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Predictive Model for Incipient Faults in Oil-Filled Transformers

Yıl 2024, Cilt: 7 Sayı: 2, 302 - 313, 31.08.2024

Efosa Igodan , Michael Osajeh , Linda Usiosefe

https://doi.org/10.35377/saucis...1414115

Öz

The power transformer is an invaluable piece of device in the power system. To prevent catastrophic failures and the ensuing power outages, the status of a transformer linked to a system must be examined for any possible faults. Despite using DGA as a global tool for detecting faults, it is limited by the inability to accurately solve the problem associated with results variability due to the intrinsic nature of the IEC TC 10 database. This study proposed a data-driven fault/defect diagnostic model using four ensemble models with three base classifiers respectively. The base classifiers are comprised of SVM, C4.5 decision tree, and naive Bayes while the ensemble methods are comprised of stacking, voting, boosting and bagging respectively. The DGA dataset used comprises seven features and 168 instances split into training (i.e. 56%) and test (i.e. 44%) datasets respectively. The results indicate that C4.5 obtained a 98.33% accuracy while stacking obtained a 99.89% accuracy as the best-performing base and ensemble models respectively. The high classification performance accuracy achieved by our proposed models indicates its capacity for real-world applications. It can be applied to advance automation in mobile-based technology.

Anahtar Kelimeler

Dissolve gas analysis power transformer Naive Bayes support vector machine C4.5 ensemble learning

Etik Beyan

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography

Destekleyen Kurum

Non

Kaynakça

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Yıl 2024, Cilt: 7 Sayı: 2, 302 - 313, 31.08.2024

