A Laboratory Set-Up for Real-Time Power System Simulation using LabVIEW and NI PXI Hardware
Bibliographic record
Abstract
This paper presents the implementation of an electrical power system in MATLAB/Simulink and its execution in a real-time environment, called laboratory set-up for real-time power system simulation. This test-bed can help engineer and operator to learn the reaction of real power system in a virtual environment. Consistent to this issue, related software and hardware (National Instrument) are utilized to monitor electrical system variables, as well as analyze and evaluate power system stability in different real scenarios. The system implemented in MATLAB/Simulink, includes synchronous generator, power system stabilizer (PSS), excitation system and external grid. PXIe-8133 hardware allows user to execute different scenarios in the real-time mode. Also, LabVIEW DSC Module, a special monitoring system for industrial application, is used to access and flow data between several Vis. A program is developed in LabVIEW environment to initialize interaction between MATLAB generated *.dll file and LabVIEW via simulation interface toolkit (SIT) and in a parallel operation, data is simultaneously transferred between these two environments. EZDSP 320f2812 board is considered as hardware-in-the-loop (HIL). This feature helps engineer to implement control logic of power system equipment (here, real excitation system logic) in the DSP board. Based on the proposed idea, this board can serve as an emulator of real system and the functionality of control logic as well as its behavior is simulated and verified. This board and related potentiometer play the role of real excitation system and receive three-phase voltages and currents from NI PXI 7833R card. In return, the board generates excitation voltage and sends it back to PXIe-8133 and consequently, the loop will be closed. Finally, different scenarios in a power system are provided to show the application of real-time HIL system in the power industry as well as verification of proposed approach for training centers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".