In view of the time-driving transmission mode of nodes in the network-contrclled reactive power optimization (RPO) system, an improved multi-objective evolutionary algorithm was proposed. Firstly, the time-driving and event-driving transmission modes of nodes in the network control system (NCS) were analyzed. To deal with the data packet dropout of the time-driving mode, the model of the network-controlled multi-objective RPO system was established. The redundancy-removal and maintaining process was introduced. Secondly, a differential evolutionary (DE) algorithm based on double populations searching scheme was presented. It solved the local optimization problem of the multi-objective optimization. The constraints were processed to the feasible and infeasible solutions populations. The mutation and crossover operations were also modified to improve the speed and performance of optimal searching. Finally, the simulations and analysis were made on IEEE 30-bus system. The results show that the proposed algorithm can make the system to achieve its approximate optima] state. The convergence rate, uniformity and approximation are also greatly much improved.%针对网络化控制无功优化系统中节点为时间驱动的传输模式,提出一种多目标优化的改进进化算法.首先,分析网络化控制系统中节点的时间驱动和事件驱动2种传输模式,并对于时间驱动模式下数据丢包现象,建立网络化控制多目标无功优化系统的数学模型,进而引入去冗一保持处理方法.其次,给出一种基于双群体搜索机制的改进差分进化算法,通过对约束条件的可行和不可行双群体处理,解决多目标优化过程中陷入局部最优的问题,并改进变异和交叉操作以提高优化速度与性能.最后,利用IEEE 30节点系统进行仿真计算及分析.研究结果表明:该算法不仅能保证系统达到次优状态,而且收敛速度、均匀性及逼近性等方面均有较大提高.
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