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Frequency Scanning based Design of Supplementary Damping Controllers for Inverter-Based Resources

2024· article· en· W4403126091 on OpenAlexaff
Kaustav Dey, A. M. Kulkarni, A.M. Gole

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInverterComputer scienceElectronic engineeringMaterials scienceControl theory (sociology)EngineeringElectrical engineeringControl (management)Voltage

Abstract

fetched live from OpenAlex

Adverse controller interactions involving IBRs are a major concern for power system engineers. IBR controllers are not standardized and are proprietary, making it difficult to study and mitigate these interactions. In this scenario, having a supplementary damping controller that modulates an available set point of the existing controller could be a pragmatic solution. There is a vast and positive experience in designing stabilizers for conventional synchronous generators using standardized control structures like washout and lead-lag blocks. The aim is to have similar, straightforward, and easily implementable damping controllers for the emerging IBR based networks. This paper presents an investigation into this issue using a three-IBR system example. For certain controller parameters, the system exhibits poorly damped low frequency oscillations between the IBRs. A simple supplementary damping controller structure using a local feedback signal is found to be feasible for mitigating the IBR oscillations. The frequency scanning approach which is well-suited for black-box system identification is found to be an effective tool for supplementary damping controller design.

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 score0.923
Threshold uncertainty score0.794

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 head0.019
GPT teacher head0.229
Teacher spread0.210 · 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
Published2024
Admission routes1
Has abstractyes

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