电网无功优化的改进遗传算法

针对遗传算法在电力系统无功优化实时控制中中速度较慢的问题，提出了一种改进的分段进化遗传算法，改变常规算法固定群体规模和最大迭代次数的做法，将其进化过程分为几个阶段，逐次对其群体规模进行扩充，并规定适应于每个阶段群体规模的迭代次数。这样既可以改善寻优方向，防止过早收敛，又可以保证进化后期每次迭代的有交笥，加快计算速度。在IEEE30节点系统的实验中，与其他常规算法进行对比分析，结果表明分段进化遗传算法具有较强的全局寻优能力，愉的收敛速度，更加适应于实时无功控制。

Improved genetic algorithm for reactive power optimization of electric network

Regarding to the low speed of traditional genetic algorithm (TGA) used in real time reactive power optimization in power systems,an improved algorithm,period evolution genetic algorithm (PEGA), is presented.The population size of the group and the maximum evolution generation are no longer fixed.The evolution process is divided into several periods, the group maximum population is increasing gradually and the maximum generation of each period is set accordingly.It can improve the direction in searching optimal solution,prevent the premature convergence of algorithm,and ensure the efficiency of each alternation during latter evolution periods for increasing the calculation speed.The proposed algorithm is evaluated in an IEEE 30 bus power system,the result shows that PEGA has better global searching ability with faster convergence rate and better performance in real time reactive power control.