Digital-PV: A digital twin-based platform for autonomous aerial monitoring of large-scale photovoltaic power plants
Bibliographic record
Abstract
• Digital-PV is a digital twin-based support for aerial monitoring of PV plants. • Digital-PV enables analysis of different scenarios on PV plants’ aerial monitoring. • Perception data from Digital-PV can be used to develop smart monitoring models. • Intelligent monitoring models’ performance can be evaluated by Digital-PV. In this study, a novel digital twin-based solution called Digital-PV has been developed for the simulation and managed execution of autonomous aerial monitoring of photovoltaic (PV) power plants. Digital-PV empowers users to simulate different scenarios and PV power plant configurations and assess their impact on PV systems’ autonomous aerial monitoring process. This procedure reduces the risk associated with real-world experimentation and helps identify the most effective strategies to improve PV system monitoring. It also provides a virtual testing platform for autonomous flights and missions, including boundary detection, path planning, and fault detection along with data generation capabilities for developing data-driven monitoring and inspection models. The solution involved creating a digital twin of an R&D utility-scale PV plant environment in Unreal Engine, aerial robot flight simulation using AirSim, and developing application programming interfaces (APIs) for running desired scenarios for collecting data, testing different monitoring models like plant boundary extraction, path planning, fault detection, etc. In addition, during this study, a dataset of synthetic aerial images was collected from Digital-PV and used to train an end-to-end segmentation model for detecting bird droppings on PV panels. Finally, we utilized this platform to evaluate various intelligent monitoring models, gaining valuable insights into their capabilities and potential performance in real-world scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".