The real-time state estimation and safety early warning are the two most important components of the distribution network management of power systems. The conventional strategies cannot satisfy the current grid requirements of efficiency, accuracy, and real-time response. In order to overcome this, this paper utilizes an edge computing-based multi-objective ant colony distributed algorithm to carry out distributed state estimation in distribution networks, which eliminates the computational time and accuracy constraints of conventional state estimation strategies. To ensure safety early warning a fault information matrix is defined to determine faulty sections. Faults on line segments are defined based on predefined threshold conditions, allowing thorough safety monitoring of the distribution network. The combination of the suggested models of state estimation and safety detection forms an IoT-based real-time state estimation and safety early warning system of distribution networks. It continuously estimates states, identifies faults, and sends safety warnings. In its application to check a three-feeder armored buried cable system in one facility, the system correctly determined that the first feeder was the faulty one, which is how it really was. It shows the promising potential of the system applications.
