Enhanced fault location method for shunt capacitor banks
Bibliographic record
Abstract
High Voltage Shunt Capacitor Banks (SCBs) are the most economical and critical components in the power system, providing reactive power and voltage support. Over temperature, over voltages, manufacturing defects can cause internal failures of capacitor elements. With todays sensitive protection available in numerical relays capacitor elements failure will be detected and capacitor bank will be taken out of service. But determining the phase and section in which capacitor elements have failed is important for utilities to expedite their repair process and can decrease downtime of this critical component. To address these issues of locating capacitor elements failures, this paper proposes an enhanced scheme for fault location detection in both grounded and ungrounded Y-Y SCBs; both for fuseless and internally fused units. Simulations of the proposed fault location method are carried out using PSCAD and MATLAB. The results validate the proposed method performance under pre-existing inherent unbalances, system voltage unbalance, and faults in the grid. The application and significance of the proposed method properties are demonstrated using illustrative simulation scenarios. This method can be integrated into a common unbalance protection of the multi-functional numerical capacitor bank relays and puts forth solutions to enhance fault location of SCBs. The method is capable of detecting consecutive capacitor elements failures, and also can mitigate gradual capacitance change due to temperature effects or natural aging. The presented fault location method is further enhanced to detect the number of failed elements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".