arXiv (All)
open access
Machine Learning-Based State Estimation for an Actual Transmission System Using Field PMU Data
Time-synchronized state estimation (SE) plays a critical role in ensuring real-time situational awareness in modern power systems. However, achieving full system observability using phasor measurement units (PMUs) is often impractical due to cost and deployment constraints. Moreover, SE operation at
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Standardized Framework for Machine Learning in Power System Protection#1014806
- A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids#1122751
- Real-Time State Estimation in Smart Grids over 5G Networks: Experimental Validation Using Raspberry Pis and Typhoon HIL#319642
- A Standardized Framework for Machine Learning in Power System Protection#471233
- Data-Assimilation-Assisted Reinforcement Learning for Power Grid Control under Load Uncertainty#1063440
Record · ID 1061204
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive