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<p>Digital twin technology is emerging as a key enabler for intelligent power equipment in renewable-dominant power systems. First, this study combines a systematic literature review with framework design to clarify the methodological evolution and practical implementation of digital twins for power equipment. Based on the analysis of 219 publications from 2018 to 2025, three dominant modeling paradigms are identified, including data-driven, physics-based, and hybrid approaches, revealing a transition toward physics-informed intelligence and lifecycle integration. Subsequently, building on the five-dimensional digital twin model, a lifecycle-oriented framework is proposed that unifies physical and virtual interaction, cross-layer data fusion, and staged capability evolution. Compared with existing static architectures, the framework establishes a four-stage evolution path comprising data twin, model twin, twin body, and twin fusion environment, enabling progression from real-time monitoring to environment-aware intelligent control. Furthermore, enabling technologies are systematically aligned with lifecycle stages to support structured implementation. Finally, the analysis indicates that interoperability, interpretability, and real-time adaptability remain major bottlenecks. The proposed framework provides a structured roadmap for actionable lifecycle management in next-generation power systems.</p>

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Digital twin for power equipment: lifecycle framework, enabling technologies, and prospects in next-generation power systems

  • 期刊论文

  • 发表日期：

    2026年4月3日

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  • Dexian Wang,
  • Qilong Liu,
  • Jinghui Yang,
  • Xingye Xu,
  • Xuanyu Chen,
  • Yinfeng Shi,
  • Delin Huang,
  • Yi Wang,
  • Kesheng Wang

摘要

Digital twin technology is emerging as a key enabler for intelligent power equipment in renewable-dominant power systems. First, this study combines a systematic literature review with framework design to clarify the methodological evolution and practical implementation of digital twins for power equipment. Based on the analysis of 219 publications from 2018 to 2025, three dominant modeling paradigms are identified, including data-driven, physics-based, and hybrid approaches, revealing a transition toward physics-informed intelligence and lifecycle integration. Subsequently, building on the five-dimensional digital twin model, a lifecycle-oriented framework is proposed that unifies physical and virtual interaction, cross-layer data fusion, and staged capability evolution. Compared with existing static architectures, the framework establishes a four-stage evolution path comprising data twin, model twin, twin body, and twin fusion environment, enabling progression from real-time monitoring to environment-aware intelligent control. Furthermore, enabling technologies are systematically aligned with lifecycle stages to support structured implementation. Finally, the analysis indicates that interoperability, interpretability, and real-time adaptability remain major bottlenecks. The proposed framework provides a structured roadmap for actionable lifecycle management in next-generation power systems.