Design and Implementation of SCADA Architecture Based on MATLAB App Designer for a Hybrid Power System
Bibliographic record
Abstract
Hybrid renewable power systems (HRPS) are now considered reliable solutions for power generation under various conditions. A critical challenge for deploying HRPS involves the design and implementation of an effective supervisory control and data acquisition (SCADA) system, which is essential for real-time monitoring and control. The SCADA system plays a vital role in remote monitoring and control by enabling real-time data management, early fault detection, and timely troubleshooting. This study presents the design and implementation of a SCADA architecture based on a MATLAB App Designer for an HRPS consisting of wind turbines, photovoltaic panels, diesel generators, and batteries. The system utilizes Arduino Mega 2560 as remote terminal units (RTUs) to interface with actuators and measure critical system parameters such as voltage and current. Arduino boards can be controlled directly from MATLAB® via the MATLAB® Support Package for Arduino® Hardware. A laptop is the main terminal unit (MTU), communicating with the Arduino via the Firmata protocol. MATLAB App Designer is the central monitoring interface, providing real-time data acquisition, processing, and visualization. In addition, a relay module is used to control the operation of the diesel generator. The relay module acts as an intermediary between the control system and the diesel generator, allowing for automated control of the generator's operation. It receives commands from the SCADA system to either start or stop the generator based on the energy demands and the availability of renewable resources. By managing the diesel generator's activity, the relay module helps ensure that the HRPS maintains a balanced and efficient power supply, minimizing reliance on non-renewable sources when renewable energy is sufficient. Based on experimental results, the proposed SCADA system effectively monitors and controls HRPS under different conditions.
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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) | 0.000 | 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) | 0.012 | 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".