Modeling Human Reliability in the Power Grid Environment: an Application of the SPAR-H Methodology

Abstract

Cascading failures, such as the Northeast outage of 2003, can keep millions of people without power for extended periods of time. A combination of simultaneous occurrence of rare events in the physical grid, the human inability to apprehend and act on the real circumstances, and failures in the IT infrastructure result- ing in poor situation awareness may lead to power outages. The formation and propagation of cascading events is nonlinear. There is a precursor phase, in which some system recovery is possible and human oper- ators are able to act and recover failed lines, followed by an escalation phase, in which the number of failed lines increases rapidly, and by a cascading phase that corresponds to the blackout. Although stochastic power grid simulation is an appropriate tool to study cascading events, human intervention, which is mostly ignored in simulation, can nonetheless influence the recovery and the extension of the blackouts. In this pa- per we model the probability of human failure by applying a human reliability modeling approach, the, Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H), to the analysis of failure scenari- os narrated by grid operators with different levels of expertise. The SPAR-H methodology is a simplified approach to human error quantification that accounts for individual performance-shaping factors that affect perception, processing and response to events in complex environments. To implement this approach, we started by interviewing system operators from New England and the South East, and took note of the narra- tives of the emergencies that happened during their shift. Next, we applied the SPAR-H methodology to each task and calculated dependency between the cognitive and action-based tasks. We then calculated the final failure probability and compared the empirical evaluation of error probability with what actually oc- curred. Using Monte Carlo methods and the SPAR-H equations, we finally estimated the HEP that can be readily integrated into the stochastic simulation of a physical power grid. With the intention to calculate the HEP specific to each cascading phase, we propose to increase the number of narrated events and classify each, according to a phase (precursor and escalation). We will then improve the model by calculating the frequency associated with each level of severity for each factor, and by creating a probability distribution for each factor, conditional to a specific phase.