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Record W7027349817

Comparison Between Distance Element Method and High Speed Discriminating Method in Loss of Field Protection

2023· dissertation· en· W7027349817 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsnot available
Fundersnot available
KeywordsGenerator (circuit theory)RelayTimerPermanent magnet synchronous generatorTerminal (telecommunication)Electrical impedanceVoltageShunt generator
DOInot available

Abstract

fetched live from OpenAlex

If a synchronous generator loses its field, it will act as an induction generator and it absorbs reactive power from the power system, which might cause damages to the synchronous generator itself. Traditionally, distance relay with zone 1 and zone 2 mho circles are used mostly for synchronous generator loss of field protection. Zone 1 distance element has some delays and zone 2 distance element is instantaneous. The relay measures output current and terminal voltage at the synchronous generator terminal and calculates the internal impedance of the generator by looking into the synchronous generator at its terminal. The relay also stores the distance relay of the zone 1 distance element and zone 2 distance element mho circles and it continuously compares the calculated impedance with the mho circles. Once the calculated impedance falls within the zone 1 mho circle, the relay starts a timer and if the calculated impedance still falls into the zone 1 distance element mho circle after the timer times up, the relay will trip the generator. For the zone 2 distance element mho circle, a similar outcome will be obtained but with the only difference being if the impedance falls within the zone 2 distance element mho circle, the relay will trip the generator instantaneously. In 1979, a new method of synchronous generator loss of field protection was intriduced by D.C. Lee, P. Kundur and R.D. Brown in Ontario Hydro. It uses generator field voltage and terminal voltage as inputs. Once the field voltage reduces to a certain level, a timer starts and if the field voltage does not recover to its health level before the time relay ends, the loss of field protection trip condition 1 will be satisfied. Meanwhile, the generator terminal voltage is monitored as well. If the generator terminal voltage reduces to a certain level, the loss of field protection trip condition 2 will be satisfied. Once both of the two conditions are satisfied, the loss of field relay will trip. This thesis focuses on investigating the required tripping time of these two methods. As renewable energy generation is being called all over the world, this thesis also investigates to see the compatibility of such new high speed discriminating method with a wind farm renewable energy in parallel with the traditional synchronous generator. Following is the contributions of this thesis: Time difference between the traditional mho method and the high speed discriminating method on a single synchronous generator in loss of field protection. Time difference between the traditional mho method and the high speed discriminating method on a single synchronous generator in parallel with a wind farm in loss of field protection. Reactions of both methods when an out-of-step fault occurs in the system right at the joint bus of a synchronous generator and a wind farm. The power system is simulated in PSCAD v 5.0.1 (64-bit) environment. All data is handled and manipulated with Matlab 2022a (64-bit) version.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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