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Record W4408399784 · doi:10.1109/tie.2025.3544195

Systematical Investigation of Transient Response and Fault Clearing Angle Estimation for Delay-Based PLL Inverters During Grid Fault

2025· article· en· W4408399784 on OpenAlexaff
Yantao Zhu, Tianzhi Fang, Zhiheng Lin, Jiayue Lyu, Yaohan Xia

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsPhase-locked loopTransient (computer programming)Fault (geology)Control theory (sociology)Transient responseTransient analysisGridComputer scienceElectronic engineeringEngineeringElectrical engineeringMathematicsGeology

Abstract

fetched live from OpenAlex

For grid-following inverters, the phase-locked loop (PLL) plays a critical role in ensuring transient stability. From the perspective of safe and robust operation, the inverter must remain stable throughout the entire transient process, including during and after grid fault. However, previous research has not provided a systematic and comprehensive analysis of the entire transient response during fault events. This article offers a detailed study of the transient stability of grid-following inverters, combining the equal area criterion with the phase portraits method that accounts for the frequency-dependent impedance. The analysis classifies the system's dynamic behavior into five distinct cases, revealing new phenomena not previously explored, including the identification of an unstable region that emerges after fault recovery, which can lead to system instability. To mitigate this issue, an advanced method for estimating the fault clearing angle is proposed, extending the traditional single-interval approach to multiple intervals, thereby enhancing transient stability during fault recovery. The proposed method and findings are validated through experimental tests on a single-phase grid-connected inverter, confirming their effectiveness and relevance during fault processes.

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 score0.636
Threshold uncertainty score0.767

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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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