An adaptive neuro-fuzzy inference system-based MPPT controller for photovoltaic arrays
Bibliographic record
Abstract
This paper presents a Maximum Power Point Tracking (MPPT) method applying an Adaptive Neuro-Fuzzy Inference System (ANFIS) for a stand-alone photovoltaic (PV) system. The proposed ANFIS-based MPPT technique determines the optimal operating point of a PV system which is designed in conjunction with a Z-source DC-DC converter as an interface between the PV array and the load. In the ANFIS-based MPPT controller, real meteorological data are used to define the two input membership function plots assuming that the PV array is located in Ottawa, Canada. The performance of the proposed MPPT technique in tracking the maximum power point (MPP) is assessed numerically in the MATLAB/Simulink environment. The simulation results highlight the benefits of determining reference voltage and duty cycle as output membership functions of the control system without using the current and voltage sensors. Moreover, in comparison with conventional control systems, the proposed solution can reduce the complexity and cost of the control system by eliminating the PID controller.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".