Abstract
This research paper deals with the problem of Metal-Oxide Surge Arrester (MOSA) condition monitoring and a new methodology in surge arrester monitoring and diagnostics is presented. A machine learning algorithm (back propagation regression) is used to estimate the non-linearity coefficient of the surge arrester, based on operating voltage and leakage current of the arrester. Using a simulated system, this research investigates the possibility of application and efficiency of machine learning. It is shown that the applied learning algorithm results are competitive with the model results parameters calculated as R2 = 0.999 and mean absolute real error computed as 0.005 which has shown that the proposed model can be used for MOSA monitoring and diagnostic purposes.
Recommended Citation
A. G. Manjunath et al., "Machine Learning MOSA Monitoring System," International Information and Engineering Technology Association, vol. 20, no. 4, pp. 203 - 208, International Information and Engineering Technology Association, Aug 2022.
The definitive version is available at https://doi.org/10.18280/i2m.200404
Department(s)
Materials Science and Engineering
Publication Status
Open Access
Keywords and Phrases
Metal-Oxide Surge Arrester (MOSA); Fourier transform; Machine learning; Regression; Simulated system
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 The Authors, All rights reserved
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
2021-08-31
