Research Article
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Predictive Model for Incipient Faults in Oil-Filled Transformers

Year 2024, Volume: 7 Issue: 2, 302 - 313, 31.08.2024

Michael Osajeh , Efosa Igodan , Linda Usiosefe

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

Abstract

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.

Keywords

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

Ethical Statement

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

Supporting Institution

Non

References

  • [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
  • [10] Dhote N, Helonde J (2012) Diagnosis of Power Transformer Faults Based on Five Fuzzy Ratio Method. WSEAS Trans. Power Syst. 7, 12.
  • [11] Liu Z, Song B, Li E, Mao Y, Wang G (2015) Study of "code Absence" in the IEC Three-Ratio Method of Dissolved Gas Analysis. IEEE Electr. Insul. Mag. 31, 6-12. doi:10.1109/MEI.2015.7303257
  • [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
  • [20] Parihar VR, Nimkar DS, Warudkar S, Deshmukh R, Thakare M (2017) Power Transformer Protection using Fuzzy Logic based Controller. International Journal of Engineering Research. Volume No.6, Issue No.7, pp: 366-370
  • [21] Fan J, Wang F, Sun Q, Bin F, Liang F, Xiao X (2017) Hybrid RVMANFIS Algorithm for Transformer Fault Diagnosis. IET Generation, Transm. Distribution 11, 3637–3643. doi:10.1049/iet-gtd.2017.0547
  • [22] Yin J, Zhu Y, Yu G (2011) Power Transformer Fault Diagnosis Based on Support Vector Machine with Cross Validation and Genetic Algorithm. In 2011 International Conference on Advanced Power System Automation and Protection. IEEE, 309–313. doi:10.1109/APAP.2011.6180419
  • [23] Benmahamed Y, Teguar Y, Boubakeur A (2017) Application of SVM and KNN to Duval Pentagon 1 Transformer Oil Diagnosis. IEEE Trans. Dielect. Electr. Inst., 24, 3443–3451, 2017
  • [24] Aizpurua JI, Catterson VM, Stewart BG, McArthur SDJ, Lambert B, Ampofo B, Pereira G, Cross JG (2018) Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing. IEEE Trans. Dielectrics Electr. Insul., vol. 25, no. 2, pp. 494–506, Apr. 2018. DOI: 10.1109/TDEI.2018.006766.
  • [25] Igodan CE, Ojietohamen SE, Izevbizua RI (2023) Cervical cancer prediction using ensemble models, NIPES Conference proceedings. The 2nd International conference 15th-17th Feb. 2023, pp.71-83. www.nipesjournals.org.ng
  • [26] Yuan F, Guo J, Xiao Z, Zeng B, Zhu W, Huang S (2019) A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine. Energies 12, 960. doi:10.3390/en12050960
  • [27] 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.
  • [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
  • [39] Igodan EC, Obe O, Thompson A.F-B. and Owolafe., O. (2022a). Erythemato Squamous Disease Prediction using Ensemble Multi-Feature Selection Approach. International Journal of Computer Science and Information Security (IJCSIS), Vol. 20, No. 2, pp. 95-106.
  • [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.
  • [41] Igodan CE, Ukaoha KC (2019) Using Multilayer Perceptron and Deep Neural Networks for the Diagnosis of Breast Cancer Classification”, 2019 IEEE AfriCon, pp. 1-7, doi:101109/AFRICON46755.2019.9133873.
  • [42] Vapnik CV, (1995) Support-vector networks. Machine Learning 20, 3, 1995, 273. doi:10.1007/BF00994018.
  • [43] Liu J, Ning B, Shi D (2019) Application of Improved Decision Tree C4.5 Algorithms in the Judgment of Diabetes Diagnostic Effectiveness. IOP Conf. Series: Journal of Physics: Conf. Series 1237 (2019) 022116. doi:10.1088/1742-6596/1237/2/022116.
  • [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.
  • [45] Witten IH, Frank E, Hall M A, Pal CJ (2017) Data Mining: Practical Machine Learning Tools and Techniques. Fourth Edition, Cambridge, U.S.A.
  • [46] Seijo-Pardo B, Porto-Diaz I, Bolon-Canedo V, Alonso-Betanzos A (2017) Ensemble Feature Selection: Homogeneous and Heterogeneous Approaches. Knowledge-Based System. 2017; 118:124-139.
  • [47] Liu J, Dong X, Zhao H, Tian Y (2022) Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion. Processes. 10(4):749. https://doi.org/10.3390/pr10040749 [Assessed 27th March 2022
  • [48] Rokach L (2019) Ensemble Learning Pattern Classification Using Ensemble Methods. Second Edition. Volumn 85. ISSN: 1793-0839. https://dokumen.pub/ensemble-learning-pattern-classification-using-ensemble-methods-2nbsped-9811201951-9789811201950.html
  • [49] Tattar PN (2018) Hands-On Ensemble Learning with R: A beginners’ guide to combining the power of machine learning algorithms using ensemble techniques. https://dokumen.pub/hands-on-ensemble-learning-with-r-a-beginners-guide-to-combining-the-power-of-machine-learning-algorithms-using-ensemble-techniques-1788624149-9781788624145.html
  • [50] Villacampa O (2015) Feature Selection and Classification Methods for Decision Making: A Comparative Analysis. Doctoral Dissertation Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. https://nsuworks.nova.edu/gscis_etd/63.
  • [51] Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A (2015) Feature Selection for High-Dimensional Data. Springer.
  • [52] Brownlee J (2019) Statistical methods for machine learning: Discover how to transform data into knowledge with Python. https://machinelearningmastery.com/statistics_for_machine_learning/
  • [53] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16 (2002) 321-357.
  • [54] Shehzad K, Zhenhua T, Shoukat S, Saeed A, Ahmad I, Sarwar Bhatti S, Chelloug, SAA (2023) Deep-Ensemble-Learning-Based Approach for Skin Cancer Diagnosis. Electronics 12, 1342. https://doi.org/10.3390/electronics12061342.
  • [55] Witten IH, Frank E (2000) Data Mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco.
  • [56] Ghoneim SSM, Taha IBM, Elkalashy NI (2016) Integrated ANN Based Proactive Fault Diagnostic Scheme for Power Transformers Using Dissolved Gas Analysis. IEEE Trans. Dielect. Electr. Insul. 23, 1838–1845. doi:10.1109/TDEI.2016.005301
  • [57] Li J, Chen X, Wu C (2009) Application of Comprehensive Relational Grade Theory in Expert System of Transformer Fault Diagnosis. In 2009 International Workshop on Intelligent Systems and Applications. IEEE, 1-4. doi:10.1109/iwisa.2009.5072742
  • [58] Zarkovic M, Stojkovic Z (2017) Analysis of Artificial Intelligence Expert Systems for Power Transformer Condition Monitoring and Diagnostics. Electric Power Syst. Res. 149, 125–136. doi:10.1016/j.epsr.2017.04.025
  • [59] Zhu X, Xiong J, Liang Q (2018) Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm. IEEE Access 6, 33583–33588. doi:10.1109/ACCESS.2018.2789933
  • [60] Zhou Z-H (2012) Ensemble methods: foundation and algorithms. CRC Press.
  • [61] Manisha, Kaur, K, Sharma NK, Singh J, Bhalla D (2022) Performance Assessment of IEEE/IEC Method and Duval Triangle technique for Transformer Incipient Fault Diagnosis

