Removal of measurement noise spikes in grid-connected power converters
Bibliographic record
Abstract
Power Electronic Converters (PECs) are routinely used to control the energy supplied to electric grids from distributed generators. The operation of PECs involves the control of voltage and current by rapidly switching power semiconductor devices. Rapid switching of these devices causes a certain class of high frequency noise called spikes which in turn are induced into sensor measurements. In addition to PEC control functions, the sensor measurements are also used to detect over current (OI) over voltage (OV) and under voltage (UV) faults. The presence of noise spikes compromises the performance and leads to unnecessary triggering of fault protection circuitry as a result of false detection of over/under voltage and current faults. The development of a new algorithm to remove short time electrical spikes from PEC sensors is the focus of this paper. In such a way, the PEC is unaffected and false OI, OV, and UV alarms are eliminated. The methodology uses the discrete wavelet transform (DWT) to generate a feature space where the spike feature is detected and removed, and the inverse DWT to reconstruct spike-free measurements. A Field-Programmable Gate Array (FPGA)-based interface board is designed to implement the new algorithm within an actual PEC system. The designed FPGA board along with the new algorithm is used in integration of distributed generators into power systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".