Scipediacontent: Scipediacontent moved page Review 812058229139 to Balachandran et al 2026a

2026-05-04T08:51:37Z

Scipediacontent moved page Review 812058229139 to Balachandran et al 2026a

Scipediacontent: Scipediacontent moved page Draft content 869028803 to Review 812058229139

2026-05-04T08:32:18Z

Scipediacontent moved page Draft content 869028803 to Review 812058229139

Scipediacontent: Created page with " == Abstract ==
In recent times, meta-heuristic optimization techniques have become indispensable for effectively solving complex engineering problems involving multiple o..."

2026-05-04T08:32:16Z

Created page with " == Abstract == <p>In recent times, meta-heuristic optimization techniques have become indispensable for effectively solving complex engineering problems involving multiple o..."

New page

== Abstract ==

<p>In recent times, meta-heuristic optimization techniques have become indispensable for effectively solving complex engineering problems involving multiple objectives. The Grasshopper Optimization Algorithm (GOA) is a population-based approach inspired by the foraging behaviour of grasshopper swarms. However, the standard GOA may fail to escape sub-optimal solutions, as it does not account for the predator-prey strategy. To address this limitation, this study introduces a modified version of GOA, termed MGOA, which incorporates the predator-prey concept to enhance its ability to avoid local optima and achieve globally optimal solutions. The proposed MGOA is tested on two distinct electric power system challenges. The first optimization problem focuses on the optimal design of a solar system (SS) combined with an energy storage system (ESS) connected to DC bus of the shunt active power filter (SHAPF) to supply an EV charging station and harmonic loads to select filter and gain values of PID controller to minimize THD and maintain stable DC bus voltage (DCBV) to enhance the power quality (PQ) of local distribution network. In addition, to show the superiority of the MGOA, two different test cases were selected with varying loads and partial shading conditions in the solar system. The second optimization problem involves power system state estimation (SE). The goal of SE is realized by employing weighted least square (WELS) or weighted least absolute value (WELAV) criterions where the objective function is formed by minimizing the sum of squares of weighted deviations (SSWD), sum of absolute values of weighted deviations (SAVWD) of estimated measurements from actual measurements Finally, the results highlight the superior performance of MGOA when compared to traditional optimization methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). However, the proposed method reduces THD to 2.19% with 98% of successes rate with a lower DCBV settling time of 0.03 s while GOA, GA and PSO give that with 3.18%, 3.45%, 3.27% THD with 91%, 88% and 91% of successes rate with higher settling.OPEN ACCESS Received: 23/11/2025 Accepted: 03/02/2026</p>

== Document ==
<pdf>Media:Draft_content_869028803-6802-document.pdf</pdf>

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Balachandran et al 2026a - Revision history

Scipediacontent: Scipediacontent moved page Review 812058229139 to Balachandran et al 2026a

Scipediacontent: Scipediacontent moved page Draft content 869028803 to Review 812058229139

Scipediacontent: Created page with " == Abstract == In recent times, meta-heuristic optimization techniques have become indispensable for effectively solving complex engineering problems involving multiple o..."

Scipediacontent: Created page with " == Abstract ==
In recent times, meta-heuristic optimization techniques have become indispensable for effectively solving complex engineering problems involving multiple o..."