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International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-8 Issue-75 February-2021
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Paper Title : Transformer health index prediction using feedforward neural network according to scoring and ranking method
Author Name : Nur Ashida Salim, Jasronita Jasni, Hasmaini Mohamad and Zuhaila Mat Yasin
Abstract :

This paper presents a transformer health index prediction applying feedforward neural network (FFNN) according to scoring and ranking method. Power transformer is an important asset of a power system where the function is to convert the level of electrical power and transfer it to the consumer. Outage in transmission line that is caused by the power transformer might lead to the interruption of power supply. Transformer asset management is vital to monitor the operation of transformers in the system to prevent failure. The technique in performing asset management of the transformer is health index (HI). Therefore, this paper presents the assessment of transformer HI by applying artificial neural network (ANN). The FFNN training algorithms proposed in this research to predict the transformer HI include Levenberg–Marquardt (LM), quasi-Newton backpropagation (QNBP), and scaled conjugate gradient (SCG). The HI values obtained from these FFNN techniques were compared to the scoring and ranking method to validate the proposed technique. The performance of the proposed ANN was assessed according to the correlation coefficient and mean square error (MSE). According to the findings obtained, the transformer HI can be successfully predicted by applying different training algorithm of ANN. LM, QNBP and SCG proposed in this research could identify whether the transformer condition is very good, good, fair or poor. The ANN proposed in this research also has been verified with the ranking and scoring method where it produces similar identification of the transformer health index. According to the HI, advance action could be initiated whether to perform upgrade, maintenance, replacement, monitoring, repair, and contingency control of the transformer.
Keywords : Feedforward neural network, Levenberg–marquardt, Quasi-newton backpropagation, Scaled conjugate gradient, Transformer health index.
Cite this article : Salim NA, Jasni J, Mohamad H, Yasin ZM. Transformer health index prediction using feedforward neural network according to scoring and ranking method. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(75):292-303. DOI:10.19101/IJATEE.2020.762125.
References :
[1]Jahromi A, Piercy R, Cress S, Service J, Fan W. An approach to power transformer asset management using health index. IEEE Electrical Insulation Magazine. 2009; 25(2):20-34.
[Crossref] [Google Scholar]
[2]Raeisian L, Niazmand H, Ebrahimnia-Bajestan E, Werle P. Thermal management of a distribution transformer: an optimization study of the cooling system using CFD and response surface methodology. International Journal of Electrical Power & Energy Systems. 2019; 104:443-55.
[Crossref] [Google Scholar]
[3]Petchrompo S, Parlikad AK. A review of asset management literature on multi-asset systems. Reliability Engineering & System Safety. 2019; 181:181-201.
[Crossref] [Google Scholar]
[4]Ranga C, Chandel AK, Chandel R. Condition assessment of power transformers based on multi-attributes using fuzzy logic. IET Science, Measurement & Technology. 2017; 11(8):983-90.
[Crossref] [Google Scholar]
[5]Pandey A, Sonwane PM. Implementation of reliability centred maintenance for transformer. In international conference on automatic control and dynamic optimization techniques 2016 (pp. 578-81). IEEE.
[Crossref] [Google Scholar]
[6]Phadungthin R, Haema J. Risk management model of 69 kV power transformer bushing in Metropolitan substation. In 4th international conference on industrial engineering and applications 2017 (pp. 341-4). IEEE.
[Crossref] [Google Scholar]
[7]Abu-Elanien AE, Salama MM, Ibrahim M. Determination of transformer health condition using artificial neural networks. In international symposium on innovations in intelligent systems and applications 2011 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[8]Wattakapaiboon W, Pattanadech N. The new developed Health Index for transformer condition assessment. In international conference on condition monitoring and diagnosis (CMD) 2016 (pp. 32-5). IEEE.
[Crossref] [Google Scholar]
[9]Ghazali YZ, Talib MA, Rosli HA. TNB experience in condition assessment and life management of distribution power transformers. In CIRED 2009-20th international conference and exhibition on electricity distribution-part 1 2009 (pp. 1-4). IET.
[Crossref] [Google Scholar]
[10]Jasni J, Azmi A, Yahaya MS, Talib MA. Assessment of transformer health index using different model. Pertanika Journal of Science and Technology. 2017; 25:143-50.
[Google Scholar]
[11]Scatiggio F, Pompili M. Health index: The TERNAs practical approach for transformers fleet management. In electrical insulation conference 2013 (pp. 178-82). IEEE.
[Crossref] [Google Scholar]
[12]Ren M, Zeng W, Yang B, Urtasun R. Learning to reweight examples for robust deep learning. In international conference on machine learning 2018 (pp. 4334-43). PMLR.
[Google Scholar]
[13]Wang SC. Artificial neural network. In interdisciplinary computing in java programming 2003 (pp. 81-100). Springer, Boston, MA.
[Crossref] [Google Scholar]
[14]Shojaeefard MH, Zare J, Tabatabaei A, Mohammadbeigi H. Evaluating different types of artificial neural network structures for performance prediction of compact heat exchanger. Neural Computing and Applications. 2017; 28(12):3953-65.
[Crossref] [Google Scholar]
[15]Wilamowski BM, Yu H. Improved computation for levenberg–marquardt training. IEEE Transactions on Neural Networks. 2010; 21(6):930-7.
[Crossref] [Google Scholar]
[16]Nikov A, Stoeva S. Quick fuzzy backpropagation algorithm. Neural Networks. 2001; 14(2):231-44.
[Crossref] [Google Scholar]
[17]Martens J. Deep learning via hessian-free optimization. In ICML 2010 (, pp. 735-42).
[Google Scholar]
[18]Chaudhary P, Rizwan M. QNBP NN-based I cos ϕ algorithm for PV systems integrated with LV/MV grid. Soft Computing. 2020:1-16.
[Crossref] [Google Scholar]
[19]Kriegeskorte N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual Review of Vision Science. 2015; 1:417-46.
[Google Scholar]
[20]Cetişli B, Barkana A. Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Computing. 2010; 14(4):365-78.
[Crossref] [Google Scholar]
[21]Bakar NA, Abu-Siada A, Cui H, Li S. Improvement of DGA interpretation using scoring index method. In 1st international conference on electrical materials and power equipment 2017 (pp. 502-6). IEEE.
[Crossref] [Google Scholar]
[22]Phadungthin R, Haema J. Development of risk optimization model for high voltage substation transformer maintenance. In PES Asia-pacific power and energy engineering conference (APPEEC) 2015 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[23]Hamrick L. Dissolved gas analysis for transformers. Neta World. 2009:1-4.
[Google Scholar]
[24]Haema J, Phadungthin R. Development of condition evaluation for power transformer maintenance. In 4th international conference on power engineering, energy and electrical drives 2013 (pp. 620-3). IEEE.
[Crossref] [Google Scholar]
[25]Hernanda IN, Mulyana AC, Asfani DA, Negara IY, Fahmi D. Application of health index method for transformer condition assessment. In TENCON 2014-2014 IEEE Region 10 Conference 2014 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[26]Islam MM, Lee G, Hettiwatte SN. A review of condition monitoring techniques and diagnostic tests for lifetime estimation of power transformers. Electrical Engineering. 2018; 100(2):581-605.
[Crossref] [Google Scholar]