Obtaining statistics of cascading line outages spreading in an electric transmission network from standard utility data

We show how to use standard transmission line outage historical data to obtain the network topology in such a way that cascades of line outages can be easily located on the network. Then we obtain statistics quantifying how cascading outages typically spread on the network. Processing real outage data is fundamental for understanding cascading and for evaluating the validity of the many different models and simulations that have been proposed for cascading in power networks.This work was supported in part by NSF grant CPS-1135825. Paper no. TPWRS-01019-2015. We gratefully thank Bonneville Power Administration for making publicly available the outage data that made this paper possible. The analysis and any conclusions are strictly those of the authors and not of Bonneville Power Administratio

An Initial Exploration of Spatial Spreading of Cascading Failure in an Electric Power System

Large blackouts typically involve the cascading outage of transmission lines. However, little is known about the overall patterns of cascading outages. This research processes and initially examines observed utility data to explore the spatial spreading of cascading failure. The utility data is combined from two different sources, one year of recorded transmission line outages and a description of the grid connections. This requires extensive work relating different descriptions of the same grid. An initial analysis of the statistics of the spreading is presented, and the potential and implications of a new statistical approach to cascade spreading is assessed

An Interaction Model for Simulation and Mitigation of Cascading Failures

In this paper the interactions between component failures are quantified and the interaction matrix and interaction network are obtained. The quantified interactions can capture the general propagation patterns of the cascades from utilities or simulation, thus helping to better understand how cascading failures propagate and to identify key links and key components that are crucial for cascading failure propagation. By utilizing these interactions a high-level probabilistic model called interaction model is proposed to study the influence of interactions on cascading failure risk and to support online decision-making. It is much more time efficient to first quantify the interactions between component failures with fewer original cascades from a more detailed cascading failure model and then perform the interaction model simulation than it is to directly simulate a large number of cascades with a more detailed model. Interaction-based mitigation measures are suggested to mitigate cascading failure risk by weakening key links, which can be achieved in real systems by wide area protection such as blocking of some specific protective relays. The proposed interaction quantifying method and interaction model are validated with line outage data generated by the AC OPA cascading simulations on the IEEE 118-bus system.Comment: Accepted by IEEE Transactions on Power System

Comparing a transmission planning study of cascading with historical line outage data

The paper presents an initial comparison of a transmission planning study of cascading outages with a statistical analysis of historical outages. The planning study identifies the most vulnerable places in the Idaho system and outages that lead to cascading and interruption of load. This analysis is based on a number of case scenarios (short-term and long-term) that cover different seasonal and operating conditions. The historical analysis processes Idaho outage data and estimates statistics, using the number of transmission line outages as a measure of the extent of cascading. An initial number of lines outaged can lead to a cascading propagation of further outages. How much line outages propagate is estimated from Idaho Power outage data. Also, the paper discusses some similarities in the results and highlights the different assumptions of the two approaches to cascading failure analysis

Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology

In a cascading power transmission outage, component outages propagate non-locally, after one component outages, the next failure may be very distant, both topologically and geographically. As a result, simple models of topological contagion do not accurately represent the propagation of cascades in power systems. However, cascading power outages do follow patterns, some of which are useful in understanding and reducing blackout risk. This paper describes a method by which the data from many cascading failure simulations can be transformed into a graph-based model of influences that provides actionable information about the many ways that cascades propagate in a particular system. The resulting "influence graph" model is Markovian, in that component outage probabilities depend only on the outages that occurred in the prior generation. To validate the model we compare the distribution of cascade sizes resulting from $n-2$ contingencies in a $2896$ branch test case to cascade sizes in the influence graph. The two distributions are remarkably similar. In addition, we derive an equation with which one can quickly identify modifications to the proposed system that will substantially reduce cascade propagation. With this equation one can quickly identify critical components that can be improved to substantially reduce the risk of large cascading blackouts.Comment: Accepted for publication at the IEEE Transactions on Power System

Can the Markovian influence graph simulate cascading resilience from historical outage data?

It is challenging to simulate the cascading line outages that can follow initial damage to the electric power transmission system from extreme events. Instead of model-based simulation, we propose using a Markovian influence graph driven by historical utility data to sample the cascades. The sampling method encompasses the rare, large cascades that contribute greatly to the blackout risk. This suggested new approach contributes a high-level simulation of cascading line outages that is driven by standard utility data