基于混合优化算法的配电网动态重构研究

The Research for Dynamic Distribution Network Reconfiguration based on Hybrid Optimization Algorithm

本文提出了一种混合了混沌粒子群与教学优化的算法来解决配电网动态重构问题。建立以网络损耗最小,开关操作次数最少的运行费用模型。将配电网络的损耗和电压偏差这两个指标通过归一化处理形成一个综合指标,并设定最大标准差及系统最大重构次数,确定重构时段。所提出的方法结合了混合粒子群优化算法和教学优化算法的特点成为了一种更有效率的全局优化算法。为了在配电网动态重构中可以动态调整惯性参数,在一般的粒子群算法中引入了混沌理论,同时具有教学优化的混合算法可以保证初始种群的多样性和防止过早收敛,提高了算法寻优的能力。最后使用了IEEE 33节点配电网测试系统证明了所提算法的合理性与有效性。

The proposed approach presents a hybrid algorithm which combines the Chaotic Particle Swarm Optimization and Teaching-Learning Optimization to overcome the Distribution Network Reconfiguration problem. Establish the mathematical model that bases on the minimum cost of operating, the minimum of network loss and the least number of switching operations. Form the comprehensive index including network loss and voltage deviation by the normalized processing.Set the maximum standard deviation of it and the maximum number of system reconstruction, determine the reconstruction period. This approach combines the Chaotic Particle Swarm Optimization and Teaching-Learning Optimization to find the global optima in more efficient way.In order to tune the inertia weight factor dynamically in distribution network reconfiguration, a chaotic framework is introduced to the PSO algorithm. Meanwhile the hybrid algorithm which include Teaching-Learning Optimization can guarantee diversity, limit the initial population premature convergence and improve the ability of the algorithm optimization. Finally, to validate the effectiveness and reasonableness of the proposed algorithm it is applied to IEEE 33 systems.