Development of an IoT Based Open Source SCADA System for PV System Monitoring
Bibliographic record
Abstract
This paper presents the development of a low cost, open source Supervisory Control and Data Acquisition (SCADA) system for solar photovoltaic (PV) system monitoring and remote control. The proposed SCADA system is based on the Internet of Things (IoT) SCADA architecture which incorporates web services with the conventional SCADA for a robust supervisory control and monitoring. It comprises of analog Current and Voltage sensors for acquiring the desired data from the solar PV system, Arduino Uno micro-controller which serves as a Remote Terminal Unit to receive the acquired sensor data, Raspberry Pi with a Node-RED programming tool for parsing the acquired data (Communication Channel), and Emoncms Local Server IoT Platform for data storage, monitoring and remote control (Master Terminal Unit). The developed SCADA system was set up to monitor and control a 260W, 12V solar PV panel in the Electrical and Computer Engineering Laboratory at Memorial University, and some of the created Dashboards and Charts showing the acquired data on Emoncms server where an operator can monitor the data in the cloud using both a computer with internet access, and Emoncms mobile app are presented in the paper.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".