Assessment of asset safety risk for transmission stations
Bibliographic record
Abstract
Safety to workers and to the public has been one of the most important concerns for electric utilities in the past and continues to be so in the deregulated electricity market. Two years ago, Hydro One launched a new initiative to make the company injury free and therefore, major efforts were taken to accomplish such a target. In achieving high safety performance for station equipment, electric utilities are using approved planning, design and operation guidelines and standards in order to minimize or eliminate hazardous events that may endanger safety to personnel. These guidelines and standards govern a variety of issues including approved equipment and facilities, training, equipment monitoring, regulatory compliance, etc. The assessment of the station asset-related safety risk was considered in the past but mainly in a qualitative context. A structured probabilistic approach to safety risk has not yet been adopted. Hydro One like other electric utilities established some safety measures such as the accident severity rate and lost time injury frequency rate based on experience and knowledge of field experts. These measures, however, can not be used to predict the safety risk in the future. In addition, due to aging of power equipment and facilities, the performance and conditions of these assets may deteriorate and that may raise some concern regarding changes to the safety risk profile. Also, running the assets harder in a competitive market requires that closer attention be directed to the safety aspects of equipment. Therefore, there is a need for developing a safety risk assessment methodology that can account for some of these factors and concerns. Hydro One has assessed a number of methodologies to assist in quantifying safety risks associated with the catastrophic failures of transmission station equipment. This paper describes the assessment and its findings. This includes the study assessment methodology, data required, results obtained for some selected station assets and study recommendations
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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 | 0.001 | 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".