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AI-driven ANN–PSO smart-inverter capacity allocation at preselected buses under meteorological uncertainty
Department of Mechanical Engineering, National Polytechnic School of Constantine, Constantine, Algeria.
QUERE Laboratory, Department of Electrical Engineering, Ferhat Abbas University, Setif, Algeria.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-1814-4278
Department of Electromechanics, Faculty of Construction Engineering, University of Tizi ouzzou UMMTO, Tizi Ouzou, Algeria.
2026 (English)In: Frontiers in Artificial Intelligence, E-ISSN 2624-8212, Vol. 9, article id 1896551Article in journal (Refereed) Published
Abstract [en]

Introduction: 

Radial distribution grids require renewable planning methods that jointly account for meteorological generation, WT/PV capacity allocation, and smart-inverter voltage support.

Methods: 

An ANN-PSO-VoltVAR framework was developed using 2012-2021 NASA POWER irradiance, wind-speed, and temperature data for In Salah, Algeria. Renewable uncertainty was represented by 12 stratified Weibull-Beta scenarios and an expected-cost/CVaR objective. The mixed decision vector optimized WT units, PV strings, and voltage-dependent inverter support at four preselected IEEE 85-bus locations. An exact backward/forward-sweep module on the standard 33-bus feeder independently verified the load-flow implementation.

Results: 

The tuned ANN achieved an RMSE of 1.6323 kW, an MAE of 1.2314 kW, and an R-squared value of 0.99999. The verified five-start mean-profile solution reduced mean active loss to 15.9900 kW and voltage deviation to 0.051082 p.u., while increasing minimum voltage to 0.98213 p.u. and minimum VSI to 0.96307. Relative to IFLO, the respective improvements were 54.57%, 22.49%, 1.21%, and 8.41%.

Discussion: 

The ablation analysis showed that capacity sizing accounted for most of the loss reduction, whereas Volt-VAR support produced the clearer additional voltage-security gains. Because the IEEE 85-bus electrical response is benchmark-calibrated rather than fully reconstructed, the findings should be interpreted as a reproducible proof-of-concept. Direct full-feeder validation on the IEEE 85-bus system and other networks remains necessary.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2026. Vol. 9, article id 1896551
Keywords [en]
artificial neural network, particle swarm optimization, radial distribution grid, renewable capacity allocation, scenario-based uncertainty, smart inverter, voltage stability index, Volt–VAR control
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Other Environmental Engineering
Research subject
Automatic Control
Identifiers
URN: urn:nbn:se:ltu:diva-119558DOI: 10.3389/frai.2026.1896551OAI: oai:DiVA.org:ltu-119558DiVA, id: diva2:2096254
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Fulltext license: CC BY;

Available from: 2026-08-28 Created: 2026-08-28 Last updated: 2026-08-28Bibliographically approved

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Ghoul, Abdelhamid

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