基于改进遗传内点算法的电网多目标无功优化

将遗传算法和内点法相结合求解电力系统无功优化问题。改进了传统的遗传算法，采用混合编码和动态调整选择、交叉、变异算子，并在适应度函数中引入了内点法的对数障碍函数，有效地解决了实际系统的离散变量和状态变量易在边界取得的问题。在无功优化模型中，计及了网损，电压平均偏离，静态电压稳定裕度和调控费用4个指标。IEEE 14和IEEE 57节点算例系统的仿真结果表明，该算法稳定且具有很好的全局寻优能力和较快的收敛速度，能有效提高系统运行的经济性和安全性。

Multi-Objective Reactive Power Optimization Based on Improved Genetic-Interior Point Algorithm

In this paper the genetic algorithm (GA) is integrated with interior point method to solve reactive power optimization of power system. By use of hybrid coding and dynamic regulation of selection, crossover and mutation operators and leading the logarithmic barrier function of interior point method into fitness function, the problems such as variable discretion of actual power system and state variables close to boundary, are effectively solved. In reactive power optimization model, four indices including network loss, average deviation of voltage, stability margin of static voltage and regulation cost are taken into account. Simulation results of IEEE 14-bus system and IEEE 57-bus system show that the proposed algorithm is stable and possesses good global search ability and it converges quickly. Using the proposed method, the economy and security of power system operation can be effectively improved.