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

Abstract

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. KeywordsDecision support systems, Planning, Power systems Disciplines Electrical and Computer Engineering | Power and Energy CommentsThis is a manuscript of a proceeding published as Papic, M. Abstract-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 blackout overall structure and some implications for sampling and mitigation

“…This is useful in mitigating the triggers associated with cascading but root cause analysis does not address the causes or mitigation of propagation. However, it is becoming feasible to relate candidate mitigations such as line upgrades to reductions in propagation or large blackout risk [25][26][27]. ... Fig.…”

“…Therefore the mitigation of the initiating events and the propagation differ. For example, clusters of lines that outage together more often during propagation can be identified, but these lines can differ from the lines that more often trigger large blackouts [25,10,26].…”

A Markovian Influence Graph Formed From Utility Line Outage Data to Mitigate Large Cascades

“…the cascade not stopping at the current generation), and cascade size is measured by number of cascade generations. In contrast, some previous papers [7], [12], [13], [34] are structured in terms of the line outages in the generations, so that, according to the branching process model [34], each line outage in each generation propagates independently to form line outages in the next generation. Then the propagation is determined by the number of line outages per line outage in the previous generation, and it is natural to use the total number of lines outaged as a measure of cascade size.…”

“…However, only a subset of cascading mechanisms can be approximated, and simulations are only starting to be benchmarked and validated for estimating blackout risk [5], [6]. Historical outage data can be used to estimate blackout risk [2] and detailed outage data can be used to identify critical lines [7]. However it is clear that proposed mitigation cannot be tested and evaluated with historical data.…”

Can an influence graph driven by outage data determine transmission line upgrades that mitigate cascading blackouts?

“…It is therefore of interest to find the lines most involved in cascade initiation and/or in cascade propagation so that candidate lines to upgrade can be selected. While this can be done directly from historical outage data as suggested in [11], there is a limitation with the historical data that the impact of a proposed mitigation cannot be assessed before it is implemented in reality on the power grid. In this paper we overcome this limitation by showing that a proposed mitigation could be tested using the influence graph.…”