Big Data Uses for Risk Assessment in Predictive Outage and Asset Management

Allowing better estimates of the risk associated with outages and asset failures is an ultimate goal since the cost of service interruption due to forced or planned outage can be substantial. This paper address how the risk calculation is improved by the use of Big Data related to weather and other environmental impacts that may lead to outages and asset deterioration. Big Data considered in this paper relates to various weather data services such as radar, satellite, land stations, specialized tracking systems (lightning), etc., as well as vegetation and topography. In addition, traditional utility measurement data coming from Intelligent Electronic Devices located in substations and along the feeders are integrated to get better assessment of the risk. Developing a framework that ties the data and physical network components together in time and space to obtain better predictions is an emerging area of research. Our innovative study opens several opportunities to address unique fundamental issues of how to effectively fuse weather and network data in time and space for the benefit of predicting likelihood of power network outages under severe weather conditions, which will lead to better maintenance and operation strategies. The results are obtained using advanced predictive data analytics approach that incorporates spatiotemporal properties of the Big Data. To better visualize the risk assessment results, a Geographic Information System view of the power system components is offered. This view allows operators to immediately assess the risk in the face of unfolding weather conditions and take actions accordingly. The benefits of using Big Data to achieve such decision-making capability is contrasted with the legacy solutions and their mostly after-the-fact reactive actions. The Big Data utilization is part of a larger effort aimed at introducing various advantages to the utility industry based on the advanced data analytics. Several examples from the recent work done for the utility industry are given, using a real‐ life representation of their system and the associated events.