Estimation of the Equivalent Circuit Parameters in Transformers Using Evolutionary Algorithms (original) (raw)
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In this paper, a black box model has been proposed in order to analyze transformer transient behaviors. This model is capable to reveal the transformer impedance (or admittance) characteristics up to 1.2 MHz observed from transformer terminals under various connections. Also, this model is powerful to display the characteristic of traveling wave toward consumer. In this contribution, a 6300/420V, 2500KVA transformer is used to investigate. The parameters of represented model have been computed by use of the practical measurements and also genetic algorithm method. Comparisons between the calculated and the measured results are satisfactory and confirm the good precision of the represented method in order to estimate model parameters.
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2013 3rd International Conference on Electric Power and Energy Conversion Systems, 2013
In this paper a novel model for analyzing the transient state of the distribution transformers is proposed. The presented model is as simple that the simulation process can be conducted so fast and easy and also its application in the form of a two port element in power network is possible. By considering the complexity of the analytical methods, Particles Swarm Optimization (PSO) algorithm is applied for estimation of the parameters of the transient model of the transformers. In order to do that, related tests were performed on a 2.5 MV A and 6300/420 V distribution transformers, after that, the desired parameters were estimated by the implemented PSO algorithm. Finally, by comparing the experimental and the estimated values, the reliability of the PSO algorithm in this case was evaluated. Also, a comparison between the obtained values in this research and the results of Genetic Algorithm (GA) and the analytical method were carried out. The result reveals the more capabilities and accuracies of the PSO algorithm.
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Accurate determination of parameters in power transformer equivalent circuit is important because it can influence the simulation results of condition monitoring on power transformers, such as analysis of frequencyresponse. This is due to inaccurate simulation results will yield incorrect interpretation of the power transformer condition through its equivalent circuit. Works on development of transformer models have been widely developed since the past for transient and steady-state analyses. Estimating parameters of a transformer using nameplate data without performing a single experiment has been developed in the past. However, the average error between the actual and estimated parameter values in the past work using Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) is considerably large. This signifies that there is a room for improvement by using other optimisation techniques, such as state of the art methods which include Heterogeneous Comprehensive Learning PSO (HCLPSO), LSHADE-EpSin, Imperialist Competitive Algorithm (ICA), Gravitational Search Algorithm (GSA) and others. Since ICA and GSA have advantages over GA and PSO, in this work, estimation of transformer parameters from its nameplate data was proposed using ICA and GSA. The results obtained using ICA and GSA was compared to those using GA and PSO to determine the parameters of transformer equivalent circuit. The results show that GSA performs the best as it gives the lowest average error compared to PSO, GA and ICA. Therefore, the proposed technique using GSA and ICA can give a better accuracy than PSO and GA in estimating the parameters of power transformers. The proposed method can also be applied to estimate parameters of three-phase transformers from their nameplate data without disconnecting them from the grid for testing.
Periodicals of Engineering and Natural Sciences (PEN), 2017
Real world problems such as scientific, engineering, industrial problems are in the form of the multi-objective optimization problems. In order to achieve optimum solutions of such problems, multi-objective optimization algorithms are utilized. In this study, the problem is estimation of single-phase transformer parameters which is one of the engineering problems. This estimation is provided by artificial bee colony (ABC) algorithm. ABC is developed as a metaheuristic method and simulates foraging of bees. Since the problem is a multi-objective optimization problem, multi-objective ABC (MOABC) is proposed to estimate parameters in the study. This study aims to estimate equivalent circuit parameters using current and voltage values at any known load. Through algorithm, difference between actual and estimated parameter values that is the error has been tried to minimize. The successful results show that the proposed method can be used for a single-phase transformer parameters estimation.
IEEE Access
In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms. INDEX TERMS Coyote, PSO, Jaya, single-phase transformer, transformer equivalent circuit.
