Supervisory Control for Under-Load Tap-Changing Transformers Using Discrete-Event Systems
Bibliographic record
Abstract
In this chapter, different solutions based on supervisory control of DES have been proposed and implemented for a control problem in electrical power systems. The problem of voltage regulation by ULTC was first modeled in terms of plant components and control specification. Controllability of the specification was evaluated and, by use of the TCT software, supervisory controllers were designed in different modes of operation including a two-level hierarchical structure. It is guaranteed by the synthesis procedure that the designed supervisors are optimal and non-blocking. The state size of the supervisory controllers was reduced for easier implementation. In the hierarchical supervisory control structure, the abstracted plant model in the high level was controlled by another supervisor, or manager, to handle the ULTC in failure situations. The synthesis study shows that hierarchical supervisory control structure can be applied as a solution to the control problem in electrical power substations. Designers of protective systems for electrical power systems can use the proposed solutions to design appropriate supervisory control systems and to verify their control logic for ULTC. The hierarchical control structure can also be employed to synthesize the coordination control of ULTC transformers and certain FACTS devices, where DES models are available. The designed supervisory controllers can be implemented by programmable logic controllers (PLC) to be used in real world. Generalizing this design approach to an electrical grid where many ULTCs and other switches are integrated is considered for future research work. Using a step-by-step procedure, a ladder diagram was developed for implementation of the designed Auto/Manual untimed ULTC supervisor that can be directly downloaded into a PLC. The generated PLC codes can be used in the real-time control of electrical power systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".