Artificial neural network model of photovoltaic generator for power flow analysis in PSS <sup>®</sup> SINCAL
Bibliographic record
Abstract
Output of a three‐phase photovoltaic generator (PVG) is a function of sunlight irradiance, temperature, and three‐phase terminal voltage phasors. Three‐phase PVGs are largely connected to rural distribution systems feeders that are predominantly unbalanced. Models of PVGs that are only a function of sunlight irradiance and temperature disregarding unbalanced three‐phase terminal voltages phasors are simple to use with three‐phase power flow analysis but yield inaccurate solutions. Detailed three‐phase PVG models are complex and non‐linear, hence unsuitable for power flow analysis applications. This study proposes an artificial neural network (ANN) model to represent a PVG comprising photovoltaic panels, a boost chopper and a three‐phase inverter. Main advantages of the ANN model are that it can be readily used to model a PVG of any size and type, mathematical simplicity, high accuracy with unbalanced systems and computational speed. The model was tested with the unbalanced distribution system feeder from a Canadian utility. The results show that the ANN model of a PVG is computationally fast and more accurate than simple model that ignores unbalanced three‐phase terminal voltage phasors. In addition, simplicity of the proposed ANN model of PVG allows easy integration into commercial software packages such as PSS ® SINCAL as reported in this study.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".