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Record W3048426338

An impedance frequency response approach for analyzing interactions in ac-dc systems

2019· dissertation· en· W3048426338 on OpenAlexfundno aff
Yi Qi

Bibliographic record

VenueMspace (University of Manitoba) · 2019
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMitacsManitoba Hydro
KeywordsElectrical impedanceElectrical engineeringComputer scienceElectronic engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

This thesis presents an integrated technique for screening the interaction level between the ac side system and dc side converters. The system resonances in the sub-synchronous and super-synchronous frequency range are identified and stability determined. This approach applies the Generalized Nyquist stability Criterion (GNC) assuming that the frequency dependent impedance responses are available. The frequency responses of the subsystems are obtained using two different techniques. One is an analytical state variable analysis requiring the mathematical model of the system elements; and the other is a simulation-based frequency scanning approach which does not rely on detailed knowledge of the internal system. These two techniques can be applied in different situations. The state variable analysis is useful when a small signal model is derivable, e.g., for the HVdc converter system, and the frequency scanning method is useful if only ‘black boxed’ models are available. For stability analysis, the systems are represented in the dq0 domain which gets rid of the coupling between frequencies. The frequency scanning is conducted on two Electromagnetic Transients (EMT) simulation platforms, the off-line PSCAD/EMTDC and the real-time RTDS. PSCAD/EMTDC uses a highly-accurate bi-valued resistor model for the switches and its scanned impedance response matches the analytically derived result very well. The RTDS uses a more approximate technique that allows high speed simulation. The impedance scans from RTDS also agree with the analytical results showing that the scanning technique is also a mechanism for validating various new models. An important contribution of this research is the development of an improved small signal analytical model of the Line Commutated Converter (LCC). Traditional studies neglected the transient dynamics of the commutation inductor and modeled it as the reactance of fundamental frequency. The model realized here assumes an infinite series-connected 6- pulse converters and regards the 12-pulse converter as one of its sampled versions. With this treatment, the waveforms of the currents and voltages are ripple-free, and the measured angles, i.e., the overlap angle and the extinction angle become continuous. This model is validated by comparing the analytical converter response with the one obtained from the frequency scanning. Moreover, this LCC model is interfaced with an arbitrary external system, where the system stability is determined using GNC. Based on the impedance responses, the ac-dc system is represented as a closed-loop system, in which the dc side impedance is the forward gain and the ac side admittance is the feedback, or vice versa. The GNC is applied on the closed-loop system and a new index “Harmonic Stability Margin” is proposed to indicate how much a gain or other parameter can be scaled so that the system reaches the boundary of instability. As the index can parameterize the ac-dc interaction level, HSM is then used in several case systems to study the Sub-Synchronous Control Interaction (SSCI), the impact of detailed modeling of remote side converter, as well as the multi-infeed system study. All the results are validated by EMT simulations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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.

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
Published2019
Admission routes1
Has abstractyes

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