Jayabarathi T, Raghunathan T, Mithulananthan N, Cherukuri S H C, Loknath Sai G
School of Electrical Engineering, Vellore Institute of Technology, Vellore, India.
School of Information Technology & Electrical Engineering, The University of Queensland, Brisbane, Australia.
Heliyon. 2024 Feb 17;10(7):e26343. doi: 10.1016/j.heliyon.2024.e26343. eCollection 2024 Apr 15.
This paper presents a comparative study of optimal reconfiguration, distributed generation, and shunt capacitor bank deployment for power loss minimization and voltage profile improvement in distribution systems. A metaheuristic approach based on the grey wolf optimizer (GWO) algorithm has been proposed for solving this high-dimensional, nonlinear, constrained, combinatorial optimization problem. Two standard IEEE 33- and 69-bus radial distribution systems (RDSs), and a practical 83-bus RDS of Taiwan Power Company have been considered for this study. The solutions obtained are compared with one another and those in the recent literature which includes classical and non-classical, metaheuristic-based methods. Going further, the little-studied problem of simultaneous reconfiguration, distributed generation, and capacitor bank deployment has been solved. The results suggest that the GWO has excellent potential for solving complicated optimization problems in distribution systems and elsewhere.
本文针对配电系统中使功率损耗最小化和改善电压分布的最优重构、分布式发电及并联电容器组配置进行了对比研究。提出了一种基于灰狼优化器（GWO）算法的元启发式方法，用于解决此高维、非线性、约束组合优化问题。本研究考虑了两个标准的IEEE 33节点和69节点辐射状配电系统（RDS），以及台湾电力公司一个实际的83节点RDS。将所得解决方案相互比较，并与近期文献中包括经典和非经典基于元启发式方法的解决方案进行比较。进一步地，解决了较少研究的同时进行重构、分布式发电和电容器组配置的问题。结果表明，GWO在解决配电系统及其他领域复杂优化问题方面具有出色潜力。
