Parallel Domain Decomposition Based Distributed State Estimation for Large-scale Power Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Growing system sizes and complexity, along with the large amount of data provided by phasor measurement units (PMUs), are the drivers to accurate state estimation algorithms for online monitoring and operation of power systems. In this paper, a distributed weighted-least-square state estimation method using an additive Schwarz domain decomposition technique is proposed to reduce the computational execution time. The proposed approach divides a data set into several subsets to be processed in parallel using a multiprocessor architecture considering data exchange among distributed areas. The slow coherency method and balanced partitioning are utilized to reduce the communication overhead and increase accuracy. Moreover, bad data analysis is also investigated in a distributed manner. The performance of the proposed distributed state estimator, along with the speed-up for several test systems, was compared with the traditional centralized state estimator. The simulation results show a speed-up of 6.5 for a 4992-bus system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it