Calculating the leakage inductance for transformer inter-turn fault detection using finite element method

Ehsanifar, Ali; Dehghani, Mohammad; Allahbakhshi, Mehdi

doi:10.1109/iraniancee.2017.7985256

Cited by 40 publications

(34 citation statements)

References 8 publications

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“…Optimization algorithms with high power in solving optimization problems are always considered as one of the effective methods of solving optimization problems [1]. In this regard, optimization algorithms have been applied by scientists in various fields such as energy [2], protection [3], distribution systems [4,5], storage designing [6], electrical engineering [7,8], energy commitment [9], and energy carriers [10,11] algorithms with random search in problem solving space can provide a suitable solution for a problem [12]. Over the years, scientists have developed numerous optimization algorithms to solve optimization problems.…”

Section: Introductionmentioning

confidence: 99%

MLO: Multi Leader Optimizer

Dehghani¹,

Montazeri²,

Dehghani³

et al. 2020

IJIES

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Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

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Section: Introductionmentioning

confidence: 99%

MLO: Multi Leader Optimizer

Dehghani¹,

Montazeri²,

Dehghani³

et al. 2020

IJIES

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, they provide a self-organized system [51]. These algorithms have shown their high abilities to solve optimization problems in different sciences such as medical [52], energy [53,54], power engineering [55][56][57], protection [58], and energy carriers [59,60]. Even though many works have been done on these heuristic algorithms, more powerful ones are required to solve complex real-world optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Momentum search algorithm: a new meta-heuristic optimization algorithm inspired by momentum conservation law

Dehghani

Samet

2020

SN Appl. Sci.

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A novel optimization methodology, Momentum Search Algorithm (MSA) is presented based on Newton's laws: the law of conservation of momentum. It includes a set of masses in a closed system considering the conservation of momentum and kinetic energy of bodies. The possible solutions are presented by system bodies' positions in an n-dimensional space. The mass of bodies is proportional to their fitness function. Larger masses represent the better solutions. At each iteration, an external body collides separately with all solution bodies and moves them toward the optimum solution. The direction of the collision depends on the position of solution bodies and the position of the body with the best fitness function. As the better solutions have heavier bodies, the external body has less effect on their positions. On the other hand, the worse solutions are lighter and moved easily by the external body toward the better positions. The best position is achieved by allowing the external body to move the solution bodies toward better positions. The numerical results obtained from several standard benchmark test functions indicate the superiority of the proposed method over many other optimization techniques such as Genetic Algorithm,

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“…Due to the magnetic nonlinearity of the transformer's ferromagnetic core, those relations are nonlinear and, in fact, can be determined by solving the Maxwell equations only. Nowadays, software packages for solving electromagnetic fields allow us to effectively find the magnetic field distribution in 2D or 3D space, also combining the Maxwell and the Kirchhoff equations [8][9][10][11][12][13][14]. However, it is rather costand time-consuming and seems to be unsuitable for solving operating problems in systems with power transformers.…”

Section: Introductionmentioning

confidence: 99%

On Simplified Calculations of Leakage Inductances of Power Transformers

Sobczyk

Jaraczewski

2020

Energies

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This paper deals with the problem of the leakage inductance calculations in power transformers. Commonly, the leakage flux in the air zone is represented by short-circuit inductance, which determines the short-circuit voltage, which is a very important factor for power transformers. That inductance is a good representation of the typical power transformer windings, but it is insufficient for multi-winding ones. This paper presents simple formulae for self- and mutual leakage inductance calculations for an arbitrary pair of windings. It follows from a simple 1D approach to analyzing the stray field using a discrete differential operator, and it was verified by the finite element method (FEM) calculation results.

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Calculating the leakage inductance for transformer inter-turn fault detection using finite element method

Cited by 40 publications

References 8 publications

MLO: Multi Leader Optimizer

MLO: Multi Leader Optimizer

Momentum search algorithm: a new meta-heuristic optimization algorithm inspired by momentum conservation law

On Simplified Calculations of Leakage Inductances of Power Transformers

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