MétaCan
Menu
Back to cohort
Record W3048933197 · doi:10.11575/prism/37692

On Verification of Load Models for Frequency Response Using PMU Data

2020· dissertation· en· W3048933197 on OpenAlexfundaboutno aff
Sin Yee Fung

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsnot available
FundersAlberta Electric System Operator
KeywordsComputer science

Abstract

fetched live from OpenAlex

During frequency excursions, not only generators respond to the frequency deviation. Most electric loads are sensitive to large variations in frequency that change their power consumption as a result. Therefore, load modeling is a crucial element in power system simulation, and a better understanding of load response in frequency excursion events is necessary. The main objective of this thesis is to investigate the performance of available load models used in the industry to represent the frequency response of system load elements during frequency excursion events in power system simulations. A dynamic simulation approach is proposed to investigate and evaluate the frequency response of an industrial load simulated with different load models recommended in the industry based on real-life frequency excursions. PMU data collected in Alberta’s grid is used for the scope of studies. Load models explored include CLOD, ZIP and exponential load models. Furthermore, measurement-based load modeling is defined to optimize the load model parameters to improve the accuracy of load response for frequency excursion studies. The performances of these optimized models are compared. The results provide a better understanding of load response and guidelines for choosing load models to be used in frequency excursion studies, to make better judgements in power system analysis and planning in terms of power system security and required balancing resources.

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.

How this classification was reachedexpand

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 categoriesnone
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 score0.690
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.103
GPT teacher head0.344
Teacher spread0.241 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueOpen MINDSame topicVibration and Dynamic AnalysisFrench-language works237,207