Paper Citation Record · LEDGER

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy

As of 19 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2508.19541.

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

pith.paper-citation-record.v1
2508.19541 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:44:37.572316Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

21 of 21 outbound references displayed

verified exact6
verified fuzzy11
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e847fee0-0d92-408a-a285-d7b713dcc328 · outbound

This paper cites an unresolved cited work.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:44:42.357270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6d1aeabb-cdc2-4344-bc0e-c910071a94d6 · outbound

This paper cites A distributed control approach for enhancing smart grid transient stability and resilience,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy A distributed control approach for enhancing smart grid transient stability and resilience,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:42.168683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 933f3cf3-be9c-42ff-961e-d0aa1b914d20 · outbound

This paper cites Improving the stability of an interconnected power system using genetic eigenvalue technique,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Improving the stability of an interconnected power system using genetic eigenvalue technique,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:41.988825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ceaab85a-8ca0-4c28-af59-a4861e07ddd9 · outbound

This paper cites Research on stability of the power system,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Research on stability of the power system,

Reference 4

Resolution: verified exact; doi, observed 2026-08-05T15:44:38.299884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 967a5050-e750-426a-8e7e-0c29a80bb607 · outbound

This paper cites Performance monitoring and redesign of power system stabilizers based on system identification techniques,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Performance monitoring and redesign of power system stabilizers based on system identification techniques,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:41.850262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c0be0be3-1a4b-484b-ba11-527e384fb4d7 · outbound

This paper cites 8. optimized lstm for accurate smart grid stability prediction using a novel optimization algorithm,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy 8. optimized lstm for accurate smart grid stability prediction using a novel optimization algorithm,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:41.674685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0572e3b2-ff77-449e-b8d8-f08aee3e3032 · outbound

This paper cites Machine - Learning Based Prediction of Stability of Smart Grid,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Machine - Learning Based Prediction of Stability of Smart Grid,

Reference 7

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:44:39.808414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91722753-dd60-4ae1-8c9b-7598b5eae269 · outbound

This paper cites Application of Deep Learning and Neural Networks for Smart Grid Stability Predictions,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Application of Deep Learning and Neural Networks for Smart Grid Stability Predictions,

Reference 8

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:44:39.524156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7bd0f680-5132-4b03-9ef4-85f998ade1f6 · outbound

This paper cites Proactive Semi -Supervised Machine Learning Method for Stability Estimation in Smart Grids,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Proactive Semi -Supervised Machine Learning Method for Stability Estimation in Smart Grids,

Reference 9

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:44:39.304695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c5f28931-504d-47b5-9a20-691d9f680862 · outbound

This paper cites an unresolved cited work.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Unresolved cited work

Reference 10

Resolution: verified exact; doi, observed 2026-08-05T15:44:38.051739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b2bf7c5-147b-4e65-bc01-8c5b4a252bce · outbound

This paper cites Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:41.464785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7e998fc2-be77-43c7-9c45-83857908ae13 · outbound

This paper cites Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:44:38.976380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04c1c98e-4648-414e-8795-006145bbed9b · outbound

This paper cites Study on a General Reinforcement Learning Simulation Platform for Regional Power Grid Control Requirements,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Study on a General Reinforcement Learning Simulation Platform for Regional Power Grid Control Requirements,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:41.164813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 46f6a7e2-b41d-4d04-8d43-9a8cff24fa8f · outbound

This paper cites GridLearn: Multiagent reinforcement learning for grid -aware building energy management,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy GridLearn: Multiagent reinforcement learning for grid -aware building energy management,

Reference 14

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:44:38.747592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7d971a9-3f43-4458-9d75-6a154291ffb6 · outbound

This paper cites Electrical Grid Stability Simulated Data ,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Electrical Grid Stability Simulated Data ,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T15:44:37.004571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e496f20-d5d4-49d5-951e-dd9b5be7e94b · outbound

This paper cites A machine learning-based model for stability prediction of decentralized power grid linked with renewable energy resources,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy A machine learning-based model for stability prediction of decentralized power grid linked with renewable energy resources,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:40.929968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f6f92079-e649-4451-b8eb-25c2f68985db · outbound

This paper cites Protection of a smart grid with the detection of cyber-malware attacks using efficient and novel machine learning models,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Protection of a smart grid with the detection of cyber-malware attacks using efficient and novel machine learning models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:40.748122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e1296b0-57be-4f14-87a4-5ca0298a1a95 · outbound

This paper cites Assessment and classification of grid stability with cost -sensitive stacked ensemble classifier,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Assessment and classification of grid stability with cost -sensitive stacked ensemble classifier,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:40.502927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0eab9d11-dfad-4cbf-8fa6-99be663712df · outbound

This paper cites A novel approach to predicting the stability of the smart grid u tilizing mlp-elm technique,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy A novel approach to predicting the stability of the smart grid u tilizing mlp-elm technique,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:40.268211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d35e0fbf-a7ca-4575-965b-9d5ef3c96cce · outbound

This paper cites Per- performance analysis of machine learning -based traditional and ensemble techniques for smart grid stability prediction,.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Per- performance analysis of machine learning -based traditional and ensemble techniques for smart grid stability prediction,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:40.048008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8de69fbe-f8f1-457b-a3c6-42e66ec9d0f4 · outbound

This paper cites Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:44:37.817072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.