Swift and precise fault location in distribution networks is paramount for minimising outage losses and expediting power restoration. This paper proposes a hybrid fault location technique based on data and model fusion

which formulates the fault localization task as an optimization problem. The technique tackles three key problems: fault classification

line identification and precise location in distribution networks. The technique hinges on the Grey Wolf Hunting (GWH) algorithm

which has significant advantages in exploring and exploiting capabilities

and the spatial contraction strategy (SCS)

which significantly curtails computational expense. Simulation results demonstrate the proposed technique's commendable performance in recognition accuracy and location precision

alongside robust noise immunity. Meanwhile

this hybird data and model fusion driven technique can be flexibly applied to different distribution networks.