Paper Citation Record · LEDGER

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data

As of 7 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2509.22795.

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

pith.paper-citation-record.v1
2509.22795 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T13:26:42.535586Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

35 of 35 outbound references displayed

verified exact0
verified fuzzy30
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a953194e-a7aa-46bc-afc9-e2c438eec6bc · outbound

This paper cites Statistical feature extraction is used to classify events from PMU data stream [8].

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Statistical feature extraction is used to classify events from PMU data stream [8]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.714304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f2c69072-3a10-4709-9aa5-0c22ce24074f · outbound

This paper cites Such constraints often result in poor generalization of event classifiers when applied to unseen conditions.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Such constraints often result in poor generalization of event classifiers when applied to unseen conditions

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.746826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bcd347ba-4373-4211-9828-f32bd9b572f9 · outbound

This paper cites The encoder network receives normalized PMU measurements at a 5 -second length 𝑴.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data The encoder network receives normalized PMU measurements at a 5 -second length 𝑴

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.761451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 57219501-40f3-4560-8399-f97de3ee9781 · outbound

This paper cites an unresolved cited work.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:54.963248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b1168585-461a-4b0e-8518-b99fa9331338 · outbound

This paper cites We develop two complementary decision-making strategies: Fig.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data We develop two complementary decision-making strategies: Fig

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.970726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cb93ad1b-c990-413d-ae5d-ac8620191ea1 · outbound

This paper cites an unresolved cited work.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:36:36.753578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 83e0e046-38f4-4956-8baf-be308d174afe · outbound

This paper cites an unresolved cited work.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:36:36.718166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4b150c5f-5c29-44da-971a-cb8dae7479c2 · outbound

This paper cites The scalability of the algorithm is tested on a laptop PC.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data The scalability of the algorithm is tested on a laptop PC

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.729075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 92801b51-5f0f-4677-84e4-f4491b1ee3e0 · outbound

This paper cites Figure 7 illustrates the distribution of individual PMU data segments in the 2D feature space defined by the reconstruction error and discriminator error.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Figure 7 illustrates the distribution of individual PMU data segments in the 2D feature space defined by the reconstruction error and discriminator error

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.721702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 47a0ef7e-afd5-4541-b289-d691bb773778 · outbound

This paper cites The left subplot shows normalized voltage magnitude signals from multiple PMU channels over a 60 -second interval.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data The left subplot shows normalized voltage magnitude signals from multiple PMU channels over a 60 -second interval

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.757520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 63be3eb4-8795-4ca0-9036-eb4ea0086fc8 · outbound

This paper cites The left panel shows the voltage magnitude measurement, where a sharp deviation indicates the presence of an event.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data The left panel shows the voltage magnitude measurement, where a sharp deviation indicates the presence of an event

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.982189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4acdcd2c-542d-4e30-be06-60ae31c532b3 · outbound

This paper cites an unresolved cited work.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:34:54.963544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e718e5ed-cd19-4397-aaf3-028f542fcdd5 · outbound

This paper cites Without the sliding window (top row), the classification accuracy is 81.89%.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Without the sliding window (top row), the classification accuracy is 81.89%

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.736255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 510eb86b-e315-493e-9d48-277577a632b4 · outbound

This paper cites an unresolved cited work.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-05-22T13:36:36.742965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 87530a43-0647-47d6-93f4-30d7a0c3a164 · outbound

This paper cites The 2003 blackout: Solutions that won’t cost a fortune.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data The 2003 blackout: Solutions that won’t cost a fortune

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.750159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9ac2cf85-5949-4ead-9445-78569e153868 · outbound

This paper cites Ice Storm Makes Christmas a Dark Day for Tens of Thousands.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Ice Storm Makes Christmas a Dark Day for Tens of Thousands

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.768053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 923f6c70-9348-4f63-93d3-c70af8463a12 · outbound

This paper cites Wavelet - based event detection method using PMU data.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Wavelet - based event detection method using PMU data

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.710655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 856bf378-1281-4738-82ae-abd8ad4415b5 · outbound

