An improved gravitational search algorithm combining with centripetal force

Liu, Yongli; Zhang, Xiaoyang; Hao, Chao

doi:10.1016/j.padiff.2022.100378

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Thus, updating them enhances the response and adaptability to confront various transient disturbances. The advantage of droop control is that it enables power sharing without necessitating a communication link, thereby conferring flexibility and autonomy [25]. As shown in Figure 4, the droop curves demonstrate the deviation after each disturbance.…”

Section: Inverter Mathematical Modelmentioning

confidence: 99%

Droop Control Optimization for Improved Power Sharing in AC Islanded Microgrids Based on Centripetal Force Gravity Search Algorithm

Taha,

Kurnaz

2023

Energies

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The urgent demand for clean and renewable energy sources has led to the emergence of the microgrid (MG) concept. MGs are small grids connecting various micro-sources, such as diesel, photovoltaic wind, and fuel cells. They operate flexibly, connected to the grid, standalone, and in clusters. In AC MG control, a hierarchical system consists of three levels: primary, secondary, and tertiary. It monitors and ensures MG stability, power quality, and power sharing based on the specifications of governing protocols. Various challenging transient disturbances exist, such as generator tripping, secondary control failure due to communication delay, and drastic load changes. Although several optimal power sharing methods have been invented, they pose complex control requirements and provide limited improvement. Therefore, in this paper, a novel optimized droop control is proposed using a metaheuristic multi-objective evolutionary algorithm called the Centripetal Force-Gravity Search Algorithm (CF-GSA) to improve the droop performance of power sharing, voltage and frequency stability, and power quality. CF-GSA is an improved algorithm designed to address the issue of local solutions commonly encountered in optimization algorithms. The effectiveness and superiority of the proposed method are validated through a series of simulations. The results of these simulations show that the proposed multi-objective optimization droop control method works well to fix problems caused by power sharing errors in isolated AC microgrids that have to deal with high inductive loads and changes in line impedance.

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Section: Inverter Mathematical Modelmentioning

confidence: 99%

Droop Control Optimization for Improved Power Sharing in AC Islanded Microgrids Based on Centripetal Force Gravity Search Algorithm

Taha,

Kurnaz

2023

Energies

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“…The algorithm has been one of the popular optimization algorithms that has been adopted by the researchers [1]. GSA has been reported to have the capability to solve highly nonlinear and complex engineering optimization problems effectively [2][3][4]. Based on literatures, GSA has demonstrated better performance in solving optimization problems such as for constrained, unconstrained, continuous, discrete and multi objective optimizations [5,6].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Gravitational Search Algorithm Based on Improved Convergence Strategy

Sabri¹,

Bahrin²,

Puteh³

2023

IJACSA

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Gravitational search algorithm (GSA) is one of the metaheuristic algorithms that has been popularly implemented in solving various optimization problems. The algorithm could perform better in highly nonlinear and complex optimization problems. However, GSA has also been reported to have a weak local search ability and slow searching speed to achieve its convergence. This research proposes two new parameters in order to improve GSA's convergence strategy by improving its exploration and exploitation capabilities. The parameters are the mass ratio and distance ratio parameters. The mass ratio parameter is related to the exploration strategy, while the distance ratio parameter is related to the exploitation strategy of the enhanced GSA (eGSA). These two parameters are expected to create a good balance between the exploration and the exploitation strategies in eGSA. There are seven benchmark functions that have been tested on eGSA. The results have shown that eGSA has been able to produce good performance in the minimization of fitness values and execution times, compared with two other GSA variants. The testing results have shown that the enhancements made to GSA have successfully improved the algorithm's convergence strategy. The improved convergence has also been able to improve the algorithm's solution quality and the processing time. It is expected that eGSA could be applied in many fields and solve various optimization problems efficiently.

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