Digital Twin Architectures for Predictive Maintenance of Electric and Gas Infrastructure
Keywords:
digital twin, predictive maintenance, electric grid, gas infrastructure, condition monitoring, smart grid, distributed energy resources, remaining useful life, Industry 4.0, cyber-physical systems, state estimation, artificial intelligenceAbstract
As plant equipment grows older and increasingly subject to the operational stresses of new distributed energy resources (DERs), pressure on electric and gas utilities is increasing to cut down on unplanned outages and to maximize asset life. The physics-based and data-driven virtual models are coupled with their physical counterparts via continuous bidirectional data propagation, forming digital twin architectures, a candidate framework for predictive maintenance in these areas. This paper presents a collection of 24 peer-reviewed papers published from 2018 to 2021 that discuss digital twin theory, reference architectures and domain applications related to electrical machines, distribution transformers, distributed photovoltaic (PV) systems, smart grid platforms, oil and gas production, and nearby mechanical and structural assets. The synthesis groups the literature into a five-dimensional reference model: physical, virtual, data, connection, and service dimensions, and illustrates how the dimensions are realised differently in the case of electric grid, gas and oil, and general industrial applications. A comparative overview shows that the majority of the studies reviewed focus on distribution level electric applications (eight), mechanical (seven) and predictive-maintenance (seven) topics, followed by general Industry 4.0 framework (five), structural infrastructure (three), and oil and gas applications (one). A field demonstration of a real-time digital twin is provided for a 678-kilowatt (kw) photovoltaic (PV) installation and a four-week industrial retrofitting case study to demonstrate the practicality of the approach. The synthesis also reveals that the 3 most often mentioned barriers to wider adoption are AI integration, cyber security and standardization. Finally, the paper states that digital twin architectures for electric and gas infrastructure are still in a relatively infancy stage of consolidation, and three key challenges across the sector for digital twin remain: data governance spanning the entire lifespan of a digital twin, model fidelity considerations, and the cross-vendor interoperability of digital twin.
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