Paper Citation Record · LEDGER

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection

As of 7 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 2 inbound Pith citation observations for arXiv:2510.00831.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2510.00831 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:22:43.206682Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:22:41.735734Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-04T18:40:02.319838Z

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy0
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8828d7f2-cb8e-4175-a484-fd7b7aac994d · outbound

This paper cites Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:41.735734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2a7c37f-e08d-445a-8e76-1519da6a8a58 · outbound

This paper cites Double Line.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Double Line

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:41.803556Z

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Observation 57f7874c-b245-4d32-839f-7e8b2cbce14f · outbound

This paper cites 2 summarizes the FC results.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection 2 summarizes the FC results

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:41.863045Z

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Observation 684cf130-0ae7-4548-93fa-22832d0377cb · outbound

This paper cites an unresolved cited work.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:41.912366Z

Source-reported events for the cited work

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Observation 12e28f43-5908-4f42-8771-537178f47186 · outbound

This paper cites Pro- tection of Distribution Systems with Distributed Energy Resources,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Pro- tection of Distribution Systems with Distributed Energy Resources,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:41.966584Z

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Observation 070be85b-a7a8-40b0-90e9-150d838b21b4 · outbound

This paper cites Der Zellulare Ansatz – VDE Studie,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Der Zellulare Ansatz – VDE Studie,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.025326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4da47005-6d19-4469-beaa-3ee199b0a37b · outbound

This paper cites Secure and de- pendable protection relay behaviour in extremely high loaded transmission systems,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Secure and de- pendable protection relay behaviour in extremely high loaded transmission systems,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.069755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02b589d7-ff91-4f5f-bbb9-855e4c748e7f · outbound

This paper cites Lewis Blackburn,Protective Relaying: Principles and Applications, F ourth Edition, Taylor & Francis Group, Baton Rouge, 4th ed edition, 2014.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Lewis Blackburn,Protective Relaying: Principles and Applications, F ourth Edition, Taylor & Francis Group, Baton Rouge, 4th ed edition, 2014

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.140049Z

Source-reported events for the cited work

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Observation 15e36adb-0ae1-4902-ada6-d41e841f4392 · outbound

This paper cites an unresolved cited work.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.201627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e951cf64-ba93-45d2-82e1-0fe082447037 · outbound

This paper cites System separation in the Continental Eu- rope Synchronous Area on 8 January 2021 – 2nd up- date,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection System separation in the Continental Eu- rope Synchronous Area on 8 January 2021 – 2nd up- date,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.264164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd532402-0535-421f-8429-309b0c6d51e3 · outbound

This paper cites A hybrid Protection Scheme based on Deep Reinforcement Learning,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection A hybrid Protection Scheme based on Deep Reinforcement Learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.334031Z

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Observation 8387accf-9c4d-48cc-b087-e6b0632f70fb · outbound

This paper cites A novel PRP based deterministic, redundant and re- silient IEC 61850 substation communication architec- ture,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection A novel PRP based deterministic, redundant and re- silient IEC 61850 substation communication architec- ture,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.392123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0a394c8-8772-450d-bc64-89fbd0b6ce97 · outbound

This paper cites Ultrafast Transmission Line Fault De- tection Using a DWT-Based ANN,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Ultrafast Transmission Line Fault De- tection Using a DWT-Based ANN,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.443050Z

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Observation 36e1e895-5a88-4692-9220-a29c790ff672 · outbound

This paper cites Impact of Data Spar- sity on Machine Learning for Fault Detection in Power System Protection,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Impact of Data Spar- sity on Machine Learning for Fault Detection in Power System Protection,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.510937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 349e0316-8e90-491c-bdc8-2fdc7ebb99b1 · outbound

This paper cites A review of machine learning applications in power system protection and emergency control: opportuni- ties, challenges, and future directions,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection A review of machine learning applications in power system protection and emergency control: opportuni- ties, challenges, and future directions,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.545188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06606cb2-9b30-4c0f-aba3-cf50789f183e · outbound

This paper cites A Scoping Review of Machine Learning Applications in Power System Pro- tection and Disturbance Management,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection A Scoping Review of Machine Learning Applications in Power System Pro- tection and Disturbance Management,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.600303Z

Source-reported events for the cited work

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Observation 2e1bc267-fe9a-400d-9e5f-935b3639ab8d · outbound

This paper cites An Intelligent Time-Domain ANN-Based Method for Fault Identification in CSC-HVDC Systems,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection An Intelligent Time-Domain ANN-Based Method for Fault Identification in CSC-HVDC Systems,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.659332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85449824-906e-43c3-a96f-c86a34940000 · outbound

This paper cites Enhancing Fault Detection and Classification in Wind Farm Power Gen- eration Using Convolutional Neural Networks (CNN) by Leveraging LVRT Embedded in Numerical Relays,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Enhancing Fault Detection and Classification in Wind Farm Power Gen- eration Using Convolutional Neural Networks (CNN) by Leveraging LVRT Embedded in Numerical Relays,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.750870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbe840e4-79fb-4896-9264-ac0ab2399629 · outbound

This paper cites A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.819494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86bf9763-980f-432a-abe5-c11ba8766b0c · outbound

This paper cites Fault Location in Three Terminal Transmission Lines Using Artificial Neural Networks,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Fault Location in Three Terminal Transmission Lines Using Artificial Neural Networks,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.876065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22a648f8-6d7d-4b16-9fa7-063d81e85f83 · outbound

This paper cites Integrating ANN and ANFIS for effective Fault Detec- tion and Location in Modern Power Grid,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Integrating ANN and ANFIS for effective Fault Detec- tion and Location in Modern Power Grid,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.933189Z

Source-reported events for the cited work

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Observation a4bf587f-84a7-46f7-9f74-c92d48d4c404 · outbound

This paper cites A System- atic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection A System- atic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:42.987667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17b8401d-3b31-4c07-96d4-f8c8bc203d94 · outbound

This paper cites Hybrid fuzzy evaluation algorithm for power system protection security assessment,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Hybrid fuzzy evaluation algorithm for power system protection security assessment,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:43.053684Z

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Observation 7751892f-5ca1-4606-a83c-08625603f396 · outbound

This paper cites A generic data generation framework for short circuit detection training of neural networks,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection A generic data generation framework for short circuit detection training of neural networks,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:43.124051Z

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Observation 2dd3dcfe-4f60-40b2-b73f-6cfcf93e358a · outbound

This paper cites Dis- tanzschutzalgorithmen,.

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection Dis- tanzschutzalgorithmen,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T13:22:43.206682Z

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Pith citing papers

Observation 8828d7f2-cb8e-4175-a484-fd7b7aac994d · inbound