Design and implementation of a low-cost, open source IoT-based SCADA system using ESP32 with OLED, ThingsBoard and MQTT protocol
Bibliographic record
Abstract
Distributed assets, such as hybrid power system components, require reliable, timely, and secure coordinated data monitoring and control systems. Supervisory Control and Data Acquisition (SCADA) is a technology for the coordinated monitoring and control of such assets. However, SCADA system designs and implementations have largely been proprietary, mostly pricey and therefore economically unjustifiable for smaller applications. With proprietary SCADA systems, there is also the problem of interoperability with the existing components such as power electronic converters, energy storage systems, and communication systems since these components are usually from multiple vendors. Therefore, an open source SCADA system represents the most flexible and most cost-effective SCADA option for such assets. In this paper, we present the design and implementation of a low-cost, open source SCADA system based on the most recent SCADA architecture, the Internet of Things (IoT). The proposed SCADA system consists of current and voltage sensors for data collection, an ESP32 micro-controller with organic light-emitting diode (OLED) display, for receiving and processing the sensor data, and ThingsBoard IoT server for historic data storage and human machine interactions. For the sensor data transfer from the ESP32 to the ThingsBoard IoT server, Message Queuing Telemetry Transport (MQTT) protocol is implemented for data transfer over a local Wi-Fi connection with the MQTT Client configured on the ESP32, and the ThingsBoard server node serving as the MQTT Broker. The ThingsBoard IoT server is locally installed with PostgreSQL database on a Raspberry Pi single-board computer and hosted locally on MUN Network for data integrity and security. To test the performance of the developed open source SCADA system solution, it was setup to acquire and process the current, voltage and power of a standalone solar photovoltaic system for remote monitoring and supervisory control. The overall system design procedures and testing, as well as the created dashboards and alarms on the ThingsBoard IoT server platform are presented in the paper.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".