An Open Source LoRa Based, Low-Cost IoT Platform for Renewable Energy Generation Unit Monitoring and Supervisory Control
Bibliographic record
Abstract
SCADA provides real-time system monitoring by constant communication and data exchange between various system devices to achieve data visualization and logging. Presently, in industrial systems, commercial SCADA systems are being used for data monitoring and control. These systems can be expensive, and as such can only be afforded by select industries. Even at these costs, the commercial SCADA systems face some challenges, which include interoperability and scalability issues. Research has shown that these problems can be solved by the introduction of low-cost materials and open-source software to achieve data monitoring for all levels of processes. This paper proposes an open source, low-cost Internet of Things (IoT)-based SCADA system that employs the IoT architecture for SCADA functions. The proposed system is an improvement to the existing IoT solutions by eliminating cloud based IoT platforms and introducing a single machine system. This solution increases the robustness of the system while reducing costs. The proposed system prototype consists of voltage and current sensors, Arduino Uno microcontroller and Raspberry Pi. The sensors acquire data from the monitored unit. The Arduino Uno receives the data and processes them for transmission to the Raspberry Pi using the LoRa communication technology. At the Raspberry Pi, the local Chirpstack platform processes the data and displays the measured data using the Grafana dashboard for real-time data monitoring, and the data is stored in an InfluxDB database. For system validation purposes, the prototype is designed, developed, and set up to monitor the panel voltage, current and battery voltage of a solar photovoltaic system. The results obtained from the experimental set-up are compared with the test data from physical digital multimeters. The system presented in this paper is a low-cost, open source, scalable and interoperable system. This, therefore, makes the proposed SCADA system an alternative for commercial SCADA systems, especially for select applications. The system proposed in this paper can be deployed to large industrial systems with appropriate upgrades and customization. The main contribution of this research is the design and development of a SCADA system that performs all the functions of a proprietary SCADA system at a very low-cost with scalable and interoperability features which are the main limitations of the traditional SCADA systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 0.004 |
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".