in association with

Gowri Rajappan and Tony McGrail, Doble Company strategy, Energy networks, Network Excellence, Strategy & management, Technology, Opinion

The foundations of emerging asset management applications are data and analytics; the aim being to provide full lifecycle management of assets in a justifiable and auditable manner.

To enable this, all pertinent data need to be accurately maintained and available on demand to various applications.

One approach to achieving this aim is implementing a standards-based data architecture which enables current analytic applications and futureproofs developments to come.

While gaining value from data analytics is the ultimate goal, experience shows the first challenge is sanitising the assorted data sets. Data ‘hygiene’ is vital to ensure there is a single ‘record of truth’ and data are available in a uniform and structured manner.

Just like an iceberg, data hygiene is the 80 per cent of the initial effort hidden from view, the remainder is the visible new tools and data structures. Without this solid foundation the advanced analytics will yield poor results.

Managing data hygiene in line with the Common Information Model (CIM), permits the removal of redundant entries, misspellings and allows for correct mappings between the asset-based data and the network location/node data which are often in independent databases. This applies not only within an organisation, but also allows data exchange across multiple organisations. Adding an IEC 61850-based protocol gateway allows data from multiple applications, such as SCADA, DER, protection and control, and condition monitoring to be merged and made available across the organisation for asset assessment, maintenance planning, R&D etc.

Enabled by improved communication technology, asset owners are increasingly inundated with data as more assets have more sensors and more condition monitors. Those organisations which can apply standards such as CIM and IEC 61850, within an ISO 55000 framework, are able to leverage advanced analytics and thus make better operational and investment decisions.

Practical examples include a large US utility using standards to develop a sustainable funding model for long-term support and management, while another utility successfully developed a seamless mobile workforce, outage and customer management system to support day-to-day operations across multiple state jurisdictions.

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