Real‐time experimental implementation of an LMS‐adaline‐based ANFIS controller to drive PV interfacing power system
Bibliographic record
Abstract
This paper presents the experimental implementation of a LMS‐Adaline‐based ANFIS controller of an improved power‐quality photovoltaic (PV) generating system connected to the grid. The proposed system applies an adaptive neuro‐fuzzy inference system (ANFIS) to control the DC–DC boost converter integrated with PV to achieve the maximum power point tracking (MPPT) operating condition. For power‐quality improvement at the point of common coupling (PCC), Adaline (adaptive linear element)‐based control algorithm is used to estimate the reference grid currents. To achieve high performance with fast dynamic response during transition and to regulate constant the DC and the AC voltages without saturation phenomena, ANFIS controller is employed. The real‐time benchmark realised in the laboratory, to implement the setup, uses a dSPACE controller. To demonstrate the performance of the proposed configuration, the system is first simulated offline under numerous critical scenarios. The experimental results are then presented to validate the concept.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".