Improved terminal sliding mode direct power control for master-slave scheme in microgrids
Bibliographic record
Abstract
Master-slave control is a practical control scheme for operating microgrids in both islanded and grid-connected modes. It offers a competitive alternative to droop control, particularly for small-scale microgrids and/or nanogrids, where all loads are connected to the same bus, namely the point of common coupling (PCC). This control scheme enables a precise voltage/frequency adjustment, as well as accurate power/current injection. This study presents an improved version of sliding mode control (SMC), specifically integral terminal sliding mode control (ITSMC), in a microgrid environment. This presented approach incorporates the tanh function in the input control law to reduce chattering in the injected power/current. This study demonstrates how the master inverter stabilises voltage/frequency at the PCC, while the slave inverter regulates power injection using direct power control. ITSMC operates both inverters across various modes. Additionally, the performance of the developed approach is compared with that of some widely used controllers for microgrid operation. The adopted controllers for comparison are the proportional-integral (PI) controller, fractional-order PI controller, and power rate-SMC, which serves as a benchmark for adaptive SMC. The stability analysis is also presented to confirm that the developed ITSMC ensures robust and stable operation across different operational modes. Furthermore, the experimental setup, which is built in the lab with a power rating of 2 kVA for each inverter, demonstrates the good performance of the designed ITSMC for the microgrid under various dynamic loads and operating modes.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".