Asset health management using predictive and prescriptive analytics for the electric power grid

Abstract

Electric utilities make up an asset-intensive industry with a broad geographical spread of assets, such as poles, transformers, cables, and switchgear. The utilities face a backlog of aging assets that are pending replacement. Increasingly, a consensus has been reached on moving away from time-based maintenance planning of assets to developing a proactive and smarter asset health management program to meet the competing constraints of reducing customer downtimes, meeting regulatory standards, and managing ever-expanding infrastructure within budget. Incomplete information, fragmented data, and a diversity of asset classes collectively make a holistic assessment of the grid extremely challenging. Working with DTE Energy and Alliander N.V., IBM Research has developed advanced analytics to model asset health and network reliability by predicting the aging of assets, identifying the remaining lifecycle, and computing the network robustness. The analytics exploit data from multiple systems such as enterprise asset management, work management, geographic information systems, supervisory control and data acquisition systems, advanced metering infrastructure, weather systems, and outage management systems. The algorithms systematically evaluate asset health and prioritize preventive, proactive, and corrective maintenance strategies for all asset classes in the electrical network. We describe outcomes, summarizing an overall health score and risk ranking along with a suggested optimal maintenance strategy considering budgetary constraints.