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Record W4319030809 · doi:10.3390/en16031457

Evaluation and Solution Suggestions for Engineering and Workmanship Failures during Design and Installation of Solar Power Plants

2023· article· en· W4319030809 on OpenAlexaff
Hale Bakır, Adel Merabet

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsPhotovoltaic systemWorkmanshipRenewable energyGrid connectionPower (physics)Solar powerGrid-connected photovoltaic power systemElectricity generationElectrical engineeringEngineeringPayback periodReliability engineeringPower stationSolar energyInverterAutomotive engineeringMaximum power point trackingProduction (economics)VoltageOperations managementPhysics

Abstract

fetched live from OpenAlex

Among the various renewable energy generation systems, the solar photovoltaic occupies a leading position today due to its simple structure. However, increasing the efficiency of solar photovoltaic systems is a highly researched topic. In this study, possible connection failures in maximum power inverters and other failures, which decrease the efficiency in solar power plants, are examined. Furthermore, the possible consequences of these losses and their effects on the performance of solar power plants are explained. Some missing-failure processes were identified and corrected in the field analysis of the solar power plant in Turkey. Detected missing failures include connection failures of solar inverters, incorrect network configuration of camera system, fixing lighting time settings. The inverter string connection failure made during the projecting and assembly phase was eliminated and the maximum output was determined as 584.25 kW after the DC string arrangement. An increase of approximately 10% was achieved in production. In the project and application phase, the connection details of the inverters should be drawn and given to the field application personnel as a full-fledged project. In this way, incorrect connections that are not shown in the project and made in the field are prevented. This ensures that the installed power plant operates more efficiently, and the budgetary payback period of the investments made is shortened.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.029
GPT teacher head0.257
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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