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Record W4321483943 · doi:10.1109/oajpe.2023.3247243

Evaluation of Inherent Damping Introduced by Thyristor Controlled Series Compensators

2023· article· en· W4321483943 on OpenAlexafffund
D. R. Weerakoon, C. Karawita, U.D. Annakkage

Bibliographic record

VenueIEEE Open Access Journal of Power and Energy · 2023
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Manitoba
FundersMitacs
KeywordsThyristorControl theory (sociology)Electric power systemSIGNAL (programming language)Series (stratigraphy)PhasorStability (learning theory)CapacitorEngineeringComputer sciencePower (physics)PhysicsVoltageElectrical engineering

Abstract

fetched live from OpenAlex

Series compensation of transmission lines increase the risk of Sub-Synchronous Resonance (SSR) problems. Use of Thyristor Controlled Series Compensators (TCSC) in place of Fixed Series Capacitors (FSC) has benefited in terms of damping SSR. SSR Damping with TCSC can be achieved with supplementary controls or through its inherent damping capability. Use of discrete time models for small signal stability analysis of TCSC is well established. This paper uses a simplified Dynamic Phasor (DP) model of the TCSC for an in-depth evaluation of the inherent SSR damping capability through small signal stability analysis. Source of inherent damping in open loop operation and the representation of it in the DP model is elaborated and the small signal model is validated against Electro Magnetic Transient (EMT) simulations. Effect of inductive-resistive nature of the TCSC on Induction Generator Effect (IGE) and Torsional Interactions (TI) is shown through small signal stability assessment. It is shown that the network resonant frequency exists only in a narrow range of firing angles of the TCSC and thus IGE and TI are avoided in most part of its operating range. IEEE first benchmark system for SSR studies is used to demonstrate the inherent damping capability of a TCSC.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.344
Teacher spread0.296 · 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 designBench or experimental
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

Citations7
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Open Access Journal of Power and EnergySame topicHVDC Systems and Fault ProtectionFrench-language works237,207