A Proposed Adaptive Filter for Harmonics Mitigation based on Adaptive Neuro Fuzzy Inference System Model for Hybrid Wind Solar Energy System
Bibliographic record
Abstract
This paper is developing an approach to analyze the behavior of renewable energy systems and with the help of analysis, design a filter to reduce the effect of harmonics and improve the efficiency of distributed power systems consisting of hybrid renewable resources namely Wind and Solar. A powerful harmonic prediction model is proposed in this work to help in adapting the filter parameters to mitigate the harmonics injection due to renewable energy resources integration into the main grid. The outcomes from this study and forecasted model serve as inputs to select from a combination of filters to effectively mitigate harmonics from system. The proposed energy source consists of two wind models using Permanent Magnet Synchronous Generator (PMSG) driven via wind turbine and a solar photovoltaic (PV). This hybrid model demonstrates that the electric power generated is not stable because of the wind speed and solar irradiation fluctuation. After establishing the hybrid model and generating the electric power output via simulation, a forecasting model was developed using the simulated data and other relevant parameters as inputs for training to predict the hour ahead and day ahead forecast. In this work the Adaptive Neuro Fuzzy Inference System (ANFIS) is used for forecasting purpose and based on that forecasting model the filter parameters could be adapted. Results prove the effectiveness of the proposed models.
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 | 0.000 |
| 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".