Comparative learning global particle swarm optimization for optimal distributed generations' output

Jamian, Jasrul Jamani; Mokhlis, Hazlie; Mustafa, Mohd Wazir; Abdullah, Mohd Noor; Baharudin, Muhammad Ariff

doi:10.3906/elk-1212-173

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…PSO and imperialist competitive algorithm (ICA) were chosen for comparison. PSO was chosen because it is a very common and widely used algorithm in various applications since the past [32,33]. ICA is another recent optimisation method which was introduced in 2007 [34].…”

Section: Results Comparison With Other Optimisation Techniques and Pamentioning

confidence: 99%

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Illias

Chan

Mokhlis

2020

IET Generation, Transmission & Distribution

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Section: Results Comparison With Other Optimisation Techniques and Pamentioning

confidence: 99%

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Illias

Chan

Mokhlis

2020

IET Generation, Transmission & Distribution

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“…In MPSO-TVAC, the main function of this technique is to avoid search form converging prematurely and to enhance the convergence rate to the optimum global solution at the later search stage [ 39 – 41 ]. A new term rb is added into the velocity equation of PSO to improve the PSO robustness by providing additional information for every particle, where rb is selected from other particles’ pb randomly.…”

Section: Particle Swarm Optimisation Techniquesmentioning

confidence: 99%

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

Illias

Liang

2018

PLoS ONE

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Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

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“…Therefore, in this work, a method to obtain an optimum design of corona ring dimensions, which yields the possible lowest electric field magnitude along the insulator string, is proposed using optimisation techniques. Optimisation involves the process of searching for the best solution among all feasible solutions (Rashedi et al, 2011;Ramadhani et al, 2017;Dreidy et al, 2017;Abdullah et al, 2015;Jamian et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimisation of corona ring design for composite insulator strings

Aramugam

Illias

Ching

2019

COMPEL

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Purpose The purpose of this paper is to propose an optimum design of a corona ring for insulator strings using optimisation techniques, which are gravitational search algorithm (GSA) and imperialist competitive algorithm (ICA). Design/methodology/approach An insulator string model geometry with a corona ring was modelled in a finite element analysis software, and it was used to obtain the electric field distribution in the model. The design was optimised using GSA and ICA. The variables were the corona ring diameter, ring tube diameter and vertical position of the ring along the insulator string. Findings Using optimisation method, the minimum electric field magnitude on the insulator string with a corona ring design is lower than without using optimisation method. GSA yields better results than ICA in terms of the optimised corona ring design. Practical implications The proposed methods can help in improvement of corona ring design in reducing the electric field magnitude on the energised end of insulator strings. Originality/value A new method to design an optimum corona ring for insulator strings, which is using optimisation methods, has been developed in this work.

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Comparative learning global particle swarm optimization for optimal distributed generations' output

Cited by 5 publications

References 25 publications

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

Optimisation of corona ring design for composite insulator strings

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