Enhanced Power control in Microgrids Using Adaptive Neuro-Fuzzy Control
Bibliographic record
Abstract
Droop control is the most commonly used controller in inverter-interfaced microgrids as a primary control to regulate active and reactive power exchange. However, feeder impedance variations and slow response to dynamic load changes often restrict its performance, resulting in power-sharing inaccura-cies. To address these limitations, this paper introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based virtual impedance controller that dynamically adjusts the inverter's reference voltage to improve power control and response time. The proposed controller continuously compensates for impedance mismatches by adjusting the virtual voltage, and it ensures more accurate tracking of active and reactive power with minimal deviations. By leveraging the combined strengths of fuzzy logic and neural networks, ANFIS eliminates the need for manual tuning and enhances control adaptability in nonlinear microgrid environments. The proposed controller is validated using a 100 k W battery energy storage in islanded and grid-connected in various operating conditions and load changes in charging and discharging mode, and comparative results with traditional controller highlights the superior tracking accuracy, reduced response time, and improved system resilience of the proposed approach, making it a viable solution for enhancing microgrid 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".