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Record W1932429074 · doi:10.1002/rnc.3277

Fault‐tolerant controller design for a master generation unit in an isolated hybrid wind‐diesel power system

2014· article· en· W1932429074 on OpenAlexaff
Adriana Vargas‐Martínez, Luis I. Minchala, Youmin Zhang, Luis E. Garza-Castañón, Patricia Acosta-Santana

Bibliographic record

VenueInternational Journal of Robust and Nonlinear Control · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)PID controllerMicrogridEngineeringRobustness (evolution)Controller (irrigation)Fault (geology)Automatic frequency controlControl engineeringVoltageComputer scienceTemperature controlControl (management)

Abstract

fetched live from OpenAlex

Summary This paper presents a methodology for designing an effective fault‐tolerant controller (FTC) through the combination of three control techniques: linear parameter varying (LPV), model reference adaptive control (MRAC), and a proportional‐integral‐derivative (PID) controller. The proposed FTC is tested in a diesel engine generator (DEG), operating as a master generation unit in an autonomous hybrid wind‐diesel power system with a battery storage system (BSS). The control objectives are to regulate voltage and frequency of the DEG and to ensure covering the demand load. Frequency regulation is achieved with the help of an MRAC‐LPV scheme combining a PID controller tuned by a genetic algorithm (GA) for maintaining the speed of the diesel engine (DE) in a constant value, and in consequence the frequency of the grid. Voltage magnitude control is performed through a constrained variation of the field voltage of the synchronous generator through a classic MRAC. Different operating conditions of the hybrid power system are applied in order to test the controller's robustness: (i) steady‐state operation; (ii) sudden connection of a load of 0.5 MW; (iii) a three‐phase fault with duration of 0.5 s; and (iv) DE's actuator fault with six different magnitudes. An improved performance is achieved by the proposed scheme over a baseline controller, IEEE type 1 AVR for voltage regulation and a governor with PI controller for frequency regulation. Dynamic models of the microgrid components are presented, and the proposed microgrid and its FTC are implemented and tested in the Simpower Systems of MATLAB/Simulink simulation environment. The simulation results showed that the use of an LPV methodology for designing the MRAC allows the online accommodation of different fault magnitudes in the DE actuator and improves the FTC system performance in comparison with the baseline controller. Copyright © 2014 John Wiley & Sons, Ltd.

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 score0.002
Threshold uncertainty score0.004

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.219
Teacher spread0.200 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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