Optimizing Over-Current Relay Coordination: The Role of Entropy Evolution in Gravitational Search Strategy

Power protection schemes are essential to ensure the reliability of power system grids. This research focuses on improving coordination between two protection zones in power systems to enhance the system performance. Traditional optimization methods, such as Particle Swarm OptimizationGravitational Search Algorithm (PSO-GSA) and Fractional Particle Swarm Optimization-Gravitational Search Algorithm (F-PSO-GSA), face challenges like slow convergence and sensitivity to tuning parameters. To overcome these issues, this study introduces a hybrid PSO-GSA with Entropy Evolution (PSO-GSA-EE) algorithm. The proposed method is tested on two different IEEE test systems, that confirms improved optimization efficiency, faster convergence, and minimizing operating time upto 12 %, compared to the state-of-the-art techniques in literature.

Authors

Institutions

Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/pedg62294.2025.11060368
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing Over-Current Relay Coordination: The Role of Entropy Evolution in Gravitational Search Strategy

Solmaz Kahourzade, Ladlay Khan, Muhammad Arshad Javed, Amin Mahmoudi et al.
Complex Systems and Time Series Analysis
article

Optimizing Over-Current Relay Coordination: The Role of Entropy Evolution in Gravitational Search Strategy

Solmaz Kahourzade, Ladlay Khan, Muhammad Arshad Javed, Amin Mahmoudi, Yasir Muhammad, Ahsan Zafar
article en

Abstract

Power protection schemes are essential to ensure the reliability of power system grids. This research focuses on improving coordination between two protection zones in power systems to enhance the system performance. Traditional optimization methods, such as Particle Swarm OptimizationGravitational Search Algorithm (PSO-GSA) and Fractional Particle Swarm Optimization-Gravitational Search Algorithm (F-PSO-GSA), face challenges like slow convergence and sensitivity to tuning parameters. To overcome these issues, this study introduces a hybrid PSO-GSA with Entropy Evolution (PSO-GSA-EE) algorithm. The proposed method is tested on two different IEEE test systems, that confirms improved optimization efficiency, faster convergence, and minimizing operating time upto 12 %, compared to the state-of-the-art techniques in literature.

COMSATS University Islamabad (PK), Tianjin University (CN), Flinders University (AU), University of South Australia (AU)
Openalex Percentile: Top 76%
Complex Systems and Time Series Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.