Evidence map›Paper›PMID 41917164›Full record

ArticleScientific reports2026

Intelligent MPPT-based energy management for hybrid renewable energy grids using trans Z-source quadratic boost converter.

Sangeetha Manickam, S Padma

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sangeetha ManickamDepartment of Electrical and Electronics Engineering, Sona College of Technology, Salem, Tamilnadu, India. m.sangeethasuresh@gmail.com.
S PadmaDepartment of Electrical and Electronics Engineering, Sona College of Technology, Salem, Tamilnadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing demand for renewable energy necessitates efficient power conversion and grid integration solutions. This work proposes an energy management system (EMS) using a trans Z-source quadratic boost converter (TZSQBC) and puffer fish optimized fuzzy neural network (PF-OFNN)-maximum power point tracking (MPPT). The system integrates photovoltaic (PV) and wind system to enhance reliability and mitigate variability issues. The TZSQBC significantly improves voltage boosting efficiency, ensuring seamless grid integration. The PF-OFNN MPPT algorithm optimizes power extraction from PV system under dynamic environmental conditions. A doubly fed induction generator (DFIG) wind system is incorporated to enhance flexibility and stability. Additionally, a bidirectional DC-DC converter with battery storage ensures efficient energy flow management. The proposed system is evaluated through MATLAB simulations under various test cases, including constant and varying environmental conditions, analysing power efficiency, voltage gain, and total harmonic distortion (THD). A hardware prototype further validates system feasibility, demonstrating effective grid synchronization and improved power quality. Comparative analysis with existing topologies highlights reduced component count, improved duty cycle utilization, and lower voltage stress. The results confirm the proposed system's effectiveness in achieving stable, high-efficiency renewable energy integration with enhanced power management and minimal harmonic distortion.

Indexed as

DFIGHybrid renewable energy system (HRES)PF-OFNN MPPTPV systemTZSQBCWind energy conversion system (WECS)

Identifiers

PMID41917164
PMCPMC13187137

What OpenQuestion holds

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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 OpenQuestion graph.