Robust Control of Grid-Connected Photovoltaic Systems Under Unbalanced Faults Without PLL
Bibliographic record
Abstract
This paper presents the design of a robust control scheme for grid-connected photovoltaic systems subjected to severe operating conditions such as grid faults, abrupt set-point changes, parametric uncertainties, and unknown disturbances. During unbalanced faults, the scheme is able to deliver either constant real and reactive powers, or constant real power with sinusoidal currents to the grid, without requiring a phase-locked loop or symmetrical component decomposition. Moreover, the same controllers are used under normal operation and grid faults. These feats result in a control system having lesser computational requirements and complexity, and a lower number of design steps in comparison to some existing schemes. The dc-link voltage controller is based on active disturbance rejection control, and phase currents are controlled using repetitive control based on the internal model principle. The controllers are designed using linear matrix inequality constraints that can be solved by readily available tools. A number of simulation test cases are presented using the SimPowerSystems toolbox of the MATLAB/Simulink computing environment to demonstrate the performance of the control scheme under various types of grid faults, parametric uncertainties, and abrupt changes under operating conditions. Controller performance is also validated through digital implementation on a low-cost microcontroller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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.
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".