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 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 | 0.000 | 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) | 0.002 | 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".