Evidence map›Paper›PMID 41667728›Full record

ArticleScientific reports2026

Direct preference optimization-based adaptive control for minimizing total harmonic distortion in photovoltaic-powered electric drives.

R K Ragavapriya, Maruthupandi Perumal

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

R K RagavapriyaDepartment of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore, India. r.k.ragavapriya123@gmail.com.
Maruthupandi PerumalDepartment of Electrical and Electronics Engineering, Government College of Technology, Coimbatore, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to reduce Total Harmonic Distortion (THD) in electric drives that are driven by photovoltaic (PV) systems, this study suggests a unique Direct Preference Optimization-based Photovoltaic Voltage Control (DPO-PVC) framework. In order to simulate realistic operating circumstances, framework combines dynamic PV modelling, inverter management, and electric motor simulation with real-world solar irradiance data from NREL PVDAQ dataset. DPO approach, in contrast to conventional scalar loss-based techniques, adapts robustly to nonlinear, noisy, and time-varying environments by learning from preference rankings across controller configurations. efficiency of suggested controller is shown by extensive simulations conducted under various irradiance profiles, including clear, cloudy, and transient weather situations, as well as dynamic load scenarios. According to quantitative studies, DPO-PVC outperforms PID, fuzzy-PID, and genetic algorithm-optimized PID (GA-PID) controllers by up to 60%, achieving a voltage THD as low as 2.88%. Likewise, current THD drops to 2.57%, guaranteeing better motor current quality. Additionally, controller has a smaller speed regulation error (1.49%) and a faster settling time (180 ms), which enhances system stability and dynamic response. Achieving 94.6% energy efficiency highlights best use of solar electricity that is available. Further tests demonstrate DPO-PVC controller’s practical usefulness by demonstrating that it retains performance robustness against irradiance changes, abrupt load surges, and sensor noise. efficacy of choice-based training is confirmed by learning convergence metrics, which show that preference accuracy exceeds 95% after 50 epochs. All things considered, DPO-PVC framework offers a noteworthy development in PV-powered electric drive control by successfully lowering harmonic distortion while boosting efficiency and stability. This method has a lot of promise for practical renewable energy systems that need high-fidelity, adaptive control in a variety of load and environmental scenarios.

Indexed as

Direct preference optimizationElectric motor simulationEnergy efficiencyMotor qualityPhotovoltaic voltage controlTotal harmonic distortion

Identifiers

PMID41667728
PMCPMC12963433

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.