摘要
针对现有配电网络重构智能算法因编码和求解方法无法保证网络满足辐射状约束而造成的寻优效率低、难以适用于大规模复杂网络的问题,引入了一种被称为"节点的名称-深度-度数"表示法(node-depth_degree representation,NDDR)的数据结构对配电网络进行编码,并基于NDDR编码构建了有明确物理意义的遗传算子.所提出的方法能够保证初始种群及遗传操作生成的所有染色体对应的网络都满足辐射状约束,避免了现有方法为满足网络辐射状约束而需要反复校验和修复网络的问题,计算负担大为减轻.3个经典测试系统和3个大型实际配电系统的测试结果表明:所提方法收敛速度快、稳定性好,能够高概率地得到问题的最优解;与环路编码的遗传算法相比,求解大规模实际复杂网络的计算时间大幅减少,能在短时间内得到高质量的解,具有良好的实用价值.
Aiming at the low efficiency of the complex distribution network reconfiguration, a existing intelligent algorithms in solving the large-scale new data structure, called node-depth-degree representation （NDDR）, was applied to encode power distribution network, and the NDDR-based genetic operators, which have clear physical meaning, were also constructed. The proposed method could guarantee that the corresponding networks of all chromosomes in the initial population and any chromosome generated in the genetic manipulation satisfy radial constraint, which avoids repeated check and repair of network radial constraint in the existing method and significantly reduces the computational burden. Test results of three sample test systems and three large-scale complex distribution systems show that the optimal solution can be obtained with high probability, which means rapid convergence and good stability of the proposed method. Compared to loop encoding-based GA, NDDR-based GA significantly reduces the computation time and obtains high-quality solution in short time while solving large-scale complex distribution network, which indicates good practical value of the proposed method.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2016年第1期234-242,共9页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(51467012
51167012)
江西省研究生创新专项资金(YC2013-S056)
江西省教育厅科技资助项目(GJJ14165)~~
