Achieving Smart Asset Management
- Written by Siri Varadan
Smart grid efforts have resulted in the creation of huge amounts of data due to the widespread deployment of automated metering, phasor measurement units and other intelligent electronic devices. Using this data to effectively manage assets with the objective of balancing performance, costs and risks in the context of shrinking budgets and tight regulatory requirements is the ultimate goal of smart asset management.
Asset management involves optimizing and prioritizing investments in assets to maintain or improve performance and life expectancy throughout the asset’s life cycle. “Smart Asset Management” uses smart grid data to improve asset management results through the use of back-end integration and data analytics.
Smart Asset Management (SAM) envisions that data from smart grid efforts will be ultimately used for asset management—and that this data will be available to asset management applications in a seamless manner using a common message bus in an integrated environment that leverages data across the utility enterprise.
Smart grid efforts have focused on the deployment of sensors such as intelligence electronic devices, smart meters and PMUs for various purposes including outage management, monitoring, control, protection and self-healing. Data obtained from such sources (operational, or OT data) can be meaningfully combined with data from other sources (information technology, or IT data) to gain additional insights.
To illustrate how smart grid data may be used for distribution asset management, take advanced metering, for instance. Using data from AMI meters associated with a given distribution transformer, it is possible to determine its utilization. Continuous overloading could be a solid basis for asset resizing, or replacement with another transformer of larger rating.
Another example for the use of smart grid data in asset management has to do with the monitoring and trending of partial electrical discharge data, a leading indicator of incipient faults due to insulation breakdown. This data is measured using IEDs over a period of time and enables prediction of incipient cable failures leading to critical decisions regarding replacement of faulty splices or cable sections.
Assuming that the utility has a good understanding of what assets are deployed and where these assets are physically with respect to the topological (electrical) connectivity, asset management aims to answer the following questions: 1) How critical are these assets to power delivery and reliability? 2) What condition are these assets in? 3) Is the performance of these assets satisfactory? 4) If it is not, should action(s) be taken to restore the asset to its original performance or health? 5) If asset performance is satisfactory, how should managers best choose among a diverse set of actions so that corporate objectives are satisfied, including customer satisfaction and regulatory approval?
The fundamental building blocks of asset management that address these questions include ACI or Asset Criticality Indexing (to address asset significance, in the context of network topology where available), ACA or Asset Condition Assessment (to address asset health and performance measures based on a combination of static and dynamic data obtained by on-line monitoring), and AIP or Asset Investment Planning (to address what investments in assets, including spares, are warranted that optimize performance and asset health over the asset lifecycle given cost and regulatory constraints ). Asset Portfolio Management, APM, addresses portfolio management for optimization across the enterprise.
Asset management applications are heavily data driven requiring data from across the enterprise (from the Geographic Information System or GIS, the work management system, the maintenance management system, outage management system, on-line monitoring system(s), meter data management systems, equipment catalogs, standards, planning and archived operational histories). Often such information is lacking altogether or inaccurate. This is because information is siloed within various utility departments, without adequate integration at the backend. Manual data transfers and processes involving copy-paste and single point-to-point integrations make the process of asset management error-prone and in most cases very time consuming.
Smart Asset Management (SAM) provides the opportunity for an integrated package that seamlessly integrates the above building blocks by bringing needed data from across the enterprise through tighter integration. It is accomplished when data created by smart grid efforts and otherwise are readily available to traditional asset management applications. Bridging the data gaps by leveraging an integrated architecture that enables the seamless data flow amongst all required enterprise systems is the future vision for SAM.
With a plug-and-play architecture and access to (near) real-time field data or properly time-stamped data, system operators will have a clear view of asset health (from ACA) and operational risk (posed by asset failure probability from ACI). It is envisioned that this information may be presented to the system operator on a map that combines topology (one line diagrams), geography and additional layers to show crew locations in an intuitive, easy-to-visualize format. ACA and ACI provide a good starting point for off-line applications like AIP and APM, which typically tend to have a long-term outlook (several years) and focus on balancing performance (or reliability, a reasonable proxy for customer satisfaction) with costs and risks over the entire asset lifecycle.
For those utilities that have embarked on their smart grid efforts, two significant paths are evident: on the distribution side with the deployment of AMI and on the transmission side with the deployment of PMUs and IEDs. Such utilities will most likely already have, or be contemplating, an IT architecture that supports data exchanges between applications with varying levels of sophistication. With this background in mind and the assumption that some or all of the basic building blocks of asset management may already exist, the roadmap to SAM consists of enabling the right data pipes.
A high level roadmap includes:
- building a common data model for all asset management applications;
- conducting a data discovery process to identify sources of (smart grid) data required by asset management applications (ACA, ACI, AIP and APM); this process should identify what is needed and what is missing, and should also identify where the required data are available;
- making identified data available on a common message bus or via data extraction for access to asset management applications;
- testing all data flows for required applications in test environments and retesting until all bugs are fixed;
- deploying the hardware and software solution to the production environment.
Smart grid data when collected and analyzed provide a rich resource for asset management, allowing utilities to better understand their deployed assets and manage performance. Smart Asset Management may be realized by providing back-end data integration so that data may be used effectively in building a knowledge-based program. Some of the benefits of implementing SAM include cost savings and a better understanding of risk; asset life cycle and procurement planning; improved performance from existing equipment; a vigorous, defendable, repeatable platform for justification of asset decisions with advanced auditing capability; improved system reliability with increased longevity of asset life by maintaining operation within asset specifications; and compliance with regulations and improved customer satisfaction through better power delivery and reliability.