Worth of Loss Vulnerability Hazard an integral part of everyday grid operation. Operators must be aware of grid component health conditions to develop mitigating control strategies, should the risk of component failures start increasing. this leads to a new risk-based framework for online monitoring of assets. the following example illustrates the new online approach to tracking the state of transmission line insulators and developing an operator decision-making framework for acting to mitigate the risk of insulator failures. this approach integrates asset management actions to opti(a) mize design and repair strategies as well. the proposed approach assumes that the deterioration state of a large number of insulators on transmission lines is tracked continuously in both time and location. the suggested spatiotemporal approach helps differentiate the insulators that are deteriorating faster and posing a risk of failure because their operating and environmental conditions create a high hazard. to differentiate declining performance (b) characteristics, the basic insulation level (BiL) is tracked for each insulator and correlated to factors causing the insulator to be vulnerable to failures. an example of the computational framework extracted from an ongoing study at texas a&m University is shown in Figure 7. the goal is to calculate BiL_new using BiL_old by taking into account the historical lightning and weather data that a given insulator has experienced over time. a particular data analytics frame(c) work-gaussian conditional random fields (gcrF) invented Risk 0-20% 20-40% 40-60% by a study partner, Z. Obradovic at temple University-is 60-80% 80-100% used to calculate BiL_new. this is a graph-based calculation paradigm that processes data in each node of the graph associated with a measurement point where data are collected. figure 8. The risk as of (a) 1 January 2009, (b) 31 December the calculation correlates data at each point with the impacts 2014, and (c) January 2015 (prediction). that data at other points may have on the measurements. the nodes where data are measured are shown in yellow. the designation tx_n relates to the n transmission table 1. data used in predictive risk data analytics for outage management on distribution feeders. lines with m towers each. For each of the nodes, a set of variables X is defined, and the Weather Data: Weather Events: gcrF data analytics uses the net* Temperature * Thunderstorm work branches to establish the graph * Precipitation * Hail correlations between the measure* Wind Data * Flash Flood * Humidity * Drought ment nodes where each individual insulator is located. as a result, Historical Outage Data: BiL_new is computed for each * Data and Time insulator at any given moment in * Location Risk Outage Event time, which allows operators to see * Duration Factor the risk of a specific insulator fail* People Affected * Cause Type ing at a particular time. this type of information also instructs the asset management planning group to iniConsequence Measures: tiate work orders that mitigate the Customer Outage * Customer Information situation by replacing all insulators Cost * Outage Cost Model * Outage Location at a high risk of failing. such time-evolving risk maps may be created to track the status 34 ieee power & energy magazine march/april 2018