Bare Conductor Temperature Coefficient Identification by Means of Differential Evolution Algorithm

Sarajlić, Mirza; Pocajt, Marko; Kitak, Peter; Sarajlić, Nermin; Pihler, J.

doi:10.1007/978-3-030-02574-8_25

Cited by 3 publications

(6 citation statements)

References 2 publications

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“…Particle Swarm Optimization (PSO) [17], Genetic Algorithm [18], or other, could be used in the optimization process. This paper used the DE, a stochastic optimization algorithm, which is suitable for solving of nonlinear and constrained real-life optimization problems in Engineering [19]- [25].…”

Section: A Research Objectivesmentioning

confidence: 99%

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Identification of the Thermal Parameters for the NH gG Fuse Using the Differential Evolution

et al. 2019

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This paper describes the identification of the thermal parameters for the high breaking capacity low-voltage NH gG fuse. For the process of parameter's determination, a 3D numerical model of the fuse is used together with the measured temperature on the fuse and differential evolution (DE) as the powerful optimizer for optimization problems. DE's main task is to reduce the difference between the measured and calculated fuse temperatures. The classic DE algorithm is compared with three improved DE algorithms that have a reduction of the population size during the optimization process. The results of all four algorithms are compared, and the most favorable choice of algorithm is given for such temperature coefficient calculations. The fuse model is appropriate for the calculation of the fusing temperature, according to the excellent agreement between the measured and calculated fuse temperatures.

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Section: A Research Objectivesmentioning

confidence: 99%

“…3. Conduction is the transfer of heat in solid materials with direct contact, and convection is the transfer of heat to the surrounding space [31]. The numerical analysis of temperature calculation for the fuse model has been conducted with the catalogue parameter values from Table 3.…”

Section: Fuse Modelmentioning

confidence: 99%

Identification of the Thermal Parameters for the NH gG Fuse Using the Differential Evolution

et al. 2019

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“…DE is a fast and robust population-based direct-search stochastic optimization algorithm, that was first introduced by Storn and Price [27]. This algorithm is popular with the engineering audience [20][21][22][24][25][26]. It is considered to be one of the best stochastic optimization methods for solving real-life engineering problems due to its satisfactory properties.…”

Section: Differential Evolutionmentioning

confidence: 99%

“…Any optimization method such as Partical Swarm Optimization (PSO) [18], genetic algorithm [19] or other, could be used in the optimization process. In this paper the DE, a stochastic optimization algorithm, which has proved to be very suitable for solving of nonlinear and constrained real life optimization problems in engineering [20][21][22][23][24][25][26][27], has been used. The goal of the DE algorithm is to reach the best possible agreement between the measured and heat equation calculated conductor temperature time behaviours under dynamic operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Identification of the Heat Equation Parameters for Estimation of a Bare Overhead Conductor’s Temperature by the Differential Evolution Algorithm

et al. 2018

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This paper deals with the Differential Evolution (DE) based method for identification of the heat equation parameters applied for the estimation of a bare overhead conductor`s temperature. The parameters are determined in the optimization process using a dynamic model of the conductor; the measured environmental temperature, solar radiation and wind velocity; the current and temperature measured on the tested overhead conductor; and the DE, which is applied as the optimization tool. The main task of the DE is to minimise the difference between the measured and model-calculated conductor temperatures. The conductor model is relevant and suitable for the prediction of the conductor temperature, as the agreement between measured and model-calculated conductor temperatures is exceptional, where the deviation between mean and maximum measured and model-calculated conductor temperatures is less than 0.03 °C.

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“…DE is a fast and robust population-based direct-search stochastic optimization algorithm that was first introduced by Storn and Price [5]. This algorithm is widespread among engineering audiences [2,3,[13][14][15][16][17] due to its robustness in reaching global minima, suitable for solving nonlinear and constrained optimization problems. It requires only boundaries of expected solutions and has only a few control parameters to be defined.…”

Section: Best External Shape and The Position Of The Internal Mvipi'smentioning

confidence: 99%

Electric Field of a Medium Voltage Indoor Post Insulator

Sarajlić¹,

Pihler²,

Sarajlić³

et al. 2018

Electric Field

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This chapter deals with the influence of the electric field on a Medium Voltage Indoor Post Insulator (MVIPI) with standard and modified external shapes. The goal of this chapter is to show the electric field behavior of the MVIPI with different external shapes and to introduce a numerical model with a favorable distribution of the electric field that relieves the dielectric from stress. The chapter describes an MVIPI with nominal voltage 20 kV AC and shows an existing MVIPI, an MVIPI with a different number of ribs, and an MVIPI with exceptional external shape. The MVIPI's new external shape does not have the typical shape of the ribs, but a new outline, using the Lagrange polynomial, that will acquire its optimal form through the optimization process. A Differential Evolution optimization algorithm is used for the optimal design of the insulator's external shape. The value of the Electric Field Strength (EFS) will be minimized within the permissible bounds during the optimization process. The important parameters during the minimization of the objective function are the value of the EFS in the interior and exterior of the insulator. EFS values are shown for every MVIPI example and are compared with the existing MVIPI. The obtained results are analyzed and discussed.

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Bare Conductor Temperature Coefficient Identification by Means of Differential Evolution Algorithm

Cited by 3 publications

References 2 publications

Identification of the Thermal Parameters for the NH gG Fuse Using the Differential Evolution

Identification of the Thermal Parameters for the NH gG Fuse Using the Differential Evolution

Identification of the Heat Equation Parameters for Estimation of a Bare Overhead Conductor’s Temperature by the Differential Evolution Algorithm

Electric Field of a Medium Voltage Indoor Post Insulator

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