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Record W4293080906 · doi:10.1049/icp.2022.1229

Energy tapping from modular multilevel converters for improvement of frequency events

2022· article· en· W4293080906 on OpenAlexaff
A. Ng, S. Filizadeh

Bibliographic record

VenueIET conference proceedings. · 2022
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsConvertersEnergy storageInertiaModular designRenewable energyParametric statisticsInertial frame of referenceControl theory (sociology)Automatic frequency controlComputer scienceEngineeringPower (physics)Electrical engineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

The decreasing use of synchronous generators (SGs) by renewable energy sources (RESs) in power systems has led to an increasing vulnerability to frequency excursions. Without the rotating shafts of SGs serving as a reservoir of energy to provide a naturally sufficient inertial response, control systems have been developed and implemented to achieve a similar effect. Other energy reservoirs, such as the inherent energy storage in modular multilevel converters (MMCs), are controlled to help compensate the power imbalances introduced by system perturbations in the grid and improve the inertial response. However, the level of improvement is dependent on the amount of energy available, which is dictated by the MMC and system configurations. In this paper, studies using electromagnetic transient (EMT) simulations are conducted on a parametric basis to investigate how such factors impact the effectiveness of controlling the stored energy to improve the inertial response. These include the energy storage capacity of the MMC, the RES penetration level, magnitude of perturbation, and system inertia. The frequency response characteristics monitored are the frequency nadir and duration of the arresting period. Results show that the available energy does not necessarily improve the inertial response or in a linear manner.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.695

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 science0.0000.000
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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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