Application of Artificial Neural Networks for electrical losses estimation in three-phase transformer

Suppitaksakul, Chatchai; Saelee, V.

doi:10.1109/ecticon.2009.5137002

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…An artificial neural network is a self-motivated system made up of highly connected, parallel nonlinear processing components, units, or nodes that exhibit extremely high levels of computation efficiency. It can alternatively be viewed as versatile mathematical structures that can recognize intricate nonlinear correlations between input and output datasets (Suppitaksakul & Saelee, 2009). A typical neural network comprises numerous small, interconnected processes called neurons, each generating a string of activations with real values.…”

Section: Deep Neural Network Structurementioning

confidence: 99%

Proposed algorithm for smart grid DDoS detection based on deep learning

Diaba

Elmusrati

2023

Neural Networks

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Section: Deep Neural Network Structurementioning

confidence: 99%

Proposed algorithm for smart grid DDoS detection based on deep learning

Diaba

Elmusrati

2023

Neural Networks

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“…New designing of core material has added an advantage of analyzing the working efficiency of transformer. Although the efficiency of power transformers for high-power convention, there are substantial quantities of power losses, that radiates heat inadequately into the ambient air [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Power Transformer with Advance Core Material using ANSYS Technique

Urooj

Singh

Amir

et al. 2020

EJECE

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Due to indulge behavior of core materials and losses that leads to degradation of transformer efficiency. This research paper evaluating the optimal performance of transformer with designing of a newly core material namely as Mo.Me6. A possibility of increment in the efficiency for designed proposed model of transformer is observable. Also, a comparative analytical study based on the different designs of core type transformer with various core materials. The design and evaluation are performed on the Maxwell ANSYS electronic desktop platform, which provides an optimized design of transformer for practical applications. Whether it is a power transformer having lower frequency applications use in electronic circuits such as rectifying circuits or higher frequency applications power distribution circuit, etc. All transformer designs require performance evaluation in the working field and better efficiency to optimize the overall three-phase type core transformer. In this paper, proposed Mo.Me6 material with improved physical properties have been present of transformer efficiency and optimal design of core. The criteria include the changes in some effective parameters of the core, bringing out advancement in the working efficiency of the transformer. There is an opportunity to have a choice of desired material as designed particularly for the customer need making it will be more reliable and more suitable for various changeable conditions. For more efficient and effective working of transformer, this paper suggests core design with ANSYS techniques to achieve mentioned high efficiency with lower losses.

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“…Least Squares Method [17] has been applied to estimate the transformer equivalent circuit parameters, also determined the optimal approximation polynomial functions for each parameter. Artificial Neural Networks (ANN) based method has been suggested for estimation of electrical losses in the three-phase distribution transformer [18]. Electronic transformer model has been developed and also estimate the equivalent circuit parameters are presented in [19].…”

Section: Introductionmentioning

confidence: 99%

Bacterial Foraging Algorithm based Parameter Estimation of Three WINDING Transformer

Subramanian¹,

Padma²

2011

EPE

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Transformers are one of the main components of any power system. An accurate estimation of system be-haviour, including load flow studies, protection, and safe control of the system calls for an accurate equiva-lent circuit parameters of all system components such as generators, transformers, etc. This paper presents a methodology to estimate the equivalent circuit parameters of the Three Winding Transformer (TWT) using Bacterial Foraging Algorithm (BFA). The estimation procedure based on load test data at one particular op-erating point namely supply voltage, load currents, input power. The performance characteristics, such as efficiency and voltage regulation are considered along with the name plate data in order to minimize the er-ror between the estimated and measured data. The estimation procedure is demonstrated with a sample three winding transformer and the results are compared against the directly measured performance of TWT and genetic algorithm optimization results. The simulation results show the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the bacte-rial foraging algorithm

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Application of Artificial Neural Networks for electrical losses estimation in three-phase transformer

Cited by 10 publications

References 7 publications

Proposed algorithm for smart grid DDoS detection based on deep learning

Proposed algorithm for smart grid DDoS detection based on deep learning

Optimal Design of Power Transformer with Advance Core Material using ANSYS Technique

Bacterial Foraging Algorithm based Parameter Estimation of Three WINDING Transformer

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