Efosa Igodan , Michael Osajeh , Linda Usiosefe

https://doi.org/10.35377/saucis...1414115

Öz

Kaynakça

  • [1] Duval M, dePablo A (2001) Interpretation of Gas-In-Oil Analysis Using New IEC Publication 60599 and IEC TC 10 Databases. IEEE Electrical Insulation Magazine, vol. 17, no. 2, pp. 31–41.
  • [2] Izzularab MA, Aly, GEM, Mansour DA (2004) On-line diagnosis of incipient faults and cellulose degradation based on artificial intelligence methods”, IEEE Int. Conf. on Solid Dielectrics, Toulouse, France.
  • [3] Hoballah, A., Mansour, J. G. and Taha IBM, (2020) Hybrid grey wolf optimizer for transformer fault diagnosis using dissolved gases considering uncertainty in measurements. IEEE Access, vol. 8, pp. 139176–139187.
  • [4] Mahamdi Y, Boubakeur A, Mekhaldi A, Benmahamed Y (2022) Power Transformer Fault Prediction using Naive Bayes and Decision tree based on Dissolved Gas Analysis. ENP Engineering Science Journal, Vol. 2, No. 1. Digital Object Identifier (DOI): 10.53907/enpesj.v2i1.63.
  • [5] Cheng L, Yu T (2018) Dissolved gas analysis principle-based intelligent approaches to fault diagnosis and decision making for large oil-immersed power transformers: A survey. Energies 11, 913. doi:10.3390/en11040913
  • [6] Fu Wan W, Weigen Chen W, Xiaojuan Peng X, Jing Shi J (2012) Study on the Gas Pressure Characteristics of Photoacoustic Spectroscopy Detection for Dissolved Gases in Transformer Oil. In 2012 International Conference on High Voltage Engineering and Application. IEEE, 286–289. doi:10.1109/ICHVE.2012.6357108.
  • [7] Chen Xi, Chen W, Gan D (2010) Properties and Gas Production Law of Surface Discharge in Transformer Oil-Paper Insulation. In 2010 Annual Report Conference on Electrical Insulation and Dielectric Phenomena. IEEE, 1–4. doi:10.1109/CEIDP.2010.5724049.
  • [8] Zeng W, Yang Y, Gan C, Li H, Liu G (2011) Study on Intelligent Development of Power Transformer On-Line Monitoring Based on the Data of DGA. In 2011 Asia-Pacific Power and Energy Engineering Conference. IEEE, 1-4. doi:10.1109/appeec.2011.5749107
  • [9] Jiang XQ, Gong Y, Han S, Zhou K (2014) Application of the Improved Three-Ratio Method in Chromatographic Analysis of Locomotive Transformer Oil. Amr 1030-1032, 29–33. doi:10.4028/www.scientific.net/amr.1030-1032.29
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  • [12] Shang H, Xu J, Zheng Z, Qi B, Zhang L (2019) A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D-S Evidence Theory. Energies 12, 4017. doi:10.3390/en12204017
  • [13] Singh S, Bandyopadhyay M (2010) Dissolved Gas Analysis Technique for Incipient Fault Diagnosis in Power Transformers: A Bibliographic Survey. IEEE Electr. Insul. Mag. 26, 41–46. doi:10.1109/MEI.2010.5599978
  • [14] Bhalla D, Bansal RK, Gupta HO (2011) Transformer Incipient Fault Diagnosis Based on DGA using Fuzzy Logic. IEEE, pages 1-5.
  • [15] Yadaiah N, Ravi N (2011) Internal Fault Detection Techniques for Power Transformers. Appl. Soft Comput. 11, 5259–5269. doi:10.1016/j.asoc.2011.05.034
  • [16] Dhini A, Faqih A, Kusumoputro B, Surjandari I, Kusiak A (2020) Data-driven fault Diagnosis of Power transformers using Dissolved Gas Analysis (DGA). International Journal of Technology. 11(2), 388-399. http://ijtech.eng.ui.as.id
  • [17] Wu Z, Zhou M, Lin Z, Chen X, Huang Y (2021) Improved Genetic Algorithm and XGBoost Classifier for Power Transformer Fault Diagnosis. Front. Energy Res. 9:745744. doi: 10.3389/fenrg.2021.745744.
  • [18] Yi J-H, Wang J, Wang G-G (2016) Improved Probabilistic Neural Networks with Self-Adaptive Strategies for Transformer Fault Diagnosis Problem. Adv. Mech. Eng. 8, 168781401562483. doi:10.1177/ 1687814015624832
  • [19] Mani G, Jerome J (2014) Intuitionistic Fuzzy Expert System Based Fault Diagnosis Using Dissolved Gas Analysis for Power Transformer. J. Electr. Eng. Tech. 9, 2058–2064. doi:10.5370/JEET.2014.9.6.2058
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  • [28] Taha IBM, Mansour DEA (2021) Novel Power Transformer Fault Diagnosis Using Optimized Machine Learning Methods. Intelligent Automation & Soft Computing. IASC, 2021, vol.28, no.3. DOI:10.32604/iasc.2021.017703
  • [29] Odongo G, Musabe R, Hanyurwimfura DA (2021) Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers. Algorithms, 14, 128. https://doi.org/10.3390/a14040128.
  • [30] Dapshima BA, Essa YC, Chaturvedi S (2023) Fault Detection and Protection of Power Transformer Using Fuzzy Logic. International Journal for Research in Applied Science and Engineering Technology (IJRASET). Volume 11 Issue I. www.ijraset.com
  • [31] Li J, Zhang Q, Wang K, Jianyi W (2015) Optimal Dissolved Gas Ratios Selected by Genetic Algorithm for Power Transformer Fault Diagnosis Based on Support Vector Machine. IEEE Transactions on Dielectrics and Electrical Insulation Vol. 23, No. 2. DOI: 10.1109/TDEI.2015.005277.
  • [32] Tao L, Yang X, Zhou Y, Yang LA (2021) Novel Transformers Fault Diagnosis Method Based on Probabilistic Neural Network and Bio-Inspired Optimizer. Sensors, 21, 3623. https://doi.org/10.3390/s21113623.
  • [33] Huang X, Yuan Y, Li J (2023) A Review of Transformer Fault Diagnosis Based on Information System Theory and Machine Learning. Preprints, pp.1-12. doi:10.20944/preprints202305.0036.v1.
  • [34] Bouchaoui L, Hemsas KE, Mellah H, Benlahneche S (2021) Power Transformer faults diagnosis using undestructive methods (Rogers and ICE) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: A comparative study. Electrical Engineering & Electromechanics 2021(4):3-11. DOI: 10.20998/2074-272X.2021.4.01.
  • [35] Demirci M, Gozde H, Taplamacioglu MC (2021) Fault Diagnosis of Power Transformers with Machine Learning Methods using Traditional Methods Data. International Journal on Technical and Physical Problems of Engineering. Issue 49, Volume 13, Number 4, pp. 225-230
  • [36] Islam MM, Lee G, Hettiwatte, SN (2018) Application of Parzen Window estimation for incipient fault diagnosis in power transformers. The institute of engineering and technology. doi: 10.1049/hve.2018.5061.
  • [37] Ma H, Zhang W, Wu R, Yang C (2018) A Power transformers fault diagnosis model based on three DGA ratios and PSO optimization SVM. IOP Conf. Series: Materials Science and Engineering 339 012001. doi:10.1088/1757-899X/339/1/012001.
  • [38] Taha IBM, Mansour DEA (2021) Novel Power Transformer Fault Diagnosis Using Optimized Machine Learning Methods. Intelligent Automation & Soft Computing. IASC, 2021, vol.28, no.3. DOI:10.32604/iasc.2021.017703
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  • [40] Igodan EC, Obe, O., Thompson, AF-B, Owolafe O (2022b) Prediction of erythemato Squamous-disease using ensemble learning framework. The Institute of Engineering and Technology. In Explainable Artificial Intelligence in Medical Decision Systems, pp.197-228.
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  • [44] Xuanyuan S, Xuanyuan S, Yue Y (2022) Application of C4.5 Algorithm in Insurance and Financial Services Using Data Mining Methods. Mobile Information Systems, vol. 2022, Article ID 5670784, 8 pages, 2022. https://doi.org/10.1155/2022/5670784.
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Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Efosa Igodan 0000-0003-2102-3597

Michael Osajeh 0009-0000-8917-9446

Linda Usiosefe 0009-0000-7425-4204

Erken Görünüm Tarihi 29 Ağustos 2024
Yayımlanma Tarihi 31 Ağustos 2024
Gönderilme Tarihi 3 Ocak 2024
Kabul Tarihi 1 Temmuz 2024
Yayımlandığı Sayı Yıl 2024Cilt: 7 Sayı: 2

Kaynak Göster

IEEE E. Igodan, M. Osajeh, ve L. Usiosefe, “Predictive Model for Incipient Faults in Oil-Filled Transformers”, SAUCIS, c. 7, sy. 2, ss. 302–313, 2024, doi: 10.35377/saucis...1414115.
 Kapak Resmi İndir

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