Economic Loss Utilized Probabilistic Defense against Load Redistribution Attacks by Selecting Optimal Critical Measuring Units

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Abstract

State Estimation (SE) reflects the real time operation of power system network in the present cyber-physical power world. However, prior research works depict that bad/false undetectable data can be injected into the system on compromising measuring devices like Remote Terminal Units or Phasor Measurement Units. If an attacker intrudes into the cyber network and injects successful undetectable bad data, then that attack is popular as False Data Injection Attack (FDIA). One practical FDIA is Load Redistribution Attack (LRA), which target bus active power injections and line active power flows. LRA harms SE and subsequently disturbs Security Constrained Economic Dispatch (SCED) which result in severe rise of power generation and load shedding costs. Hence to maintain grid’s security, defense is one of the optimistic options. Attacker or defender won’t have access or control over all units. So, certain critical measuring units must be considered to attack or defend the system. In this research article, a procedure is developed to select optimal critical units subjected to all possible attack resources and load variations. The developed procedure is analyzed on three loading scenarios of modified IEEE-14 bus test system. These critical units are set as basis to find an optimal attack-defense strategy among possible strategies, which is accomplished by static zero-sum game theory in which economic loss is utility. This study provides an in-sight of consequences due to LRA, critical units’ selection under load variations and probabilistic optimal attack-defense strategy of the modified IEEE-14 bus system at three loading conditions. Graphical Abstract: [Figure not available: see fulltext.].

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APA

Kommoju, C. S., & Kotapuri, M. R. (2022). Economic Loss Utilized Probabilistic Defense against Load Redistribution Attacks by Selecting Optimal Critical Measuring Units. Technology and Economics of Smart Grids and Sustainable Energy, 7(1). https://doi.org/10.1007/s40866-022-00131-4

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