Shutter array technique for real-time non-invasive extraction of individual channel responses in multi-channel CPV modules
Bibliographic record
Abstract
Concentrator photovoltaic (CPV) solar energy systems use optics to concentrate direct normal incidence (DNI) sunlight onto multi-junction photovoltaic (MJPV) cells fabricated from III-V compound semiconductors on germanium substrates. The MJPV receiver, which integrates cell and bypass diode, is then mated with its concentrating optic to form a channel, and several such channels form a CPV module, in which the receivers are connected electrically in series. The two ends of the module receiver string are brought out to a single pair of electrical connections, at which point the lightcurrent- voltage (L-I-V) response of the entire module can be tested. With commercial CPV modules commonly sealed against outdoor exposure, there are no other accessible test points, and field installation on trackers further complicates access to performance data. There are many physical phenomena influencing module performance, and in early development and commercialization some of these may not yet be completely under control. Unambiguous diagnosis of such phenomena from one full-module L-I-V curve is problematic. Simple, fast test methods are needed to develop more detailed information from full-module on-tracker testing, without opening up modules in the field. We describe a test protocol, using a simple optical shutter array constructed to fit mechanically over the module. When module L-I-V curves are recorded for each of various combinations of open and closed shutters, the information can be used to identify one or more anomalous channels, and to further identify the kind of anomaly present, such as optical misalignment, conductor failure, series or shunt resistance, and so on. Simulated results from anomaly models can be compared with the measured results to identify the anomalous behaviour. Results herein are compared with direct single-channel measurements to verify the technique. The L-I-V response curves were obtained in continuous real time, an approach found to be more helpful than single-shot capture in understanding field response. A triangular wave function generator is used to drive the DC power supply, and a four-channel digital sampling oscilloscope displays and stores the real time response. Where modules exhibit unstable or intermittent response under certain conditions, this is immediately obvious in real-time display.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".