A Decentralized Robust Control Strategy for Multi-DER Microgrids—Part II: Performance Evaluation
Bibliographic record
Abstract
In Part I of this two-part paper, a power-management and a control strategy for the microgrid autonomous mode of operation were presented. The strategy consists of 1) open-loop frequency control of the system and synchronization of DER units based on a GPS signal; 2) voltage reference setpoint determination for the DER units by the central power-management system; and 3) tracking the assigned setpoints and rejecting disturbances by robust, decentralized, local controllers of DER units. This Part II paper applies the envisioned strategy to a three-DER microgrid. Offline digital time-domain simulation studies in the EMTDC/PSCAD software environment demonstrate the robustness of the local controllers to parametric, topological, and unmodelled uncertainties of the microgrid, its fast performance in tracking the setpoints with zero steady-state error, and rapid disturbance rejection. The results also show the effectiveness of the proposed power-management system in achieving prescribed load sharing of DER units. The digitized algorithms of the proposed control system of the three-DER microgrid are also implemented in NI-cRIO industrial-grade platforms and tested in an RTDS-based real-time hardware-in-the-loop (HIL) environment to demonstrate the feasibility of the strategy for hardware implementation and hardware-based performance validation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".