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Record W2077148637 · doi:10.1109/tpwrs.2014.2362923

A Novel Adaptive Wide Area PSS Based on Output-Only Modal Analysis

2014· article· en· W2077148637 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Saskatchewan
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaUniversity of Saskatchewan
KeywordsAdaptabilityElectric power systemComputer scienceModalControl theory (sociology)Modal analysisControl engineeringAdaptive controlGridPower (physics)EngineeringControl (management)Mathematics

Abstract

fetched live from OpenAlex

Usage of wide area PSSs (WAPSS) in actual power systems is limited because of their restricted adaptability to different operating conditions. Numerous adaptive control methods have been proposed to solve this problem, but they are limited due to strict prerequisites, such as the necessity of consistent excitations or large disturbances. This paper, therefore, presents a novel adaptive wide area PSS (AWAPSS), which has the ability of tracking system operating condition without the need for meeting the above prerequisites. The inherent dead-band structure of a WAPSS enables a steady system to perform like in an open loop, thus allowing the parameters of a WAPSS to be tuned adaptively through output-only modal analysis. The effectiveness of the novel AWAPSS has been validated in a two-area four-machine system as well as in a large complex system, China Southern Grid.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.205
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it