ADAPTIVE NEURO-FUZZY APPROACH TO EVALUATION OF ELECTRIC FIELDS OF ELECTROSTATIC DISCHARGE

Authors

A. B. Bakasova Институт автоматики и информационных технологий НАН КР
Аскат Асан уулу Kyrgyz State Technical University named after I. Razzakova

Keywords:

electrostatic discharge, electromagnetic field, electromagnetic compatibility, electromagnetic environment, artificial intelligence, neural network, finite element method, adaptive neuro-fuzzy inference system

Abstract

In this work, an algorithm for predicting the dynamics of an electrostatic discharge (ESD) in the context of the method of moment in the time domain is developed and used for computer simulation of electromagnetic processes associated with an electrostatic discharge of an ideally conducting prolate spheroid located near a grounded ideally conducting plane. The currents and fields of the ESR in the near and far zones are calculated, and a physical analysis of the dependence of the transition radiation on the individual parameters of the model is given. In computer simulation, the finite element method (FEM) is used in the harmonic time mode, developed in the FEMM software environment.
An efficient adaptive neuro-fuzzy inference system (ANFIS) model has been developed to estimate the ESD electromagnetic field for two configuration cases, where the results require taking into account not only the standard parameters, but also the direction and configuration of the discharge. The FEMM simulation results were used to train ANFIS in the MATLAB fuzzy software environment.

References

J. Weiss and Z. Csendes, “A one-step finite element method for multiconductor skin effect problems,” IEEE Trans. Power Appart. Syst., vol. PAS-101, no. 10, pp. 3796-3803, Oct. 1982.

D. Labridis and P. Dokopoulos, “Finite element computation of field, losses and forces in a three-phase gas cable with nonsymmetrical conductor arrangement,” IEEE Trans. Power Delivery, vol. PWDR-3, pp. 1326-1333, Oct. 1988

I. G. Damouis, K. J. Satsios, D. P. Labridis, P. S. Dokopoulos, “A fuzzy logic system for calculation of the interference of overhead transmission lines on buried pipelines”, Electric Power Systems Research, vol. 57, pp. 105-113, 2001.

I. G. Damouis, K. J. Satsios, D. P. Labridis, P. S. Dokopoulos, “Combined fuzzy logic and genetic algorithm techniques-application to an electromagnetic field problem”, Fuzzy sets and systems, vol. 129, pp. 371-386, 2002.

T. I. Maris, L. Ekonomou, G. P. Fotis, A. Nakulas, E. Zoulias, “Electromagnetic field identification using artificial neural networks” , Proc. of the 8th Conference on 8th WSEAS International Conference on Neural Networks - Volume 8 , Canada, pp.84-89, 2007.

R. G. Olsen and P. S. Wong, “Characteristics of Low Frequency Electric and Magnetic Fields in the Vicinity of Electric Power Lines”, IEEE Trans. on Power Delivery, Vol. 7, No. 4, pp. 2046-2055 , 1992.

S. Reitzinger, U. Schreiber, and U. V. Rienen, “Electro-quasi-static calculation of electric field strength on high-voltage insulators with an algebraic multigrid algorithm,” IEEE Trans. Magn., vol. 39, pp. 2129-2132, 2003.

Hasselgren L., Moller E., Hamnerius Y., “Calculation of Magnetic Shielding of a Substation at Power Frequency Using FEM”, IEEE Transactions on Power Delivery, Vol. 9, No. 3, 1994.

G.B. Iyyuni and S.A. Sebo, “Study of Transmission Line Magnetic Fields,” Proceedings of the Twenty-Second Annual North American, IEEE Power Symposium, pp.222-231, 1990.

Ministry of Electricity, Iraq, “Iraq Super Grid Projects 132 kV Single and Double Circuit Steel Tower Transmission Lines”, Volume 1 Technical Specification, 2006.

J.-S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference Systems”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, 1993.

R. Jang, “Input selection for ANFIS learning,” in Proc. 5th IEEE Int. Conf. Fuzzy Systems, Sep. 8-11, 1996, vol. 2, pp. 1493-1499.

G. Capizzi, G., S. Coco, A. Laudani, “A Neural Network tool for the prediction of electromagnetic field in urban environment”, Proc. of the 12th Biennial IEEE Conference on Electromagnetic Field Computation, pp. 60, 2006

Бакасова, А. Б., Асан уулу, А., (2022). Обзор альтернативных подходов к моделированию электромагнитной обстановки на высоковольтной электрической подстанции. Проблемы автоматики и управления, №2 (44), 4–14. извлечено от https://imash.kg/jrn/index.php/pau/article/view/307.

Верзунов С. Н. (2022) СИСТЕМА ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ДИАГНОСТИКИ COVID-19 ПО КТ-СНИМКАМ. Проблемы автоматики и управления, №3 (45), 119–134. извлечено от http://pau.imash.kg/index.php/pau/article/view/396/295

Бакасова, А., & Асан уулу, А. (2022). Применение нейронных сетей в задачах электромагнитных помех. Проблемы автоматики и управления, №1 (43), 95–103. извлечено от https://imash.kg/jrn/index.php/pau/article/view/259.

Бакасова, А., & Асан уулу, А. (2022). МОДЕЛИРОВАНИЕ ЭЛЕКТРОСТАТИЧЕСКОГО РАЗРЯДА И ИДЕНТИФИКАЦИЯ ЭЛЕКТРОМАГНИТНОГО ПОЛЯ С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ Проблемы автоматики и управления, №3 (45), 159–166. извлечено от http://pau.imash.kg/index.php/pau/article/view/387/299