Adaptive Neuro Fuzzy Inference System Based Intelligent Control for Grid Connected Hybrid Energy System with Improved SEPIC Converter
Bibliographic record
Abstract
The primary objective of this study is to meet the energy demands of the power consumer through the implementation of Hybrid Renewable Energy System (HRES) with multiple solar panels and wind turbines. As the photovoltaic (PV) power generation is enveloped with multiple advantageous measures like low maintenance, environmental-friendliness and fuel-efficient, it is significantly preferred in this study. However, the low power conversion quality of PV weakens the overall system and so the DC-DC improved SEPIC converter with magnetic coupling is employed to achieve maximum DC output voltage. In addition, the Adaptive Neuro Fuzzy Inference System (ANFIS) is implemented in this work totrack the maximum power from PV and to maximize the energy efficiency in an optimal manner. The MATLAB Simulink is used to validate the present study with optimal outcomes and the obtained results prove that the present approach delivers lesser THD of 1.00%, which in turn efficiently enhances the overall performance of the system. Thus, the research findings of this system are well suited to be applied as the solution for rectifying the issues in the DC link voltage control and grid compensation or synchronization.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".