MétaCan
Menu
Back to cohort

Quadratic-Equation Based Volt-Watt Control Rules Design Strategy to Mitigate Overvoltage in Power Grids with High Penetration of Solar Sources

2024· article· en· W4404103279 on OpenAlexaff
Shafait Ahmed, Tohid Rahimi, Julián Cárdenas-Barrera, Zahid A. Khan, Chris Diduch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsOvervoltagePenetration (warfare)WattVoltElectrical engineeringQuadratic equationControl theory (sociology)Power (physics)Power controlEngineeringComputer scienceControl (management)VoltagePhysicsMathematicsOperations research

Abstract

fetched live from OpenAlex

The over-voltage issue caused by the high penetration of non-controllable PV generators is a significant challenge for power grids, particularly in distribution networks. Various international standards recommend volt-watt curves to address over-voltage issues, but determining set points on those curves is typically left to grid designers. This paper aims to design control rules with a decentralized approach, allowing each PV unit to locally decide its active power curtailment level without involving a central controller for real-time data exchange. The proposed approach uses the sensitivity values of PV inverters to create a quadratic equation curve, ensuring a more equitable contribution of PV inverters in active power curtailment (APC). This method ensures that all PV inverters enter the shut-down mode concurrently, promoting fairness in shut-down scenarios. The IEEE 37 Bus system was selected to evaluate the proposed approach, yielding positive results. The proposed method is straightforward to implement and effective in facilitating fair APC. The results demonstrate more than 30% improvement during peak PV generation times.

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 score0.640
Threshold uncertainty score0.683

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 head0.011
GPT teacher head0.192
Teacher spread0.182 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicSmart Grid Energy ManagementFrench-language works237,207