Evaluating Power Quality Monitoring Devices: A Hydro-Québec Benchmark Using Statistical and Machine Learning Methods
Bibliographic record
Abstract
This paper presents a detailed performance evaluation of various power quality (PQ) monitoring devices utilizing the Hydro-Québec benchmark and a combination of statistical analysis tests and machine learning techniques, specifically Partial Least Squares Discriminant Analysis (PLSDA). The study examines six devices, assessing their compliance with IEC standards 61000-4-30, 61000-4-15, and 61000-4-7. The evaluation was conducted using a proprietary reference system developed by Hydro-Québec/IREQ. The devices underwent rigorous testing, including stationary and variable frequency assessments, as well as event detection for voltage dips, swells, and interruptions. The combination of statistical tests and machine learning provided a robust analysis, identifying the most accurate and reliable devices, closely matching the HydroQuébec benchmark. This study highlights the importance of integrating statistical analysis with advanced machine learning techniques like PLS-DA to enhance the evaluation and selection process of PQ monitoring devices, ensuring compliance with industry standards and optimizing reliability at Hydro-Québec.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".