Security concerns have been raised about cascading
failure risks in evolving power grids. This paper reveals, for the
first time, that the risk of cascading failures can be increased at
low network demand levels when considering security-constrained
generation dispatch. This occurs because critical transmission cor-
ridors become very highly loaded due to the presence of central-
ized generation dispatch, e.g., large thermal plants far from de-
mand centers. This increased cascading risk is revealed in this
work by incorporating security-constrained generation dispatch
into the risk assessment and mitigation of cascading failures. A se-
curity-constrained AC optimal power flow, which considers eco-
nomic functions and security constraints (e.g., network con-
straints, 𝑵 − 𝟏 security, and generation margin), is used to pro-
vide a representative day-ahead operational plan. Cascading fail-
ures are simulated using two simulators, a quasi-steady state DC
power flow model, and a dynamic model incorporating all fre-
quency-related dynamics, to allow for result comparison and ver-
ification. The risk assessment procedure is illustrated using syn-
thetic networks of 200 and 2,000 buses. Further, a novel preventive
mitigation measure is proposed to first identify critical lines, whose
failures are likely to trigger cascading failures, and then to limit
power flow through these critical lines during dispatch. Results
show that shifting power equivalent to 1% of total demand from
critical lines to other lines can reduce cascading risk by up to 80%.
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Power Grid Cascading Failure Prediction Based on Transformer
Smart grids can be vulnerable to attacks and accidents, and any initial failures in smart grids can grow to a large blackout because of cascading failure. Because of the importance of smart grids in modern society, it is crucial to protect them against cascading failures. Simulation of cascading failures can help identify the most vulnerable transmission lines and guide prioritization in protection planning, hence, it is an effective approach to protect smart grids from cascading failures. However, due to the enormous number of ways that the smart grids may fail initially, it is infeasible to simulate cascading failures at a large scale nor identify the most vulnerable lines efficiently. In this paper, we aim at 1) developing a method to run cascading failure simulations at scale and 2) building simplified, diffusion based cascading failure models to support efficient and theoretically bounded identification of most vulnerable lines. The goals are achieved by first constructing a novel connection between cascading failures and natural languages, and then adapting the powerful transformer model in NLP to learn from cascading failure data. Our trained transformer models have good accuracy in predicting the total number of failed lines in a cascade and identifying the most vulnerable lines. We also constructed independent cascade (IC) diffusion models based on the attention matrices of the transformer models, to support efficient vulnerability analysis with performance bounds.
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- Award ID(s):
- 1948550
- NSF-PAR ID:
- 10319642
- Date Published:
- Journal Name:
- Lecture notes in computer science
- Volume:
- 13116
- ISSN:
- 0302-9743
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-91434-9_15
