Fault location in DG-integrated new distribution networks with the Subdomain Multi-Strategy Adaptive Fusion optimization algorithm

As the high penetration of Distributed Generators (DGs), the distribution networks has shifted to a new system with multidirectional power flow and more complex network architecture, posing big challenges to traditional fault location methods, especially when dealing with multi-point faults and information distortion. This paper proposed a Subdomain Multi-Strategy Adaptive Fusion (SMAF) optimization algorithm based on the Weighted Mean of Vectors (INFO), and applied it to the fault location in new distribution networks. In the SMAF algorithm, firstly, the solution space is divided into several non-overlapping subdomains, and chaotic mapping is introduced to enhance the initial population diversity. Secondly, during the iteration, each subdomain adopts a multi-strategy adaptive fusion mechanism based on weighted averaging for individual updates, enhancing the exploration capability of the algorithm. Finally, guided by the payoff comparison mechanism, the subdomains are integrated to achieve subdomain-global parallel updates, thereby improving the global optimization performance and convergence speed. This paper constructs a hierarchical differential fault location model based on the SMAF, and reconstructs the switching function for DGs switching conditions and FTU information distortion. Validation is performed using the IEEE 33-node distribution network simulation model under DGs switching conditions. The results show that, under varying distortion levels, the proposed model can more reliably locate both single-point and multi-point faults, with an average location accuracy exceeding 95%. This demonstrates that the proposed model exhibits superior fault location accuracy and fault tolerance, providing an efficient and reliable solution to the fault location problem in distribution networks with high penetration of DGs.