Detailed modelling, simulation and benchmarking of the voltage error detector circuit in the static excitation system of Manitoba Hydro's Long Spruce G. S. using PSCAD/EMTDC
Bibliographic record
Abstract
Manitoba Hydro continues to refine a reliable model of its power system using PSCAD/EMTDC Testing Long Spruce detailed exciter model revealed an under-damped response not observed in field tests. In this paper, the cause of this oscillation is investigated and a solution proposed. In addition, a solution is proposed to include a voltage reference input to the model to allow for testing comparable to the field. A method to determine proper settings for the calibration potentiometers, which could be of use in the field as well as in simulations, was also developed The exciter model consists of several sub-modules representing the detailed functionality of actual control circuits, such as minimum excitation and volts/hertz limiters, stabilizer, current feedback, etc. Selective testing of each module showed the voltage error detector (VED) to be the cause of the oscillations. Mesh current analysis and other circuit analysis techniques were applied to establish an accurate transfer characteristic of the VED. The characteristic of the VED smoothing filter was determined to be unrealistic. The zener diodes were modeled as ideal, resulting in a non-linear transfer function. The voltage adjustment potentiometer was included to allow for user defined reference voltage settings. A conversion model was developed to convert the per-unit reference voltage setting to an adjustment potentiometer setting. The model was evaluated in open and closed loop step response simulations. Results were compared to field data for validation of the overall exciter. Including the nonlinear idealized behavior of the zener diodes improved the transfer characteristic for severe voltage drops in keeping with expected field operation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".