This paper proposes a newly chaotic maps integrated stochastic fractal search (CMSFS) for solving the optimal distributed generation placement (ODGP) problem in radial distribution networks. The objective of the problem is to minimize the network real power loss satisfying the operational constraints of distributed generations (DGs) and the network. The proposed CMSFS approach is an improvement of the standard SFS approach by integrating chaotic maps into SFS to enhance its solution quality and convergence rate. The experimental results on the IEEE 33, 69, and 118-bus networks and the power loss reduction percentages with the integration of the optimal number of non-unity power factor DG are 99.21%, 99.43% and 92.36%, respectively. Moreover, the simulation results have also shown that the proposed CMSFS can provide better solution quality than many other methods for the considered scenarios. Therefore, the proposed CMSFS can be an effective alternative approach to the ODGP problems in RDNs.

本文提出了一种新的混沌映射集成随机分形搜索 (CMSFS)，用于解决径向配电网络中的最优分布式发电布局 (ODGP) 问题。该问题的目标是最小化满足分布式发电 (DG) 和网络运行约束的网络实际功率损耗。所提出的 CMSFS 方法是对标准 SFS 方法的改进，通过将混沌映射集成到 SFS 中以提高其解决方案质量和收敛速度。在IEEE 33、69和118总线网络上的实验结果以及整合最优非单位功率因数DG数的功率损耗降低百分比分别为99.21%、99.43%和92.36%。而且，模拟结果还表明，对于所考虑的场景，所提出的 CMSFS 可以提供比许多其他方法更好的解决方案质量。因此，所提出的 CMSFS 可以成为解决 RDN 中 ODGP 问题的有效替代方法。