Abstract：The digital and intelligent upgrading of power equipment is essential for reliable operation, efficient maintenance, and transparent management in modern power systems. With the extensive deployment of sensing devices and information systems, equipment operation increasingly depends on heterogeneous data from electrical, environmental, and structural sources. However, engineering practice still faces difficulties in transforming fragmented multi-source data into coherent operational knowledge. Insufficient data integration, limited representation of dynamic operating scenarios, and weak coupling between analytical results and operational interaction constrain the effectiveness of condition monitoring, fault analysis, and maintenance decision-making. These challenges highlight the need for a systematic reorganization of perception, reasoning, and interaction in digital-intelligent power equipment management. This study investigates the evolution of power equipment operation and maintenance from a hardware- driven paradigm toward a data-driven and cognition-oriented paradigm. The research focuses on three interrelated domains: state perception, situation deduction, and service interaction. State perception is extended beyond isolated measurements to emphasize structured organization and association of multi-source sensing data under complex operating conditions. Situation deduction is examined from the perspective of dynamic scenario representation and causal reasoning, addressing the limitations of static analysis and purely data-driven models in describing evolving operating states. Service interaction is regarded as a knowledge-oriented decision-support process rather than conventional visualization, requiring effective knowledge organization and context-aware interaction. A unified methodological framework is developed to support integrated perception, deduction, and interaction for power equipment operation and maintenance. Multi-source operational data are organized through structured information modeling, enabling consistent mapping among physical equipment, operating states, and spatial structures. Dynamic situation deduction is achieved by combining topology-aware state association with logic-based reasoning, allowing operating conditions and control behaviors to be represented and updated coherently. Service interaction is implemented through knowledge-oriented visualization mechanisms that jointly present operating states, environmental influences, and control responses, supporting operational understanding and maintenance analysis. Engineering implementation and system-level verification are conducted using representative operation and maintenance scenarios. The developed system enables integrated visualization of primary and secondary circuit states, spatial distribution of multi-physical fields, and associated operating conditions. Results indicate that heterogeneous operational data can be consistently fused and associated with equipment topology and spatial structure, supporting coherent state recognition and condition assessment under complex conditions. In addition, a hierarchical maturity evaluation framework and a staged evolutionary roadmap for digital- intelligent construction are established. The maturity framework characterizes the progression from basic data organization to cognition-oriented operation through structured evaluation indicators and weighting mechanisms, providing a quantitative basis for assessing digital-intelligent implementation levels and identifying development priorities in power equipment management.
苏元浩, 沈小军, 毛颖科, 朱正一, 闫全全. 电力设备数智化升级关键技术与发展趋势[J]. 电工技术学报, 2026, 41(20): 7086-7107.
Su Yuanhao, Shen Xiaojun, Mao Yingke, Zhu Zhengyi, Yan Quanquan. Key Technologies and Development Trends of Intelligent and Digital Upgrading for Power Equipment. Transactions of China Electrotechnical Society, 2026, 41(20): 7086-7107.
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2026, Vol. 41 
