基于自适应折射学习和精英搜索SSO算法的电力系统无功优化

Reactive Power Optimization of Power System Based on Adaptive Refraction Learning and Elite Search Social Spider Optimization Algorithm

对电力系统无功优化问题进行研究,提出了一种基于自适应折射学习和精英搜索SSO算法(ARLESSO)的电力系统无功优化方案。针对群居蜘蛛优化(SSO)算法易于陷入局部最优和收敛精度不高的缺陷,引入多功能子族群划分策略:依据蜘蛛个体适应度大小,动态地将蜘蛛种群划分为精英群、扰动群和保持群;精英群和扰动群分别采用精英搜索和自适应折射学习进化机制,以提高算法全局深度搜索能力和种群样本多样性,在此基础上,构建最小网络损耗无功优化模型,并采用ARLESSO算法进行问题求解。IEEE节点测试系统仿真结果表明,同其他无功优化方案相比,所提算法全局寻优能力更强、精度更高,并且能够有效给出电力系统无功优化结果。

The reactive power optimization problem is studied and a kind of reactive power optimization scheme of power system based on adaptive refraction learning and elite search social spider optimization （ARLESSO）algorithm is proposed. As for such defect as not high local optimization and convergence pre- cision by social spider optimization（SSO）algorithm, the multi-function sub ethnic partition strategy is introduced. The spider population is classified dynamically into elite group, disturbance group and mai- ntain group in aceordance with the size of individual fitness of the spider. For elite group and disturbanee group ,the elite search and adaptive refraction learning evolution mechanism are adopted respectively to improve the algorithm＇s global depth search ability and population sample diversity. Based on this, the minimum network loss model for reaetive power optimization is constructed and the ARLESSO is used to solve the problem. It is shown by the IEEE node test system simulation results that the proposed algo- rithm,compared with other reactive power optimization schemes,has stronger global searching ability and higher accuracy,and can give effectively the reactive power optimization result of power system.