This paper cites Event detection method for the PMUs synchrophasor data.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Event detection method for the PMUs synchrophasor data

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.954853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8b410d32-9741-4bc6-94f4-4a0ae43a5e81 · outbound

This paper cites Dimensionality reduction of synchrophasor data for early event detection: Linearized analysis.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Dimensionality reduction of synchrophasor data for early event detection: Linearized analysis

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.703032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6b42a468-d5f8-4c79-80e9-ca68e12e3a1e · outbound

This paper cites Real time anomaly detection in wide area monitoring of smart grids.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Real time anomaly detection in wide area monitoring of smart grids

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.699295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 712ad516-93c2-457d-b121-1b2a8c79690c · outbound

This paper cites Real -time event identification through low-dimensional subspace characterization of high -dimensional synchrophasor data.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Real -time event identification through low-dimensional subspace characterization of high -dimensional synchrophasor data

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.732615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8ab23271-c662-4662-9268-478953b21de9 · outbound

This paper cites Event detection and its signal characterization in PMU data stream.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Event detection and its signal characterization in PMU data stream

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.739748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 63e04773-6ce3-46cc-aaed-8b05408d6f67 · outbound

This paper cites A novel event detection method using PMU data with high precision.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data A novel event detection method using PMU data with high precision

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.959763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation df03ce76-8f3c-475d-a685-855937a75c6f · outbound

This paper cites Preliminary work to classify the disturbance events recorded by phasor measurement units.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Preliminary work to classify the disturbance events recorded by phasor measurement units

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.966701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0d449140-33f4-455e-aa23-2e8c3095d4ea · outbound

This paper cites Frequency disturbance event detection based on synchrophasors and deep learning.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Frequency disturbance event detection based on synchrophasors and deep learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.706688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 17aa4d78-d243-4574-809e-b1f66c82e364 · outbound

This paper cites PMU-data-driven event classification in power transmission grids.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data PMU-data-driven event classification in power transmission grids

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.958661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f2d78647-439b-42a2-9cd7-c035b009ecc8 · outbound

This paper cites Hierarchical convolutional neural networks for event classification on PMU measurements.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Hierarchical convolutional neural networks for event classification on PMU measurements

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.978533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 775b0c4c-7978-4dd1-9caa-b8160368513c · outbound

This paper cites Learningbased real-time event identification using rich real PMU data.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Learningbased real-time event identification using rich real PMU data

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.952391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 56285338-ee85-45bc-ad62-ad58408e1966 · outbound

This paper cites Big data processing for power grid event detection.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Big data processing for power grid event detection

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.944837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d58ad68f-ab52-4bf2-8995-764cbc5926c4 · outbound

This paper cites Deep-Learning based Multiple Class Events Detection and Classification using Micro -PMU Data.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Deep-Learning based Multiple Class Events Detection and Classification using Micro -PMU Data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.951638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a65c16b4-1850-4a12-8557-0015f70ec6dd · outbound

This paper cites Online power system event detection via bidirectional generative adversarial networks.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Online power system event detection via bidirectional generative adversarial networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.937857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 268e04df-0308-4b11-9902-db278da71503 · outbound

This paper cites Smart grid line event classification using supervised learning over PMU data streams.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Smart grid line event classification using supervised learning over PMU data streams

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.941393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 64640255-4c6d-44c4-8af2-47fcb703cfa8 · outbound

This paper cites Data- driven event detection of power systems based on unequal-interval reduction of PMU data and local outlier factor.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Data- driven event detection of power systems based on unequal-interval reduction of PMU data and local outlier factor

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.764785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 205478f2-0552-4522-a6c1-91f95c3ffab6 · outbound

This paper cites Generative adversarial networks-based synthetic PMU data creation for improved event classification.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Generative adversarial networks-based synthetic PMU data creation for improved event classification

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:34:54.974967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a65a1883-293b-4983-bb1f-bfb56e835f65 · outbound

This paper cites Automated High-Speed Power System Event Detection and Classification Using Synchrophasor Data.

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data Automated High-Speed Power System Event Detection and Classification Using Synchrophasor Data

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:36:36.725431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.