Alanazi Mohana, Alanazi Abdulaziz, Almadhor Ahmad, Memon Zulfiqar Ali
Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.
Department of Electrical Engineering, College of Engineering, Northern Border University, Ar'Ar, 73222, Saudi Arabia.
Sci Rep. 2022 Aug 11;12(1):13686. doi: 10.1038/s41598-022-17881-x.
This paper proposes a new intelligent algorithm named improved transient search optimization algorithm (ITSOA) integrated with multiobjective optimization for determining the optimal configuration of an unbalanced distribution network. The conventional transient search optimization algorithm (TSOA) is improved with opposition learning and nonlinearly decreasing strategies for enhancing the convergence to find the global solution and obtain a desirable balance between local and global search. The multiobjective function includes different objectives such as power loss reduction, enhancement of voltage sag and unbalance, and network energy not supplied minimization. The decision variables of the reconfiguration problem including opened switches or identification of optimal network configuration are determined using ITSOA and satisfying operational and radiality constraints. The proposed methodology is implemented on unbalanced 13-bus and 118-bus networks. The results showed that the proposed ITSOA is capable to find the optimal network configuration for enhancing the different objectives in loading conditions. The results cleared the proposed methodology's good effectiveness, especially in power quality and reliability enhancement, without compromising the different objectives. Comparing ITSOA to conventional TSOA, particle swarm optimization (PSO), gray wolf optimization (GWO), bat algorithm (BA), manta ray foraging optimization (MRFO), and ant lion Optimizer (ALO), and previous approaches, it is concluded that ITSOA in improving the different objectives.
本文提出了一种名为改进瞬态搜索优化算法（ITSOA）的新智能算法，该算法集成了多目标优化，用于确定不平衡配电网的最优配置。传统瞬态搜索优化算法（TSOA）通过反向学习和非线性递减策略进行改进，以增强收敛性，从而找到全局解，并在局部搜索和全局搜索之间取得理想的平衡。多目标函数包括不同的目标，如降低功率损耗、增强电压暂降和不平衡以及最小化未供电网络能量。使用ITSOA并满足运行和辐射约束，确定重构问题的决策变量，包括断开的开关或最优网络配置的识别。所提出的方法在不平衡的13节点和118节点网络上实现。结果表明，所提出的ITSOA能够找到最优网络配置，以在负载条件下增强不同目标。结果表明了所提出方法的良好有效性，特别是在提高电能质量和可靠性方面，而不会影响不同目标。将ITSOA与传统TSOA、粒子群优化（PSO）、灰狼优化（GWO）、蝙蝠算法（BA）、蝠鲼觅食优化（MRFO）和蚁狮优化器（ALO）以及先前的方法进行比较，得出结论：ITSOA在改善不同目标方面表现出色。
