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Record W4407825864 · doi:10.1109/jestie.2025.3544377

Series-Cascaded Islanded Microgrids Comprising Storage: Sensorless Control Under PV Partial Shading and Load Variation

2025· article· en· W4407825864 on OpenAlexaff
Khalil Saad A. Algarny, D. Mahinda Vilathgamuwa, Ahmed Sheir, Dezső Séra

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Industrial Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsAlchemy (Canada)
FundersTaif UniversityQueensland University of Technology
KeywordsShadingControl theory (sociology)Photovoltaic systemVariation (astronomy)Control (management)MicrogridSeries (stratigraphy)Computer scienceEngineeringPhysicsElectrical engineeringBiology

Abstract

fetched live from OpenAlex

This article extends the use of voltage and current estimators, typically used in conventional systems, to a master–slave distributed generator (DG) configuration. The proposed method eliminates the need for expensive dc-side voltage and current sensors necessary to maximum power point tracking of the connected photovoltaic (PV) modules. Moreover, in medium- and high-power applications, this method can provide a valuable substitute to one or more faulty current or voltage sensors, which ensures system continuous operation while locating the faulty sensor(s). The introduced method does not affect the PV normal operation even in the presence of partial shading, battery charging/discharging, and fluctuating loads. The introduced method utilizes voltage and current estimators that rely on the ac-side measurements and a proposed switching function to isolate and calculate the voltage and current of each connected DG. The structure and the design details of these estimators are discussed in detail. The simulation and practical results conducted using the series-connected converter configured by the master (battery)–slave (PV) DG proved the validity of the proposed method in maintaining normal operation below 2% estimation error.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.783

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 integrity0.0000.001
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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
Published2025
Admission routes1
Has abstractyes

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