Transformer Model Identification by Ārtap

Kuczmann, Miklós; Szűcs, Attila; Kovács, Gergely

doi:10.3311/ppee.17606

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Звертаючись до роботи [7] ми бачимо розгляд проблеми ідентифікації параметрів схеми заміщення Т подібної схеми трансформатора на основі реальних вимірів із інтеграцією оптимізаційних та метауристичних алгоритмів [8] , що мають між собою суттєві відмінності, як у випадку із аналогічними дослідженнями [9] та [10] застосовані алгоритми добре себе показують в статичних мірах режиму однак складність їх розуміння вимагає специфічних математичних знань та необхідності розрахунків для кожного окремого трансформатора та випадку окремо. Слід зазначити , що застосування в роботі [7] параметрів неінвазійного…”

Section: аналіз досліджень і публікаційunclassified

Application of a neural network for determining the parameters of a transformer elimination circuit under the conditions of non-invasive monitoring

Reva¹,

Todorov²,

Bezzub³

2022

Elektroteh. elektroenerg.

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Purpose. Application of a specially constructed neural network with the task of identifying the parameter substitution scheme in non-invasive monitoring conditions. Methodology. Use of electrical measurement methods by non-invasive monitoring, methods of identification and training of neural networks based on anterior and back propagation error, NARX networks. Findings. The power transformer is an important object of the power system of the electric shop substation. At the same time, frequent transitions from underload to partial overload mode are possible, which creates preconditions for the development of undesirable phenomena in transformers. Monitoring of the power transformer based on control of its substitution scheme, allows to pass to control of its basic parameters irrespective of an operating mode. Non-invasive monitoring works well in the context of the task, because research shows that the parameters of voltage and current, which it operates well reflect the dependence on changes in the parameters of the substitution scheme. Based on T equivalent the substitution scheme can detect and predict changes in parameters substitution schemes according to the parameters of the current and voltage regime flowing in this system. Many neural networks will work out the calculation of electrical and electrotechnical equivalent circuits as a task of identifying the parameters of electrical circuits in static conditions. In the process of identification, the ability of neural networks of different designs to identify one of the substitution scheme parameters, to resist damage was tested, which corrected the value of an unknown parameter. The experiment made it possible to obtain data for comparing the effectiveness of various architectures of neural networks in relation to the real parameters of the equivalent circuit. Concretizing the obtained results, we say that the NARX architecture is able to identify parameters in standard modes for all elements of the substitution circuit, which further opens up opportunities for its improvement in the calculation of nonlinear elements of the transformer when operating in a saturated state. Originality. It is established that the neural boundaries studied in the work are able to determine the parameters of the replacement circuit of a transformer or electric machine in static operating modes, which allows in the future to monitor the state of windings and magnetic circuit according to their values. Practical value. Using a neural network in the monitoring system allows you to get clear values of the equivalent circuit parameters, regardless of the mode, the proposed method significantly reduces the amount of time spent on monitoring the transformer parameters, allows you to control the power level, and, if necessary, reduce the amount of information required for the transformer monitoring.

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Section: аналіз досліджень і публікаційunclassified

Application of a neural network for determining the parameters of a transformer elimination circuit under the conditions of non-invasive monitoring

Reva¹,

Todorov²,

Bezzub³

2022

Elektroteh. elektroenerg.

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“…then digital-twin-distiller calculates a bunch of 2D FEM simulations and returns the calculated parameters (R 0 , R 1 ) with their tolerances, using modern Design of Experiments (DoE) based strategies [35][36][37]. Therefore, thedigital-twindistiller can be invoked as a simple Python function from a third-party robust optimization package, such as Ārtap [25,38,39] and it gives back the resulting value of the calculated parameter with its estimated error. It is not necessary to install the required solver to run an digital-twin-distiller, because it contains another innovative feature, which can save the built model into a platform independent Digital Twin (DT).…”

Section: Modeling With Digital-twin-distillermentioning

confidence: 99%

Automatic Tolerance Analysis of Permanent Magnet Machines with Encapsuled FEM Models Using Digital-Twin-Distiller

Orosz¹,

Gadó²,

Katona

et al. 2021

Processes

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Tolerance analysis is crucial in every manufacturing process, such as electrical machine design, because tight tolerances lead to high manufacturing costs. A FEM-based solution of the tolerance analysis of an electrical machine can easily lead to a computationally expensive problem. Many papers have proposed the design of experiments, surrogate-model-based methodologies, to reduce the computational demand of this problem. However, these papers did not focus on the information loss and the limitations of the applied methodologies. Regardless, the absolute value of the calculated tolerance and the numerical error of the applied numerical methods can be in the same order of magnitude. In this paper, the tolerance and the sensitivity of BLDC machines’ cogging torque are analysed using different methodologies. The results show that the manufacturing tolerances can have a significant effect on the calculated parameters, and that the mean value of the calculated cogging torque increases. The design of the experiment-based methodologies significantly reduced the calculation time, and shows that the encapsulated FEM model can be invoked from an external system-level optimization to examine the design from different aspects.

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“…These parametric FEM models are generated by a function call fromĀrtap, which is an optimization framework for robust design optimization. It was developed within the Department of Theoretical Electrical Engineering at the University of West Bohemia in conjunction with a fully hp-adaptive FEM-solver: Agros Suite or Agros2D [28,36,37]. It provides a simple, general interface for facilitating the solution of real-life engineering design problems.…”

Section: Modeling and Optimization Frameworkmentioning

confidence: 99%

Robust and Multi-Objective Pareto Design of a Solenoid

Gadó¹,

Orosz²

2021

Electronics

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The optimization of the design of a practical electromagnetic device involves many challenging tasks for new algorithms, especially those involving numerical modeling codes in which objective function calls must be minimized for practical design processes. The Compumag Society provides openly accessible, challenging benchmark problems (TEAM problems) for testing novel numerical solvers. This paper deals with a novel solution for the multi-objective TEAM benchmark problem. This solenoid design test problem aims to search for the optimal shape of a coil, which ensures a uniform field distribution in the control region, while the sensitivity and the mass/DC loss of the coil are also considered in the context of robust design. The main differences from the previously published solutions are that the proposed methodology optimizes all three objectives together, not only as two independent two-dimensional sub-problems. We considered the asymmetrical cases in the solution and found that the symmetrical solutions always produced better uniformity and sensitivity measures. However, the difference between the symmetrical and asymmetrical solutions is insignificant for these objectives. Despite the fact that the cheapest solutions are symmetrical setups, they perform worse than the cheapest asymmetric ones in these uniformity and sensitivity criteria. Therefore, some asymmetric solutions that were previously neglected from the solution space can be competitive and interesting for practical design.

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Transformer Model Identification by Ārtap

Cited by 3 publications

References 23 publications

Application of a neural network for determining the parameters of a transformer elimination circuit under the conditions of non-invasive monitoring

Application of a neural network for determining the parameters of a transformer elimination circuit under the conditions of non-invasive monitoring

Automatic Tolerance Analysis of Permanent Magnet Machines with Encapsuled FEM Models Using Digital-Twin-Distiller

Robust and Multi-Objective Pareto Design of a Solenoid

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