Evidence map›Paper›PMID 39309776›Full record

ArticleHeliyon2024

Artificial immune systems (GA-AIS) enabled power loss mitigation in distributed generation: X3PAIS optimization approaches.

Oday A Ahmed, K H Chong, S P Koh, Chong Tak Yaw, Jagadeesh Pasupuleti

Abstract read
In one paragraph

Article in Heliyon, 2024. 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

5 authors.

Oday A AhmedDepartment of Electrical Engineering, University of Technology- Iraq, 35299, Baghdad, Iraq.
K H ChongInstitute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang, 43000, Selangor, Malaysia.
S P KohInstitute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang, 43000, Selangor, Malaysia.
Chong Tak YawInstitute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang, 43000, Selangor, Malaysia.
Jagadeesh PasupuletiInstitute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang, 43000, Selangor, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The distribution network is a crucial component of the power system, with industrialization driving increased energy demand. Traditional power-generating techniques, such as thermal and hydroelectric are not enough to meet this demand, leading to the development of Distributed Generation (DG). DG requires an extensive re-evaluation of the current power system, as it modifies energy losses and line flows. Inadequate integration of DG can cause power outages, disruption of protection coordination, and lead to islanding. AI can help overcome this issue by determining the best system architecture. Researchers have been interested in the Artificial Immune System (AIS) algorithm, which has room for development and lacks a fixed template. In order to improve AIS, X3PAIS, a hybridization strategy that combines clonal selection with a three-parent crossover has been developed within the scope of the study. X3PAIS was pre-tested using applications in a planetary gear train, a wastewater treatment facility, and mathematical calculations, showcasing its robustness and versatility. In the context of power distribution, X3PAIS is used in the multiple DG architecture of the power distribution system, reducing power losses by placing DG units in the best locations and sizing them to match load profiles. The four DGs' experiment results show that X3PAIS can minimize power losses by more than 89 %. To optimize power losses in the power distribution system, X3PAIS may be improved with a three-parent multiple-point crossover operation.

Indexed as

Artificial immune systemDistribution generationGenetic algorithmPower loss reduction

Identifiers

PMID39309776
PMCPMC11415675

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.