Pre-Charge Pressure Estimation of a Hydraulic Accumulator Using Surface Temperature Measurements
Bibliographic record
Abstract
Pitch systems form an essential part of today’s wind turbines; they are used for power regulation and serve as part of a turbine’s safety system. Hydraulic pitch systems include hydraulic accumulators, which comprise a crucial part of the safety system, as they are used to store energy for emergency shutdowns. However, accumulators may be subject to gas leakage, which is the primary failure mode. Gas leakage affects the performance of the accumulator and, in extreme cases, compromises the safety function of the pitch system. This paper deals with the development and experimental validation of an algorithm to detect gas leakage in piston-type accumulators. The innovation of the algorithm is the ability to generate estimates of the remaining amount of gas while solving the drift problem evidenced in previous research. Additionally, this method enables the ability to isolate gas leakage to a single accumulator out of a bank of accumulators. The approach is based on a State Augmented Extended Kalman Filter (SAEKF), which utilizes an extended thermal model of the accumulator, as well as temperature measurements along the accumulator surface to estimate the remaining gas in the accumulator. The method is experimentally validated and addresses the drift problem in estimating the gas leakage evidenced from previous research. Additionally, the method can identify and isolate gas leakage to a single accumulator from a bank of accumulators.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".