A Digital Controller For Testing Control Strategy At Power Plants
Bibliographic record
Abstract
This paper describes the software and hardware of a programmable digital controller that was designed to enable the power system engineer to derive parameters and control strategies with a maximum degree of flexibility and efficiency during field test situations at power plants. Many test situations arise, in practical situations, where a person is attempting to determine suitable feedback parameters prior to establishing the "frozen" form of the analog, or digital, controller that will be permanently installed. In many cases prior theoretical calculations and simulation studies have enabled the designer to obtain "ball park" parameters for the controller and what is required is a further fine-tuning on-site to establish confidence that the controller, to be permanently installed, will do an adequate job. The programmable digital controller described in this paper has been tested extensively during field tests at power plants where studies were being conducted on the effect of power system stabilizer feedback to damp out local mode and inter-tie mode oscillations. The digital controller incorporates many attractive features that make it compatible with power system security. These include high impedance buffering on input and output of the digital controller, and redundant software and hardware limiting on the controller to avoid overdriving the critical control loops of the generator. One of the drawbacks of "off the shelf" microprocessors, or minicomputers in the low end price range, is the cycle time that severely limits the number of mathematical operations (multiplications, additions, etc) that can be done between A/D samples in the control algorithm. The digital controller described in this paper utilizes a microprocessor system with modifications and hardware improvements so that the digital controller is able to work in the fast feed back loops of the power plant with fairly complex and high order controllers simulated in the controller. Finally, a sample of some of the field test results obtained with the digital controller is included in the paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".