A Novel Hypothesis Testing-Based Scheme for Root Cause Classification of Disturbances in Distribution Systems
Bibliographic record
Abstract
In power systems, disturbances often result from faults or operational events, making it crucial to accurately identify their sources to prevent system failures and maintain grid stability. Existing research primarily classifies disturbances based on waveform characteristics, such as sags, swells, and transients, without determining their root causes, including incipient faults, constant impedance faults, load switching, and capacitor switching events. This paper proposes a hypothesis testing-based scheme for classifying power distribution disturbances by their root causes, ensuring reliable and interpretable results without extensive datasets. The scheme uses discrete-time voltage and current measurements at substations to develop disturbance models for substation voltages, incorporating disturbance parameters and load impedance. Load impedance is estimated from recent normal cycles, and disturbance parameters are then derived using substation measurements and the estimated load impedance. By substituting these estimated parameters into the corresponding disturbance models, substation voltages for each disturbance type are estimated. The disturbance type is classified by selecting the one that minimizes the normalized mean square error between the estimated and measured substation voltages. The proposed method is evaluated using the IEEE 13-bus test feeder simulated in PSCAD/EMTDC and validated on a two-day real-world power system dataset collected by the IEEE Power & Energy Society Working Group on Power Quality Data Analytics.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".