A wavelet feature-based neural network approach to estimate electrical arc characteristics

Farzanehdehkordi, Mahshid; Ghaffaripour, Shadan; Tirdad, Kayvan; Cruz, Alex Dela; Sadeghian, Alireza

doi:10.1016/j.epsr.2022.107893

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…With the wavelet transform, it is possible to calculate both the high and low-frequency components of the signal in a specific time interval. In this way, the examination of the systems whose frequency changes over time, and the analysis of their instantaneous changes can be done very sensitively [27].…”

Section: Wavelet Transformmentioning

confidence: 99%

Modelling of Different Mother Wavelet Transforms With Artificial Neural Networks for Estimation of Solar Radiation

KAYSAL

Hocaoğlu

2023

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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IIn recent years, the interest in renewable energy sources has increased due to environmental damage and, the increasing costs of fossil fuel resources, whose current reserves have decreased. Solar energy, an environmentally friendly, clean and sustainable energy source, is one of the most important renewable energy sources. The amount of electrical energy produced from solar energy largely depends on the intensity of solar radiation. For this reason, it is essential to know and accurately predict the characteristics of the solar radiation intensity of the relevant region for the healthy sustainability of the existing solar energy systems and the systems planned to be installed. For this purpose, a two-stage forecasting model was developed using the hourly solar radiation intensity of 2014 in a region in Turkey. In the first stage of the study, the second month of each season was selected to investigate the seasonal effects of the region and large, medium, and small-scale events in the study area were examined using discrete wavelet transform. The performances of different mother wavelets in the Artificial Neural Network model with Wavelet Transform (W-ANN) are compared in the second stage. July, the most successful estimation result in seasonal solar radiation intensity was obtained. The most successful RMSE values for January, April, July and October were 65,9471W/m^2, 74,3183 W/m^2, 54,3868 W/m^2, 78,4085 W/m^2 respectively, the coiflet mother wavelet measured it.

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Section: Wavelet Transformmentioning

confidence: 99%

Modelling of Different Mother Wavelet Transforms With Artificial Neural Networks for Estimation of Solar Radiation

KAYSAL

Hocaoğlu

2023

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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“…Analysing the energy consumption of EAFs, factors related to the technological mode, in particular the power transferred to the arc gap, is traditionally considered. A significant number of publications [7][8][9][10] have been devoted to solving this problem, including methods of forecasting electricity consumption [11] and the use of artificial intelligence systems [12,13].…”

Section: Introductionmentioning

confidence: 99%

Electric Arc Furnace Electrode Movement Control System Based on a Fuzzy Arc Length Identifier

Kozyra,

Lozynskyy,

Łukasik

et al. 2023

Energies

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From the point of view of the synthesis of control influences, arc steelmaking furnaces are complex nonlinear objects with strongly expressed mutual influences. It has been demonstrated that at a given supply voltage, the distribution of the current values of the phase currents in the quasi-steady-state mode makes it possible to estimate the situation in the arc space of an arc steelmaking furnace and identify the value of arc lengths. This dependence is preserved in transient modes. In order to identify arc lengths from the phase currents, it is proposed to use an approach based on the theory of fuzzy sets. The construction of the fuzzy arc length identifier rule base was carried out in two stages: the first stage used data from quasi-steady-state modes and the Wang–Mendel algorithm; the second stage involved adding a new rule to the database if the activity level of the formed rules was lower than the established level α for the data obtained in the dynamic mode. Further optimization of the parameters of the fuzzy identifier for operation in dynamic modes was carried out using the “back-propagation” algorithm. Based on the identified values of arc lengths, a control system for the movement of electrodes in an arc steelmaking furnace was synthesised. The proposed control system makes it possible to eliminate unproductive electrode movements due to changes in the situation in other phases of the arc steelmaking furnace and simplifies the application of modern methods of synthesising a control system for such complex objects. The results obtained in the mathematical model have confirmed the effectiveness of the proposed control system for the movement of electrodes in an arc steelmaking furnace.

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Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models

Kumar,

Bharti,

Singh

et al. 2024

Environ Monit Assess

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A wavelet feature-based neural network approach to estimate electrical arc characteristics

Cited by 7 publications

References 30 publications

Modelling of Different Mother Wavelet Transforms With Artificial Neural Networks for Estimation of Solar Radiation

Modelling of Different Mother Wavelet Transforms With Artificial Neural Networks for Estimation of Solar Radiation

Electric Arc Furnace Electrode Movement Control System Based on a Fuzzy Arc Length Identifier

Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models

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