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pith:XSGITVL3

pith:2026:XSGITVL3VKS7BIADC2WPRGW7I2

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Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids

A.P. Sakis Meliopoulos, Emad Abukhousa, Saman Zonouz

Deep learning models classify power grid anomalies in under 15 ms but require 50 to 90 ms for complete inference.

arxiv:2605.17256 v1 · 2026-05-17 · eess.SY · cs.AI · cs.LG · cs.SY

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Claims

C1strongest claim

All models successfully classified two representative multi-event sequences in real time with sub-cycle response times below 15 ms. However, although classification decisions occurred within one cycle, the end-to-end inference latency consistently exceeded three cycles, ranging from 50 to 90 ms. These results highlight a critical gap between algorithmic capability and protection-grade deployment.

C2weakest assumption

That the high-fidelity, time-domain signals generated from the industry-grade electromagnetic transient simulator accurately represent the behavior of real inverter-dominated power grids under both physical faults and cyber-attacks, allowing the benchmark results to inform real-world deployment decisions.

C3one line summary

Benchmark of eight neural networks on simulated power grid data finds sub-cycle classification but 50-90 ms end-to-end latency, indicating a deployment gap.

References

11 extracted · 11 resolved · 0 Pith anchors

[1] Influence of inverter-based resources on microgrid protection: Part 1: Microgrids in radial distribution systems, 2021

[2] Scpse: Security-oriented cyber-physical state estimation for power grid critical infrastructures, 2012

[3] Dynamic estimation-based protection and hidden failure detection and identification: Inverter-dominated power systems, 2023

[4] Cnn-based transformer model for fault detection in power system networks, 2023

[5] Deep machine learning model-based cyber-attacks detection in smart power systems, 2022

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Receipt and verification

First computed	2026-05-20T00:03:47.970956Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

bc8c89d57baaa5f0a00316acf89adf469f28dcdbd9d8d5be0ed1f098ce6b4b93

Aliases

arxiv: 2605.17256 · arxiv_version: 2605.17256v1 · doi: 10.48550/arxiv.2605.17256 · pith_short_12: XSGITVL3VKS7 · pith_short_16: XSGITVL3VKS7BIAD · pith_short_8: XSGITVL3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/XSGITVL3VKS7BIADC2WPRGW7I2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: bc8c89d57baaa5f0a00316acf89adf469f28dcdbd9d8d5be0ed1f098ce6b4b93

Canonical record JSON

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    "abstract_canon_sha256": "13723de569f66c4603fd56981dd7fbb29ce280960901fa074ccaf1af1e767622",
    "cross_cats_sorted": [
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      "cs.LG",
      "cs.SY"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-05-17T04:57:30Z",
    "title_canon_sha256": "1a865205d968f9d60bea76d84ca749a246e50f00efa0d513b7fda08ac6b9c78a"
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  "source": {
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