Paper Citation Record · LEDGER

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels

As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.29293.

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

pith.paper-citation-record.v1
2607.29293 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:55:03.038567Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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External citation measurements

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Outbound references

Observation 79f44c1e-6ec1-4720-829f-829c36c919a6 · outbound

This paper cites IEEE Std 1366-2022 (Revision of IEEE Std 1366-2012).

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels IEEE Std 1366-2022 (Revision of IEEE Std 1366-2012)

Reference 1

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Observation 7b2c72b9-ce1f-4ce7-a367-1ea3fdf57535 · outbound

This paper cites an unresolved cited work.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Unresolved cited work

Reference 2

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Observation 056aaa9e-4ece-40b0-a5d7-352bfb9a0ffc · outbound

This paper cites Electricity grids and secure energy tran- sitions,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Electricity grids and secure energy tran- sitions,

Reference 3

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Observation fc0c6c61-5a34-43cf-8e3c-01225de1ee6a · outbound

This paper cites Graphgan-based fault detection and location for complex power grids,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Graphgan-based fault detection and location for complex power grids,

Reference 4

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Observation fb541127-cfa7-4eba-90bb-6e57c7eb4c27 · outbound

This paper cites Impact assessment of increasing renewable energy penetration on voltage insta- bility tendencies of power system buses using a QV-based index,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Impact assessment of increasing renewable energy penetration on voltage insta- bility tendencies of power system buses using a QV-based index,

Reference 5

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Observation 0827f145-14c9-43a7-9f33-45f5b2054011 · outbound

This paper cites Review on artificial intelligence-based fault location methods in power distribution net- works,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Review on artificial intelligence-based fault location methods in power distribution net- works,

Reference 6

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Observation 097b72e8-e9d3-4ad4-b2db-d034e996c7ca · outbound

This paper cites An extended impedance- based fault location algorithm in power distribution system with dis- tributed generation using synchrophasors,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels An extended impedance- based fault location algorithm in power distribution system with dis- tributed generation using synchrophasors,

Reference 7

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Observation acd1f018-9fbd-44f3-a20c-e713a5851a66 · outbound

This paper cites A new method for fault location in distribution networks based on voltage sag measurements,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels A new method for fault location in distribution networks based on voltage sag measurements,

Reference 8

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Observation 25330d29-d54a-4de6-b286-748d49fa64af · outbound

This paper cites A novel fault location method based on traveling wave for multi-branch distribution network,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels A novel fault location method based on traveling wave for multi-branch distribution network,

Reference 9

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Observation 39381eb6-34cb-42f2-a8a5-e8d3dbe8e4e8 · outbound

This paper cites Ap- plication of graph neural network for fault location in PV penetrated distribution grids,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Ap- plication of graph neural network for fault location in PV penetrated distribution grids,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T09:55:02.958345Z

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Observation be6fd276-3918-4b01-8869-9ada8a1207b5 · outbound

This paper cites Fault detection, classification and location for transmission lines and distribution systems: a review on the methods,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Fault detection, classification and location for transmission lines and distribution systems: a review on the methods,

Reference 11

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Observation 7c72cc71-36d0-4449-9473-f0139b152fec · outbound

This paper cites Spatial-temporal recurrent graph neural networks for fault diagnostics in power distribution systems,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Spatial-temporal recurrent graph neural networks for fault diagnostics in power distribution systems,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T09:55:02.966775Z

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Observation 822039ff-b77f-4a57-ab54-8a30eb57abeb · outbound

This paper cites Fault location in power distribution systems via deep graph convolutional networks,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Fault location in power distribution systems via deep graph convolutional networks,

Reference 13

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Observation 8fa68218-37dc-46fe-8fb1-58114388aeb8 · outbound

This paper cites The graph neural network model,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels The graph neural network model,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T09:55:02.975306Z

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Observation 363618ab-d580-44fd-b979-222133c581df · outbound

This paper cites A review of graph neural networks and their applications in power systems,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels A review of graph neural networks and their applications in power systems,

Reference 15

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Observation 9d862c89-8e3a-4bae-8ddf-ecf68b041645 · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Semi-supervised classification with graph convolutional networks,

Reference 16

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Observation 2b1d25a2-2c58-4c6a-bbcf-5c868980d26f · outbound

This paper cites Graph attention networks,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Graph attention networks,

Reference 17

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Observation 0f5229a5-3691-41f3-9211-4b9c62c46d55 · outbound

This paper cites Inductive representation learning on large graphs,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Inductive representation learning on large graphs,

Reference 18

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Observation d571cafe-4ddc-40f5-9971-90bde054c73d · outbound

This paper cites An enhanced fault localization technique for distribution networks utilizing cost-sensitive graph neural networks,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels An enhanced fault localization technique for distribution networks utilizing cost-sensitive graph neural networks,

Reference 19

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Observation 41788170-964a-4220-94cb-8774c0ff0673 · outbound

This paper cites Fault location method of distribution network based on VGAE-GraphSAGE,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Fault location method of distribution network based on VGAE-GraphSAGE,

Reference 20

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Observation 645ce622-ca38-4f78-aacb-c3b9a2c429af · outbound

This paper cites Fault diagnosis in active distribution networks with renewable energy using multi-task learning and graph attention networks,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Fault diagnosis in active distribution networks with renewable energy using multi-task learning and graph attention networks,

Reference 21

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Observation 9c822d2c-a211-40df-bad3-27d66f43beda · outbound

This paper cites A GCN-GRU-based framework for voltage sag detection and early warning in distribution networks,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels A GCN-GRU-based framework for voltage sag detection and early warning in distribution networks,

Reference 22

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Observation acd29f30-6a82-4ee4-83c3-07e15fedec3a · outbound

This paper cites Decision combination in multiple classifier systems,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Decision combination in multiple classifier systems,

Reference 23

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Observation 2b5461b6-11c4-4276-8345-f146d088149e · outbound

This paper cites Distributionally Robust Receive Combining.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Distributionally Robust Receive Combining

Reference 24

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Observation d512d8fb-1f78-4599-bcc9-539f3e16f208 · outbound

This paper cites How attentive are graph attention networks?,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels How attentive are graph attention networks?,

Reference 25

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Observation 70eab140-beae-4044-9bcb-701654838ca5 · outbound

This paper cites OpenDSS.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels OpenDSS

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T09:55:03.025829Z

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Observation 7bac7479-ce61-4e6d-b5fe-a484f84565e4 · outbound

This paper cites PyDSS interface.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels PyDSS interface

Reference 27

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Observation 96b5d089-6a36-4d06-b517-6eb5c13b495d · outbound

This paper cites Share of energy consumption from renewable sources in europe,.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Share of energy consumption from renewable sources in europe,

Reference 28

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Observation ffa44965-e210-40ec-aed0-7fd17eef221e · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels Fast Graph Representation Learning with PyTorch Geometric

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T09:55:03.038567Z

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Pith citing papers

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