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Enhancing voltage stability in active distribution networks through solar PV integration

2025· article· en· W4410988650 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

VenueInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsPhotovoltaic systemStability (learning theory)Environmental scienceEngineering physicsMaterials scienceComputer scienceElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Solar PV's explosive expansion is changing distribution networks and posing new problems, such as bidirectional power flow, unstable voltage, and power quality problems, particularly in networks with low X/R ratios. Abrupt changes in voltage are difficult for conventional voltage control techniques like shunt capacitors and on-load tap changers (OLTCs) to handle. IEEE Standard 1,547 has little efficacy in such networks, despite the fact that PV inverters may provide reactive power. This paper suggests a real-time coordinated control approach to improve voltage regulation by combining PV inverters, OLTC, and battery energy storage systems (BESS). Reactive power from PV inverters is prioritized to lower operational expenses and reliance on BESS. Better voltage stability, a decrease in BESS energy processing from 9400.3 kWh to 1701.87 kWh, and a reduction in OLTC activities are the outcomes. Rural networks gain from the strategy's ability to support smaller, more affordable BESS units’ voltage sensitivity analysis, and ideal BESS sizing may be investigated in future studies.

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 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: none
Teacher disagreement score0.778
Threshold uncertainty score0.914

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 communication0.0000.001
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 head0.003
GPT teacher head0.211
Teacher spread0.208 · 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