Artificial Neural Network PI-Based Compensator for DC-Link Current Control in Grid-Connected Photovoltaic Current Source Inverter
Bibliographic record
Abstract
Conventional proportional integral (PI) controllers are the most, commonly, known and used techniques in grid-connected photovoltaic (PV) applications. Indeed, PI-based controllers are, practically, easy to design and implement. Moreover, they provide good performance, i.e., high-quality output signals, in steady-state conditions. Nevertheless, these controllers are, mainly, designed for the control of linear systems. Therefore, when implemented with non-linear systems, they suffer from slow dynamic response. Accordingly, in this paper, a non-linear and advanced PI-based controller is proposed. The proposed controller consists of an auto-tuned PI-based controller using artificial neural network (ANN). The main objective is to enhance the dynamic response of the DClink inductor current in grid-connected PV current source inverter (CSI) and to ensure that the injected grid current is equal to that generated by the DC-bus, which implies that the entire power provided by PV generator is injected into the grid. A numerical simulation model of the grid-connected PV system, i.e., CSI, PI-controller, and MPPT algorithm, is realized using Simulink and PLECS environments to validate the accuracy of the proposed solutions. The numerical results prove that the ANN auto-tuned PI-based controller leads to superior dynamic response of the DC-link inductor current and better-quality of the injected grid current compared to the conventional PI-based controller.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".