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Record W1941081195 · doi:10.1109/pes.2003.1267355

Practical issues in load modeling for voltage stability studies

2004· article· en· W1941081195 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.

Bibliographic record

Venue2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491) · 2004
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsPowertech Labs (Canada)
Fundersnot available
KeywordsComputer scienceStability (learning theory)Electric power systemVoltageInduction motorReliability engineeringComponent (thermodynamics)Power (physics)Control engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Load modeling has become a critical component for the comprehensive and accurate analysis of power systems. In particular, voltage stability limits derived from simulations may be highly influenced by load models used in both static and dynamic analysis. While data for most transmission and generation elements is either well established or can be readily determined from measurement, good load data and models remain difficult to reliably ascertain. This paper discusses the requirements of load models, current approaches, and provides a practical approach to develop models for use in voltage stability analysis. Based on billing data or load inventory surveys, the load is classified into broad types such as residential, industrial, and commercial load and each is subdivided into more specific components including induction motors and other elements. Time-domain simulations are used to establish the load characteristics and general purpose models synthesized from the results for use in static analysis. Examples of this approach are presented in case studies.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.269
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.295
Teacher spread0.259 · 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