus system and compares the results of optimization with other paper. The favourable effect can be obtained by using IGA and IPM in reactive optimization that is node voltage control, power loss reduction.All of the results show that the proposed algorithm in this paper has better ability of overall searching optimal solution and higher precision.