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Record W4395961400 · doi:10.18280/jesa.570216

Optimizing Energy Management of Hybrid Battery-Supercapacitor Energy Storage System by Using PSO-Based Fractional Order Controller for Photovoltaic Off-Grid Installation

2024· article· en· W4395961400 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2024
Typearticle
Languageen
FieldEngineering
TopicFrequency Control in Power Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSupercapacitorPhotovoltaic systemEnergy storageGridBattery (electricity)Energy managementComputer scienceController (irrigation)Computer data storageBattery storageAutomotive engineeringEnergy (signal processing)Electrical engineeringEngineeringComputer hardwarePower (physics)PhysicsMathematicsElectrode

Abstract

fetched live from OpenAlex

The integration of hybrid energy sources, such as batteries and supercapacitors, in off-grid photovoltaic installations is of crucial importance.This method promotes energy autonomy, offers operational flexibility, compensates for fluctuations in solar production, and can result in long-term economical savings.It also allows for optimized energy management through efficient storage and redistribution.This work details the design and simulation of a self-sufficient solar system that uses supercapacitors and batteries as part of a hybrid energy storage system.Recognizing the increasing significance of efficient energy systems, this study addresses the importance of such installations in delivering sustainable energy solutions.The FOPI-PSO controller optimized using the Particle Swarm Optimization (PSO) technique; demonstrates greater flexibility with a greater number of parameters, surpassing the adaptability of the conventional PI controller.By using multiple simulation scenarios that take into consideration both variations in load and irradiance, the study compares the effectiveness of both controllers in terms of synchronizing batteries and supercapacitors.The results demonstrate the PSO-based FOPI control strategy's outstanding performance, showing its optimal efficiency and robustness.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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