Performance of different kernel functions for LS‐SVM‐GWO to estimate flashover voltage of polluted insulators

Bessedik, Sid Ahmed; Djekidel, Rabah; Ameur, A.

doi:10.1049/iet-smt.2017.0486

Cited by 16 publications

(17 citation statements)

References 35 publications

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“…LS-SVM is based on concepts of machinery learning. For that we establish the model on two steps:  training  test In the first step, we consider a given training set [15][16][17][18][19][20] as follows:…”

Section: Least Squares Support Vector Machinesmentioning

confidence: 99%

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Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

M'hamed¹,

Taibi²,

Bessedik³

et al. 2020

Diagnostyka

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Section: Least Squares Support Vector Machinesmentioning

confidence: 99%

“…We need to search solutions of partial derivatives with respect to w, b, ei and αi which give [17][18][19][20]:…”

Section: Least Squares Support Vector Machinesmentioning

confidence: 99%

Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

M'hamed¹,

Taibi²,

Bessedik³

et al. 2020

Diagnostyka

Self Cite

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“…However, it employs a complex methodology. In terms of successful applications of the GWO, representative application research can be summarized as flow shop scheduling [32], machine learning [33][34][35][36], economic load dispatch [37], robotics and path planning [38,39], channel estimation in wireless communication systems [40], and other applications detailed in References [24,25]. eoretical and practical research has shown the potential of the GWO algorithm in real life.…”

Section: Introductionmentioning

confidence: 99%

Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

Jia

Yun

2019

Complexity

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The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders). This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO). In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm. The experimental results for 23 general benchmark functions and 3 well-known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm. The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.

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“…The insulator contour design is of prime importance as well, in order to have minimal tangential force on the insulator [6]. Genetic algorithm is used for contour optimization of a suspension type insulator with the aim to Manuscript received 5 December, 2018; accepted 8 April, 2019. minimize the electrical stress [7], [8]. For operation of composite insulators the electric stress on the surface of the insulator is a major factor [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Hybrid K Based Influential Parameter Determination and Design Optimization of 220 kV High Voltage Insulator

Goswami¹,

Sadhu²,

Sadhu³

2019

ELEKTRON ELEKTROTECH

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Insulator failure generally occurs due to improper design, and if there exists sharp edges over the insulator surface. There is a need to detect the fragile parameters in the insulator design as they can cause large electrical stress, leading to insulation failure. In this paper, machine learning based approach is used to identify the fragile design parameter and thus determine an optimized design of the insulator, which will eventually decrease the voltage stress over it. A 220 kV insulator is designed and electric field intensity and potential distribution for an assembly of live, ground and insulator is calculated for different geometries. The design parameters of the insulator are considered and varied. The electric field is calculated for the whole assembly of insulator to generate the most optimized design. The optimized design reduces the overall stress by 13.12 %.

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Performance of different kernel functions for LS‐SVM‐GWO to estimate flashover voltage of polluted insulators

Cited by 16 publications

References 35 publications

Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

Hybrid K Based Influential Parameter Determination and Design Optimization of 220 kV High Voltage Insulator

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