An Adaptive Neuro-Fuzzy Controller to Enhance Power Sharing in Distributed Energy Resources Applications
Bibliographic record
Abstract
In inverter-interfaced microgrids, droop control techniques are essential for regulating active and reactive power exchange. However, their performance is compromised by the varying impedance of the feeder and the slow response to dynamic load changes, leading to power-sharing inaccuracies. This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based virtual impedance controller to address this issue and enhance active and reactive power sharing. The proposed controller dynamically adjusts a virtual voltage to compensate for impedance mismatches, modifying the reference voltage of the inverter. This enables precise power tracking with minimal deviation from the defined reference values and a faster response under transient conditions, including startup and external disturbances. The ANFIS framework integrates fuzzy logic and neural networks, eliminating the limitations of manual and separate tuning in conventional controllers and improving performance in nonlinear systems. The controller’s performance is validated on an IEEE 39-bus test system under various scenarios, including charging, discharging, and transient disturbances. It is tested with three battery sizes (1 MW, 96 kW, and 75 kW) under the same controller setup to assess scalability. Training with per-unit data ensures scalability across different battery capacities and distributed generators. The results are compared to traditional methods to demonstrate the controller’s superior effectiveness.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".