A Novel Design of a Low-Cost SCADA System for Monitoring Standalone Photovoltaic Systems
Bibliographic record
Abstract
Standalone photovoltaic (PV) systems are pivotal in the global transition towards sustainable energy, offering reductions in fossil fuel dependence and helping homes and businesses lower electricity costs. Key to optimizing the performance and efficiency of standalone systems are supervisory control and data logging (SCADA) systems. They monitor and record operational data such as power output, facilitating early detection of potential issues. This paper introduced a novel design for both the Human-Machine Interface (HMI) and data storage in a SCADA system for standalone PV systems, addressing two crucial aspects: real-time monitoring and efficient data retrieval, both at very low cost. The proposed design utilized Bluetooth Low Energy technology to transmit voltage and current data from the PV panel to a mobile application, marking a departure from traditional HMI approaches. This method enabled historical data analysis for trend identification. Additionally, the system intermittently transferred collected data to a cost-effective cloud storage service via Wi-Fi, allowing for substantial data storage at no cost. Remote data storage, another key feature of this design, simplifies data retrieval, which is particularly beneficial for systems in rural areas. Emphasizing open-source development, this design ensured flexibility and customization options. To demonstrate its practical effectiveness of the design, a one-day power curve of the PV system and the battery voltage data are presented, showcasing the design's capability in handling extensive and remote data storage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".