Distribution automation (DA) and Internet of Things (IoT) all need the topology information of power distribution network to support some applications, such as fault diagnosis, network reconfiguration and optimization. IEC 61850 is a general communication model and standard for information exchange between intelligent electronic devices (IEDs). However, it has no mechanism for feeder topology information exchange. This paper solves this problem by developing the corresponding information model. Firstly, a feeder model is established as a container of the equipment along a distribution line. Secondly, logical models, such as terminal and connection nodes, are added to describe the physical connection relationship between the electrical equipment. Taking a circuit breaker as an example, this paper introduces how to add the terminal attribute to an existing logical node (XCBR). The physical connection between the circuit breaker and other electrical equipment is described by adding the logic node LCNN. Then, a new logical node LTPN is added to describe the logical connection between the devices. A new logical node, FTPA, is added to describe the status of the topology analysis and the topology results. Based on these new logical nodes, this paper proposes the mechanism of topology information exchange between IEDs. Three IEDs and the IEEE 13-node model are used to build an experimental environment. The result verifies the effectiveness of this method. More distributed applications can be used to test the validity and interoperability of the proposed model.

The topology identification of low-voltage distribution networks is an important foundation for the intelligence of low-voltage distribution networks. Its accuracy fundamentally determines the effectiveness of functions such as power system state estimation, operational control, optimization planning, and intelligent electricity consumption. The low-voltage distribution network is composed of transformers, lines, and end users. The key task of topology identification is to distinguish the connection relationship between distribution transformers, low-voltage lines, and phase sequence with end users, which can be divided into transformer user relationship, line user relationship, and phase user relationship. At present, the main methods of low-voltage network topology identification can be divided into signal injection method and data analysis method. The signal injection method requires a large number of additional terminal devices and is difficult to promote. The data analysis method combines the characteristics of switch state, voltage, current, electrical energy, and other data to perform topology analysis. The commonly used methods include correlation analysis and feature learning. Finally, typical problems that urgently need to be solved in topology recognition and representation were proposed, providing a reference for the research and development of low-voltage distribution network topology automatic recognition technology.

Green buildings increasingly couple electrical, thermal, and hydrogen subsystems, yet these assets are typically monitored and controlled through separate standards and protocols. The resulting heterogeneous information models and communication stacks hinder millisecond-level coordination, plug-and-play integration, and resilient operation. To address this gap, we develop a unified information model and a cross-protocol real-time interaction mechanism based on extensions of IEC 61850. At the modeling level, we introduce new logical nodes and standardized data objects that describe electrical, thermal, and hydrogen devices in a single semantic space, supported by a global unit system and knowledge-graph-based semantic checking. At the communication level, we introduce a semantic gateway with adaptive mapping bridges IEC 61850 and legacy building protocols, while fast event messaging and 5G-enabled edge computing support deterministic low-latency control. The approach is validated on a digital-twin platform that couples an RTDS-based multi-energy system with a 5G test network. Experiments show device plug-and-play within 0.8 s, cross-protocol response-time differences below 50 ms, GOOSE latency under 5 ms, and critical-data success rates above 90% at a bit-error rate of 10⁻³. Under grid-fault scenarios, the proposed framework reduces voltage recovery time by about 60% and frequency deviation by about 70%, leading to more than 80% improvement in a composite resilience index compared with a conventional non-unified architecture. These results indicate that the framework provides a practical basis for interoperable, low-carbon, and resilient energy management in green buildings.