基于改进并行遗传算法的电网状态估计

鉴于现有电网状态估计算法存在的问题,提出了一种基于改进并行遗传算法的状态估计算法。该算法采用粗粒度模型组织局域网内的计算机进行并行计算,运用迁移策略组织染色个体的并行处理,结合小生境技术保证估计结果的全局收敛性,并根据电网结构和量测量数据特点对染色体编码进行改进。仿真结果表明,该算法提高了状态估计结果的准确性和计算速度,可满足实时处理电网数据的要求。

Power Network Status Estimation Based on Improved Parallel Genetic Algorithm

In vies of existing problems in current power network state estimation, a state estimation algorithm based on improved parallel genetic algorithm is proposed. In this algorithm the computers in the LAN is organized to carry out parallel computation by means of coarse-grained model, the parallel processing of chromosome individual is organized by migration strategy, and the global convergence of estimation results is ensured by niche technology. The improvement of chromosome coding is performed according to power network structure and the feature of measured data. Simulation results show that the proposed algorithm can improve the accuracy of state estimation and speed up the computation, so it can meet the requirement of processing data in real time mode.