An Internet of Things—Supervisory Control and Data Acquisition (IoT-SCADA) Architecture for Photovoltaic System Monitoring, Control, and Inspection in Real Time
Bibliographic record
Abstract
The Internet of Things (IoT) serves as a key component to enhance operational efficiency and decision-making in the context of supervisory control and data acquisition (SCADA) systems. Featuring the improved system robustness and real-time parameters, including images of the load, a new design of SCADA system monitoring for a photovoltaic (PV) system based on dual IoT platforms is proposed in this paper. Two voltage sensors collect the voltages of the PV module and the battery, while three current sensors accumulate the current data from the PV module, the battery, and the load. ESP32-E assembles the data and then transmits them to the Arduino Cloud via MQTT for real-time display and ESP32-S3 via HTTP. The relay and the load are controlled by ESP32-E to turn ON/OFF based on the battery voltage as well. In addition, ESP32-S3 forwards the received data to ThingSpeak for advanced analysis, data storage, and real-time display via HTTP. The load images are also displayed on a camera web server built by ESP32-S3. Successfully monitoring for over 20 days, the proposed system demonstrated its robustness and versatility even during the downtime of the Arduino Cloud, with a one-day voltage measurement ranging to a maximum of 13 V and current ranging from zero amperes to 4.42 amperes. To add to this system, it incorporates visual load monitoring features, which are unseen in traditional systems.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".