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Integration of Power Transformer Maintenance Information with Online Monitoring Data for advanced asset health diagnostics and digital twin modelling.
As increased low carbon generation is connected to the grid, changes in load profile driven by this evolving usage means that traditional approaches to assessing transformer asset health may be out of date. Additional capabilities in terms of data collection, data modelling, and data analysis are needed to assess the full risk to the transformer fleet from fluctuating renewable energy resources and dynamic loading conditions.
This session will share findings from Pacific Gas & Electric's (PG&E) project to implement a condition assessment and prescriptive analytics software for critical power transformers in their transmission and distribution networks. A next generation platform has been implemented to consolidate PG&E transformer maintenance data, loading data, along with online monitoring sensor data, to deliver holistic condition assessment for power transformers and digital twin models that evaluate risk under system operating conditions. With the necessary data channels integrated, the platform automatically delivers a prioritized, fleet-level asset health assessment, detailed diagnoses of degradation in PG&E's transformers, and recommended operational and/or maintenance action to mitigate risk. This platform is quickly becoming an increasingly important planning and operational tool to understand and manage critical assets in order to increase safety, reliability and resiliency across power generation and delivery.
