A New Interface for Power Hardware-in-the-Loop Simulation Using Nelder-Mead Algorithm
Bibliographic record
Abstract
A cyber-physical system is a system that integrates computation and physical processes. Such a system has found numerous applications in power systems. One such application is power hardware-in-the-loop (PHIL) simulation. In the context of PHIL simulation, a hardware device under test (DUT) is typically linked to a digital real-time simulator (DRTS) via a PHIL interface. Over time, several PHIL interfaces have been proposed and explored. Notably, the ideal transformer model (ITM) stands out due to its popularity, primarily for its ease of implementation. Other PHIL interfaces, such as partial circuit duplication (PCD) and damping impedance, can be viewed as extensions of the ITM concept. These PHIL interfaces necessitate a strict impedance ratio between the physical (i.e., the DUT) and the cyber parts (i.e., the system modeled in DRTS) before embarking on a PHIL implementation. This prerequisite can often prove to be a demanding and complex task. This article introduces a novel PHIL interface for PHIL using Nelder–Mead (NM) algorithm, designed to eliminate such constraints. Notably, the proposed PHIL interface offers an expanded stability region when compared with ITM, thus rendering it suitable for a broader range of PHIL applications. The effectiveness of this proposed method has been confirmed by a practical PHIL setup.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".