Visual Operation and Maintenance Monitoring of Power System Data Network Based on Neural Network Model
Author:
Affiliation:
1. 1 Fujian branch of China Huadian Corporation LTD ., Fuzhou , , China .
2. 2 Huadian (Fujian) Wind Power Co., Ltd , Fuzhou , , China .
3. 3 State Grid Power Space Technology Co., Ltd , Beijing , , China .
Abstract
Publisher
Walter de Gruyter GmbH
Subject
Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science
Link
https://www.sciendo.com/pdf/10.2478/amns.2023.1.00425
Reference11 articles.
1. Hosseini, M. M., Umunnakwe, A., Parvania, M., & Tasdizen, T. (2020). Intelligent damage classification and estimation in power distribution poles using unmanned aerial vehicles and convolutional neural networks. IEEE Transactions on Smart Grid, 11(4), 3325-3333.
2. Tripathi, M. (2021). Analysis of convolutional neural network based image classification techniques. Journal of Innovative Image Processing (JIIP), 3(02), 100-117.
3. Teodoro, A. A., Gomes, O. S., Saadi, M., Silva, B. A., Rosa, R. L., & Rodríguez, D. Z. (2022). An FPGA-based performance evaluation of artificial neural network architecture algorithm for IoT. Wireless Personal Communications, 127(2), 1085-1116.
4. Yao, Y., Wang, J., Long, P., Xie, M., & Wang, J. (2020). Small‐batch‐size convolutional neural network based fault diagnosis system for nuclear energy production safety with big‐data environment. International Journal of Energy Research, 44(7), 5841-5855.
5. Venzke, A., & Chatzivasileiadis, S. (2020). Verification of neural network behaviour: Formal guarantees for power system applications. IEEE Transactions on Smart Grid, 12(1), 383-397.
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