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Record W2980097854 · doi:10.1109/ccece.2019.8861935

Performance and Conformance Analysis of a Commercial Scale PV Inverter

2019· article· en· W2980097854 on OpenAlexaffabout
Alexandre B. Nassif, Hesam Yazdanpanahi, Matthew B. Wright, A.P. Robertson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsATCO (Canada)
Fundersnot available
KeywordsMicrogridRenewable energyInverterPhotovoltaic systemReliability engineeringComputer sciencePower electronicsNetwork topologyInterconnectionScale (ratio)Electrical engineeringEngineeringAutomotive engineeringSystems engineeringVoltageTelecommunicationsComputer network

Abstract

fetched live from OpenAlex

Renewable energy has been in the spotlight of most jurisdictions around the world. Among the sources of renewable energy, solar energy is one of the most commonly adopted in small to medium-scale installations, such as those in residential and commercial applications. With recent technical interconnection requirements for smart inverters standardized since 2016, there is an expectation that hosting capacity will be expanded. With the anticipated proliferation of solar generators, it has become imperative to understand their impact so that accommodating measures, if required, can be employed. Researchers and utility engineers have devoted efforts in understanding all facets in integrating these resources. This paper presents measurements and performance and conformance analysis of a commercial scale solar inverter. Currently, this inverter is in operation in the soon-to-be-completed microgrid laboratory constructed by a Canadian electrical utility. This research suggests that, despite their power electronics topologies and sophisticated switching control, these inverters are expected to have very small negative impact on the distribution system voltage supply characteristics.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.002
GPT teacher head0.153
Teacher spread0.151 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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