Shunt Capacitor Banks Online Monitoring Using a Superimposed Reactance Method
Bibliographic record
Abstract
A new indicating quantity, named superimposed reactance (SR), is presented in this paper to determine failed capacitor elements' involved phase and number in shunt capacitor banks (SCBs). The proposed quantity is estimated using available measurements to the unbalance protection function of SCBs numerical protective relays. The proposed SR adopts calibrating factors for element failures online monitoring and can provide live report of the number of failed capacitor elements. The proposed method applications are: faster identifying SCBs faulty units, for fuseless and internally fused designs, and planning preventive/scheduled maintenance for all types of unit designs including externally fused units. With integration of these applications into protection intelligent electronic devices that provide captured and time-tagged event records via communication protocols for HMIs and SCADA systems, outage times would be reduced and smart grids reliability would be improved. The developed algorithm supports three different grounding arrangements, Wye-ungrounded, Wye-grounded via a low ratio current transformer, and Wye-grounded via a grounding capacitor at the SCB neutral point. Comprehensive simulation and fault-location sensing in PSCAD and MATLAB have verified the proposed algorithm performance. Advantages of the proposed method reports over conventional unbalance relaying alarms are also demonstrated using a commercial relay test results comparison.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".