Extraction of Parameters of Planar Transformers Using Genetic Algorithms
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The planar inductors and transformers are widely used passive components in RF circuits. The precise RF model development of inductor and transformer elements is an important step in the design of microelectronic circuits for RF applications. The models can be extracted and optimized using Genetic Algorithms (GA). A general approach for parameter extraction of the model parameters for planar transformers is proposed and compared with the results, obtained via similar methods [1,2,3,4,5]. The presented extraction approach is applicable and with a good accuracy. It is based on computer models, developed using the possibilities of MATLAB and GA toolbox [6]. GA programs are designed using the MATLAB GA toolbox for parameter extraction of the model parameters of planar transformers. A verification of the obtained results for the extracted model parameters is done by comparison of the simulation results obtained using PSpice of the corresponding model and measurement data in [4].
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The problem of parametric estimation in single-phase transformers is addressed in this research from the point of view of metaheuristic optimization. The parameters of interest are the series resistance and reactance as well as the magnetization resistance and reactance. To obtain these parameters considering only the voltage and the currents measured in the terminals of the transformer, a nonlinear optimization model that deals with the minimization of the mean square error among the measured and calculated voltage and current variables is formulated. The nonlinear programming model is solved through the implementation of a simple but efficient metaheuristic optimization technique known as the black-hole optimizer. Numerical simulations demonstrate that the proposed optimization method allows for the reduction in the estimation error among the measured and calculated variables when compared with methods that are well established in the literature such as particle swarm optimization...
An Evolutionary Computation Solution to Transformer Design Optimization Problem
7th International Conference on Electrical and Electronics Engineering Research (CIIIEE), 2010
The transformer design optimization (TDO) is a complex constrained mixed-integer non-linear programming problem with discontinuous objective function. This paper proposes an innovative method combining genetic algorithm (GA) and finite element method (FEM) for the solution of TDO problem. The main contributions of the proposed method are: (i) introduction of an innovative recursive GA with a novel external elitism strategy associated with variable crossover and mutation rates resulting in an improved GA, (ii) adoption of two particular finite element models of increased accuracy and high computational speed for the validation of the optimal design by computing the no-load loss and impedance, and (iii) combination of the innovative recursive GA with the two particular finite element models resulting in a proposed GAFEM model that finds the global optimum, as concluded after several tests on actual transformer designs, while other existing methods provided suboptimal solutions that are 3.1% to 5.8% more expensive than the optimal solution.
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In this article, a combinatorial optimization approach for estimating the electrical parameters in single-phase distribution transformers by considering voltage and current measures is presented. A nonlinear programming model was formulated to represent the parametric estimation problem. This mathematical optimization model was developed by applying Kirchhoff’s laws to the equivalent electric circuit of the transformer. To solve the NLP model is employed the sine-cosine algorithm, which corresponds to a combinatorial optimization methodology from the family of metaheuristics that has the ability for finding good solutions with minimum computational requirements, easily implementable at any programming language. Numerical results show that the parametric estimation in the transformers using the proposed NLP model represents the electrical behavior of these devices adequately, considering different load scenarios. All the simulations were carried out using MATLAB software and compared...
Parameter identification of power transformers thermal model via genetic algorithms
Electric Power Systems Research, 2001
Recent studies by various authors have shown as the IEEE Transformer Loading Guide model and the more recent modified equations, proposed by the Working Group K3 of the IEEE 'Power System Relaying Committee', are lacking in accuracy in predicting the winding hottest-spot temperature of a power transformer in presence of overload conditions. This is mainly due to the deviation of the parameters of the thermal model of the power transformer in the presence of overload conditions. In the paper, a novel technique to identify the thermal parameters to be used for the estimation of the hot-spot temperature is presented. The proposed method is based on a genetic algorithm (GA) which, working on the load current and on the measured hot-spot temperature pattern, permits to identify a corrected set of parameters for the thermal model of the power transformer. Thanks to data obtained from the experimental tests, the GA based method is tested to evaluate the performance of the proposed method in terms of accuracy.