Fault‐tolerant controller design for a master generation unit in an isolated hybrid wind‐diesel power system
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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