Application of Powell’s optimization method to surge arrester circuit models’ parameters

Vita, Vasiliki; Ekonomou, Lambros; Chatzarakis, George E.; Stathopulos, I.A.

doi:10.1016/j.energy.2010.04.023

Cited by 16 publications

(16 citation statements)

References 5 publications

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“…Added to the above analysis, the outcomes of the harmony search algorithm are compared with results obtained from methodologies presented in previous works [15][16][17], i.e., the downhill simplex method [15], the Powell method [16] and a genetic algorithm (GA) [17] (Table 3). In Cases 2 and 3, the simplex and the Powell methods cannot be successfully applied, since both are trapped in local minima.…”

Section: Resultsmentioning

confidence: 99%

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Adjusting the Parameters of Metal Oxide Gapless Surge Arresters’ Equivalent Circuits Using the Harmony Search Method

et al. 2017

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Abstract:The appropriate circuit modeling of metal oxide gapless surge arresters is critical for insulation coordination studies. Metal oxide arresters present a dynamic behavior for fast front surges; namely, their residual voltage is dependent on the peak value, as well as the duration of the injected impulse current, and should therefore not only be represented by non-linear elements. The aim of the current work is to adjust the parameters of the most frequently used surge arresters' circuit models by considering the magnitude of the residual voltage, as well as the dissipated energy for given pulses. In this aim, the harmony search method is implemented to adjust parameter values of the arrester equivalent circuit models. This functions by minimizing a defined objective function that compares the simulation outcomes with the manufacturer's data and the results obtained from previous methodologies.

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Section: Resultsmentioning

confidence: 99%

“…The precision of the outcomes of each model is very highly influenced by the adjustment of the parameter values. In response to these challenges, various iteration methods have been proposed [15][16][17][18][19][20][21] to compute the parameters that minimize the difference between the results of each model and the manufacturer's data.…”

Section: Introductionmentioning

confidence: 99%

Adjusting the Parameters of Metal Oxide Gapless Surge Arresters’ Equivalent Circuits Using the Harmony Search Method

et al. 2017

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“…Powell algorithm uses the successive approximate conjugate direction to search for the solution, which can quickly converge [10]. This algorithm is divided into several stages.…”

Section: Powell Conjugate Gradient Methodsmentioning

confidence: 99%

Optimization of Hydrogen-fueled Engine Ignition Timing Based on L-M Neural Network Algorithm

Wang

Liu

et al. 2016

TELKOMNIKA

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In view of the improvement measures of the optimization control algorithm for the ignition system of the hydrogen-fueled engine, the L-M neural network algorithm, Powell neural network algorithm and the Keywords: Hydrogen-fueled Engine, L-M Algorithm, Neural Network, OptimizationCopyright © 2016 Universitas Ahmad Dahlan. All rights reserved. IntroductionResearch on hydrogen as fuel of internal combustion engine began in the middle of nineteenth century. It fell behind for about 100 years compared with the research on traditional internal combustion engine. Although at this stage, the traditional gasoline engine and diesel engine technologies become more and more perfect and mature, but it's unable to directly use the traditional internal combustion engine for hydrogen combustion due to the particularity of hydrogen as a fuel. Its technology is not perfect and there is no commercial promotion. However, hydrogen is considered to be the most promising clean energy source in this century, with its advantages of low emission, renewable energy and so on. In the process of combustion, the ignition limit of hydrogen is wide, the combustion speed is high and the calorific value is high. This results pre-ignition, inlet backfire and other abnormal combustion phenomenon in the combustion process of internal combustion engine, so that the engine cannot operate normally [1]. How to avoid the abnormal combustion phenomenon without reducing the output power of the hydrogen-fueled engine is the research focus of the relevant researchers in various countries.Changwei, et al., have studied on the effect of hydrogen and methanol mixture combustion on the emission characteristics of hydrogen-fueled engine [2]. J.M. Gomes Antunes, et al., have studied on the influence of the high pressure direct injection technology on the hydrogen combustion characteristics of diesel engine, and the influence of homogeneous charge compression ignition technology and hydrogen injection timing and length on the performance of the hydrogen-fueled engine [3]. Xing-hua, Fu-shui Liu, et al., have studied the effects of hydrogen injection timing under different rotation speed and equivalence ratio on the formation of hydrogen mixture gas and its effect on the prevention of backfireby using computational fluid dynamics simulation [4]. The effect of EGR system on the combustion and performance of a compression ignition hydrogen-fueled engine was studied by Vinod Yadav Singh, et al., [5]. However, these studies are based on a large number of bench test or simulation process, and only a part of the operating conditions of the hydrogen-fueled engine characteristics are studied, which can't take into account the operation, emission and combustion characteristics of all the working conditions of the hydrogen-fueled engine.

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“…The accurate modeling and simulation of their dynamic characteristics is very important for arrester allocation, systems reliability assessment, and insulation coordination studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Proper voltage-current (V-I) characteristics, ignorable power losses, highlevel reliability in the operation time, high-speed response to the overvoltages, and long life time are some of the advantages of MOSAs.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the parameter estimation methods of surge arresters using modified particle swarm optimization algorithm

Nafar

Gharehpetian

Niknam

2011

Int Trans Elec Energy Syst

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SUMMARY The measurements show that the residual voltage of the metal oxide surge arrester (MOSA) has a dynamic characteristic. The accurate modeling and simulation of this dynamic characteristic is very important for arrester allocation, systems reliability, and insulation coordination studies. Several models with acceptable accuracy have been suggested to describe this behavior. It should be noted that the determination of nonlinear elements of MOSAs is very important for all models. In this article, a comparison among the different methods of the determination of the surge arrester voltage–current characteristics is represented. A new method, which is the combination of particle swarm optimization (PSO) and ant colony optimization methods, is proposed to estimate the parameters of MOSA models. In the proposed algorithm, to overcome the drawback of the PSO algorithm (convergence to local optima), to improve the global search capability, and to prevent the convergence to local minima, ant colony optimization algorithm is combined to PSO algorithm. The proposed method is called modified PSO. The transient models of MOSA have been simulated by using ATP‐EMTP. The results of simulations have been applied to the program, which is based on the modified PSO method and can determine the fitness and parameters of different models. The validity and the accuracy of the estimated parameters are assessed by comparing the predicted residual voltage with the experimental results. Copyright © 2011 John Wiley & Sons, Ltd.

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Application of Powell’s optimization method to surge arrester circuit models’ parameters

Cited by 16 publications

References 5 publications

Adjusting the Parameters of Metal Oxide Gapless Surge Arresters’ Equivalent Circuits Using the Harmony Search Method

Adjusting the Parameters of Metal Oxide Gapless Surge Arresters’ Equivalent Circuits Using the Harmony Search Method

Optimization of Hydrogen-fueled Engine Ignition Timing Based on L-M Neural Network Algorithm

Comparison of the parameter estimation methods of surge arresters using modified particle swarm optimization algorithm

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