A new multiobjective fuzzy shuffled frog-leaping algorithm for optimal reconfiguration of radial distribution systems in the presence of reactive power compensators

Dehnavi, Hossein Dehghan; Esmaeili, Saeid

doi:10.3906/elk-1109-24

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Compared with HBB-BC [13] and hybrid PSO (HPSO) [26], the 𝑃 𝑙𝑜𝑠𝑠 reduction and 𝑉 𝑚𝑖𝑛 of AEO are worse than those of two mentioned method but the LUBI index and the number of switch operations of AEO is better than those of HBB-BC and HPSO. Meanwhile, the minimum voltage and the number of switch operations of AEO are better performance than those of SFLA [14]. This confirms that AEO is an effective approach for the single as well as the multi-goal ENRP.…”

Section: Resultssupporting

confidence: 56%

“…For case 1, the optimal configuration obtained by AEO is the same with MPSO [1], RRA [16], HSA [6], PSO [5], HBB-BC [13] and IWO [15]. Compared with FWA [7], ant colony algorithm (ACO) [25] and SFLA [14], the minimum voltage of these methods are higher than that of AEO but their reduction of 𝑃 𝑙𝑜𝑠𝑠 is lower than that of AEO. For the case 4, the 𝑃 𝑙𝑜𝑠𝑠 reduction, minimum voltage and LUBI index obtained by AEO are worse than those of RRA [16] and IWO [15] but the number of switch operations of AEO is less than that of RRA and IWO.…”

Section: Resultsmentioning

confidence: 99%

“…In [13], power losses, voltage deviation and feeder load balancing are optimized by using hybrid big bangbig crunch algorithm (HBB-BC). Similarly, these objectives are optimized by shuffled frog-leaping algorithm (SFLA) [14]. In [15], power loss, voltage deviation, switching operations and load balancing are considered by using invasive weed optimization (IWO).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Metaheuristic Method for Multi-Goal Electric Distribution Network Reconfiguration

2022

IJIES

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This paper demonstrates an artificial ecosystem optimization (AEO) for the multi-goal electric network reconfiguration (ENR) problem (ENRP). The membership goal functions of the ENRP comprise power loss reduction, voltage deviation reduction, reduction of load unbalance index among branches and reduction of number of switch operations. To show the advantages of AEO for the multi-goal ENRP, the multi-goal ENR approaches relied on particle swarm optimization (PSO), genetic algorithm (GA) and cuckoo search algorithm (CSA) are applied to contrast with the AEO method. The result comparisons among methods on the 33-node distribution network show that the AEO method can archive the optimal solution with the higher success rate and the lower average value and standard deviation of the fitness values than GA and PSO. In addition, the result comparisons with other previous approaches also show the reliability of the proposed AEO method for the single and multi-goal function ENRP. Therefore, the AEO can be an effective and useful approach for the ENRP to optimize the single and the multi-goal functions.

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Section: Resultssupporting

confidence: 56%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Metaheuristic Method for Multi-Goal Electric Distribution Network Reconfiguration

2022

IJIES

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“…From the table, the feeder balance (FB) is not evaluated due to taking power from only a feeder of this system. Compared with Fuzzy-SFL [44], P loss and LB gained by IMSA is 5.023 kW and 0.0016 higher than those of Fuzzy-SFL but V min obtained by IMSA is 0.0018 higher than that of Fuzzy-SFL and number of switching change (NSC) obtained by IMSA is 4 less than that of Fuzzy-SFL. e P loss , V min and LB obtained by IMSA are as few improvement as those of MOIWO [32] but NSC obtained by IMSA is 4 and 2 less than that of MOIWO.…”

Section: E First Testmentioning

confidence: 82%

Network Reconfiguration and Distributed Generation Placement for Multi-Goal Function Based on Improved Moth Swarm Algorithm

Nguyen

Duong

Ngo

2022

Mathematical Problems in Engineering

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This paper demonstrates an effective method of performing network reconfiguration (NR) and distributed generation placement (DGP) simultaneously called NR-DGP for the multi-goal function of reducing power loss and voltage deviation as well as enhancing load balance and feeder balance with the least number of changing switches. These membership goals are combined by the fuzzy decision-making relied on the max-min technique. The results for the NR and NR-DGP on two test system including of 33 and 84 nodes are gained by the improved moth swarm algorithm (IMSA). Wherein, IMSA is developed based on the original moth swarm algorithm (MSA) with improvement of the exploration mechanism using Lévy distribution. The calculated results show that NR-DGP method has gained the better improvement of the test systems’ technical indicators than the NR only. The obtained results also show that IMSA has better performance for the NR and NR-DGP problems than the MSA in term the quality of the obtained optimal solution. Thus, IMSA can be an effective approach for determining the optimal solution of the NR and NR-DGP problems.

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Simultaneous feeder reconfiguration, DSTATCOM allocation, and sizing using seagull optimization algorithm in unbalanced radial distribution systems

Samal,

Panigrahy

2023

Soft Comput

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A new multiobjective fuzzy shuffled frog-leaping algorithm for optimal reconfiguration of radial distribution systems in the presence of reactive power compensators

Cited by 6 publications

References 35 publications

A Novel Metaheuristic Method for Multi-Goal Electric Distribution Network Reconfiguration

A Novel Metaheuristic Method for Multi-Goal Electric Distribution Network Reconfiguration

Network Reconfiguration and Distributed Generation Placement for Multi-Goal Function Based on Improved Moth Swarm Algorithm

Simultaneous feeder reconfiguration, DSTATCOM allocation, and sizing using seagull optimization algorithm in unbalanced radial distribution systems

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