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Record W2137576359 · doi:10.1109/tpwrd.2014.2365587

Dynamic Phasor Modeling of Type 3 DFIG Wind Generators (Including SSCI Phenomenon) for Short-Circuit Calculations

2014· article· en· W2137576359 on OpenAlexaff
Sriram Chandrasekar, Ramakrishna Gokaraju

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2014
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPhasorControl theory (sociology)Wind powerFault (geology)RelayEngineeringEquivalent circuitInduction generatorComputer scienceControl engineeringElectronic engineeringElectric power systemVoltageElectrical engineeringControl (management)Power (physics)

Abstract

fetched live from OpenAlex

Short-circuit modeling of wind generators is crucial to determine protective relay and control settings, equipment ratings, and to provide data for protection coordination. The short-circuit contribution of a Type 3 wind farm connected to a series-compensated line is affected by subsynchronous interactions, making it essential to model such behavior. Fundamental frequency models are unable to represent the majority of critical wind generator fault characteristics. The complete electromagnetic transient (EMT) models, though accurate, demand high levels of computation and modeling expertise. This paper proposes a novel modeling technique for a Type 3 wind farm based on the generalized averaging theory, where system variables are represented using time-varying Fourier coefficients known as dynamic phasors. The novelty and advantage of the proposed modeling technique is that it does not just include 60-Hz frequencies but also other dominant frequencies, such as 36 Hz, that are present due to the SSCI in the system. Methods currently used by the industry mostly rely on fundamental frequency-based analysis. Only the appropriate dynamic phasors are selected for the required fault behavior to be represented, improving computational efficiency. Once the SSCI behavior (waveforms showing resonant frequency at the point of common coupling) of a series-compensated Type 3 wind farm from real-time field data is available, the developed model could be used to simulate the scenario without necessarily having to know the exact control blocks of the wind generator controls. A 450-MW Type 3 wind farm, consisting of 150 units, was modeled using the proposed approach. The method is shown to be accurate for representing faults at the point of interconnection of the wind farm to the grid for balanced and unbalanced faults as well as for nonfundamental frequency components present in fault currents during subsynchronous interactions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.027
GPT teacher head0.235
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations51
Published2014
Admission routes1
Has abstractyes

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