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Written by Al Thomas
on 20 Jan 2020

Grid modernization programs currently in progress across electric utilities worldwide are driving a dramatic shift toward ADMS and grid analytics. Many utilities are developing new capabilities, and are introducing new business models to meet evolving business dynamics by harnessing and combining operational and grid management systems data with GIS and ERP to create “digital twins” of their distribution networks. Digital twins unite both the physical geospatial representation and as-is operational states of electric grids in real time to visualize data and monitor systems. These powerful new capabilities allow utilities to optimize performance, enhance safety, and prevent outages by creating virtual models that accurately mirror the characteristics and behaviors of electric networks.

As the foundation of the physical network representation in a digital twin, utilities must now maintain an unprecedented level of GIS data accuracy, quality, and latency to match real-time data. Unfortunately, most existing utility GIS systems were not designed and to provide up-to-date, high-fidelity data. As a result, many utilities have embarked on enterprise GIS data quality assessment and improvement programs to implement new processes to build, validate, correct, maintain, and govern source data from GIS and real-time systems to fully realize the benefits of ADMS and digital twins.

At the same time, data is growing exponentially, but its quality and accuracy are often suspect. Poor data governance practices and expensive labor and tools required to ensure premium data quality are costing utilities dearly. Many are spending anywhere between $20 million and $50 million to obtain essential data that drives new operations systems such as ADMS and build digital twins of electric networks. Clearly, utilities will reap big rewards through focused efforts to understand, address, and maintain GIS and operational data quality programs across the enterprise.

Cyient’s experience with data shows we have been involved in every facet of utility network data creation, transformation, and management for decades—from field data collection to automated data quality measurement and improvement. We leverage purpose-built solutions combining recursive machine learning, voltage signature analysis, and artificial intelligence with business rules engines to identify errors and missing data, resolve common problems such as transformer to meter connectivity, phasing, and other issues, as well as, providing insights for load, voltage, DER, and outage management.

As grid complexity continues to increase, the need for data that is readily available and accurate is critical for the management of distributed energy resources (DER). Cyient’s intelligent Data Management Solution (iDMS) enables utilities to assess and validate GIS data against other relevant systems and govern data quality for Advanced Data Management System (ADMS) readiness and digital twin management. It helps increase data accuracy to predict outages better and improve fault identification. More importantly, iDMS turns underutilized capital investments into productive resources by leveraging smart meter data to automate network data validation. Our solution is technology-agnostic, supporting all commercial off-the-shelf products and platforms through published APIs.

To learn more about iDMS and how we can help you with digital twins, join us at DistribuTECH 2020.

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