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Record W3125136137 · doi:10.1109/tpel.2020.3031342

Aggregated-Impedance-Based Stability Analysis for a Parallel-Converter System Considering the Coupling Effect of Voltage Feedforward Control and Reactive Power Injection

2020· article· en· W3125136137 on OpenAlexaff
Beihua Liang, Jinwei He, Yunwei Li, Peijian Guo, Chengshan Wang

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFeed forwardControl theory (sociology)AC powerOutput impedanceConvertersElectrical impedancePower (physics)VoltageEngineeringElectronic engineeringComputer sciencePhysicsControl engineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

In this article, the sequence impedance of a single converter is separated into a fixed impedance term and an output power dependent term. Then, an aggregated impedance model of identical parallel converters is established to evaluate the stability performance. It is found that the internal power allocation among parallel converters has no impact on the system stability. Further analysis has indicated that the total reactive power injection and the type of voltage feedforward control have highly coupled impact on the system stability. Specifically, increasing the reactive power can always help to stabilize the system when no voltage feedforward loop or voltage feedforward with a low-pass filter is adopted. However, when the direct point of common coupling (PCC) voltage feedforward control is applied to converters, reactive power injection within a certain range can improve system stability. Comprehensive verification results are provided to validate the correctness of conclusion and the reactive power injection approach.

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 score0.842
Threshold uncertainty score0.845

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 head0.006
GPT teacher head0.189
Teacher spread0.184 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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