Abstract
Research on cascading failures in power-transmission networks requires detailed data on the capacity of individual transmission lines. However, these data are often unavailable to researchers. Consequently, line limits are often modeled by assuming that they are proportional to some average load. However, there is scarce research to support this assumption as being realistic. In this paper, we analyze the proportional loading (PL) approach and compare it to two linear models that use voltage and initial power flow as variables and are trained on the line limits of a real power network that we have access to. We compare these artificial line-limit methods using four tests: the ability to model true line limits, the damage done during an attack, the order in which edges are lost, and accuracy ranking the relative performance of different attack strategies. We find that the linear models are the top-performing method or are close to the top in all the tests we perform. In comparison, the tolerance value that produces the best PL limits changes depending on the test. The PL approach was a particularly poor fit when the line tolerance was less than two, which is the most commonly used value range in cascading failure research. We also find indications that the accuracy of modeling line limits does not indicate how well a model will represent grid collapse. The findings of this paper provide an understanding of the weaknesses of the PL approach and offer an alternative method of line-limit modeling.
Highlights
Power networks are an essential part of modern civilization
The ndings of this paper provide an understanding of the weaknesses of the proportional loading (PL) approach and provide an alternative method of line-limit modeling using linear models
The rst section describes the results of the linear model used to generate line limits, and the second section compares the performance of di erent arti cial line limits to the real line limits
Summary
Power outages caused by either random failure or targeted attack can cascade through the power system and cause massive damage. The most successful cyber-physical attacks were against the Ukrainian power grid in 2015 and 2016 causing a loss of power to 200 000 people.[6,8] Given the potential magnitude scitation.org/journal/cha of cascading failures and the threat posed by a deliberate attack, it is not surprising that researchers are looking for ways to reduce the impact and frequency of such failures. One method of understanding cascading failures is through network science, where cascading failures are stimulated using targeted attacks on network nodes or edges. Substantial work in this regard has focused on developing “vulnerability metrics” or “attack strategies” that identify the order in which nodes should be attacked to cause maximum damage to the power grid
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