A rule-based fuzzy logic controller for a PWM inverter in photo-voltaic energy conversion scheme
Bibliographic record
Abstract
A rule-based controller based on fuzzy set theory for controlling the output power of a pulse width modulation (PWM) inverter in a photovoltaic (PV) energy conversion interface scheme is presented. The objective is to track and extract the maximum available solar power from the PV array under varying solar insolation levels. To achieve this the power error and the rate of change of this error are used as input signals to the fuzzy rule-based controller and its output signal is used to control the PWM inverter. The input error signals are fuzzified and expressed as linguistic labels characterized by their membership grades. Using a fuzzy relation matrix, a set of 49 rules find fuzzy logic operations, the controller output is obtained. The fuzzy controller output expressed in linguistic labels is defuzzified to obtain the actual analog signal to control the PWM inverter. The proposed fuzzy rule-based controller is simulated and experimentally verified, and is found to give good power tracking performance.>
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".