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Record W2801059316 · doi:10.1155/2018/3140309

Low-Cost SCADA System Using Arduino and Reliance SCADA for a Stand-Alone Photovoltaic System

2018· article· en· W2801059316 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Solar Energy · 2018
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSCADAModbusArduinoEmbedded systemMicrocontrollerPhotovoltaic systemEngineeringComputer scienceCommunications protocolElectrical engineeringOperating system

Abstract

fetched live from OpenAlex

SCADA (supervisory control and data acquisition) systems are currently employed in many applications, such as home automation, greenhouse automation, and hybrid power systems. Commercial SCADA systems are costly to set up and maintain; therefore those are not used for small renewable energy systems. This paper demonstrates applying Reliance SCADA and Arduino Uno on a small photovoltaic (PV) power system to monitor the PV current, voltage, and battery, as well as efficiency. The designed system uses low-cost sensors, an Arduino Uno microcontroller, and free Reliance SCADA software. The Arduino Uno microcontroller collects data from sensors and communicates with a computer through a USB cable. Uno has been programmed to transmit data to Reliance SCADA on PC. In addition, Modbus library has been uploaded on Arduino to allow communication between the Arduino and our SCADA system by using MODBUS RTU protocol. The results of the experiments demonstrate that SCADA works in real time and can be effectively used in monitoring a solar energy system.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.207
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it