Neural Network Design for Energy Estimation in Surge Arresters
الموضوعات : Majlesi Journal of Telecommunication DevicesZohreh Dorrani 1 , Hojat annat Abadi 2
1 - Department of Electrical Engineering, Payame Noor University (PNU), Tehran, Iran
2 - . Department of Electrical Engineering, Payame Noor University (PNU), Tehran, Iran.
الکلمات المفتاحية: Arresters, Lightning, Neural Network, Power Systems,
ملخص المقالة :
In power systems, the transmission and distribution networks of electrical energy rely heavily on the performance of various equipment. Any malfunction within these systems can lead to network interruptions, short circuits, and power failures. Arresters are critical devices used to limit transient overvoltages caused by lightning strikes and switching events in transmission and distribution networks. These arresters protect equipment from transient overvoltages while ensuring that they do not react to temporary overloads. Their effectiveness is influenced by environmental conditions, such as humidity and pollution. This research aims to analyze the factors affecting voltage and energy absorption during lightning strikes on power systems. Additionally, we focus on designing an artificial neural network (ANN) to estimate the energy absorbed by the arrester, minimizing the error of this neural network. The results demonstrate that the ANN can effectively estimate the power of the arrester within the power system, providing a valuable tool for enhancing system reliability and performance. This study contributes to the understanding of arrester behavior under transient conditions and offers a novel approach to estimating their energy absorption capabilities using advanced computational techniques.
[1] N. Ravichandran, D. Proto, and A. Andreotti, "Surge arrester optimal placement in distribution networks: A decision theory-based approach," Electric Power Systems Research, vol. 234, p. 110744, 2024.
[2] L. Cai et al., "Electromagnetic fields of lightning return stroke to wind turbines with discontinuous impedance model," Electric Power Systems Research, vol. 233, p. 110515, 2024.
[3] M. Boukhouna, B. Nekhoul, and B. Khelifi, "Time domain modeling of lightning transients in grounding systems considering frequency dependence and soil ionization," Electric Power Systems Research, vol. 234, p. 110542, 2024.
[4] B. Ranjbar, A. Darvishi, R. Dashti, and H. R. Shaker, "A survey of diagnostic and condition monitoring of metal oxide surge arrester in the power distribution network," Energies, vol. 15, no. 21, p. 8091, 2022.
[5] M. Khodsuz and V. Mashayekhi, "Grounding system impedance influence on the surge arrester frequency-dependent model parameters using PSO-GWO algorithm," COMPEL-The international journal for computation and mathematics in electrical and electronic engineering, vol. 42, no. 6, pp. 1456-1476, 2023.
[6] M. Zainuddin and L. Bima, "Jarak Penempatan Lightning Arrester sebagai Pelindung Transformator terhadap Tegangan Lebih pada Gardu Induk 150 Kv Harapan Baru," Mutiara: Jurnal Ilmiah Multidisiplin Indonesia, vol. 1, no. 2, pp. 164-185, 2023.
[7] S. Xu, H. Tu, and Y. Xia, "Resilience enhancement of renewable cyber–physical power system against malware attacks," Reliability Engineering & System Safety, vol. 229, p. 108830, 2023.
[8] A. H. K. Asadi, M. Eskandari, and H. Delavari, "Accurate Surge Arrester Modeling for Optimal Risk-Aware Lightning Protection Utilizing a Hybrid Monte Carlo–Particle Swarm Optimization Algorithm," Technologies, vol. 12, no. 6, p. 88, 2024.
[9] H. Abduljabar Salim Ahmed and R. Asgarnezhad, "Improving students' performance prediction using LSTM and neural network," Majlesi Journal of Telecommunication Devices, vol. 12, no. 3, pp. 121-127, 2023.
[10] S. M. M. Ziaei, P. Etezadifar, Y. Nouruzi, and N. Zarei, "Distinction of Target and Chaff Signals by Suggesting the Optimal Waveform in Cognitive Radar using Artificial Neural Network," Majlesi Journal of Telecommunication Devices, vol. 12, no. 2, pp. 69-77, 2023.
[11] A. Arshaghi and M. Norouzi, "A Survey on Face Recognition Based on Deep Neural Networks," Majlesi Journal of Telecommunication Devices, 2023.
[12] E. Karami, E. Hajipour, M. Vakilian, and K. Rouzbehi, "Analysis of Frequency-Dependent Network Equivalents in Dynamic Harmonic Domain," Electric Power Systems Research, vol. 193, p. 107037, 2021.
[13] Z. Dorrani, "Road Detection with Deep Learning in Satellite Images," Majlesi Journal of Telecommunication Devices, vol. 12, no. 1, pp. 43-47, 2023.
[14] A. A. Abed and M. Emadi, "Detection and Segmentation of Breast Cancer Using Auto Encoder Deep Neural Networks," Majlesi Journal of Telecommunication Devices, vol. 12, no. 4, pp. 209-217, 2023.
[15] R. Rohini and C. Pugazhendhi Sugumaran, "Enhancement of electro-thermal characteristics of micro/nano ZnO based surge arrester," Journal of Electrical Engineering & Technology, vol. 16, pp. 469-481, 2021.
[16] V. Hinrichsen, "Metal-oxide surge arresters in high-voltage power systems," Fundamentals. Siemens AG, Erlangen, Germany, 2012.
[17] Z. Cui, L. Wang, Q. Li, and K. Wang, "A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network," International Journal of Energy Research, vol. 46, no. 5, pp. 5423-5440, 2022.
[18] R. Y. Choi, A. S. Coyner, J. Kalpathy-Cramer, M. F. Chiang, and J. P. Campbell, "Introduction to machine learning, neural networks, and deep learning," Translational vision science & technology, vol. 9, no. 2, pp. 14-14, 2020.
[19] Z. Dorrani, H. Farsi, and S. Mohamadzadeh, "Deep Learning in Vehicle Detection Using ResUNet-a Architecture," Jordan Journal of Electrical Engineering. All rights reserved-Volume, vol. 8, no. 2, p. 166, 2022.
[20] Z. Dorrani, H. Farsi, and S. Mohammadzadeh, "Edge Detection and Identification using Deep Learning to Identify Vehicles," Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 39, p. 201, 2022.
[21] B. Fesl, M. Koller, and W. Utschick, "On the mean square error optimal estimator in one-bit quantized systems," IEEE Transactions on Signal Processing, vol. 71, pp. 1968-1980, 2023.