Year 2024, Volume: 7 Issue: 2, 302 - 313, 31.08.2024

Michael Osajeh , Efosa Igodan , Linda Usiosefe

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

Abstract

References

  • [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
  • [10] Dhote N, Helonde J (2012) Diagnosis of Power Transformer Faults Based on Five Fuzzy Ratio Method. WSEAS Trans. Power Syst. 7, 12.
  • [11] Liu Z, Song B, Li E, Mao Y, Wang G (2015) Study of "code Absence" in the IEC Three-Ratio Method of Dissolved Gas Analysis. IEEE Electr. Insul. Mag. 31, 6-12. doi:10.1109/MEI.2015.7303257
  • [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
  • [20] Parihar VR, Nimkar DS, Warudkar S, Deshmukh R, Thakare M (2017) Power Transformer Protection using Fuzzy Logic based Controller. International Journal of Engineering Research. Volume No.6, Issue No.7, pp: 366-370
  • [21] Fan J, Wang F, Sun Q, Bin F, Liang F, Xiao X (2017) Hybrid RVMANFIS Algorithm for Transformer Fault Diagnosis. IET Generation, Transm. Distribution 11, 3637–3643. doi:10.1049/iet-gtd.2017.0547
  • [22] Yin J, Zhu Y, Yu G (2011) Power Transformer Fault Diagnosis Based on Support Vector Machine with Cross Validation and Genetic Algorithm. In 2011 International Conference on Advanced Power System Automation and Protection. IEEE, 309–313. doi:10.1109/APAP.2011.6180419
  • [23] Benmahamed Y, Teguar Y, Boubakeur A (2017) Application of SVM and KNN to Duval Pentagon 1 Transformer Oil Diagnosis. IEEE Trans. Dielect. Electr. Inst., 24, 3443–3451, 2017
  • [24] Aizpurua JI, Catterson VM, Stewart BG, McArthur SDJ, Lambert B, Ampofo B, Pereira G, Cross JG (2018) Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing. IEEE Trans. Dielectrics Electr. Insul., vol. 25, no. 2, pp. 494–506, Apr. 2018. DOI: 10.1109/TDEI.2018.006766.
  • [25] Igodan CE, Ojietohamen SE, Izevbizua RI (2023) Cervical cancer prediction using ensemble models, NIPES Conference proceedings. The 2nd International conference 15th-17th Feb. 2023, pp.71-83. www.nipesjournals.org.ng
  • [26] Yuan F, Guo J, Xiao Z, Zeng B, Zhu W, Huang S (2019) A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine. Energies 12, 960. doi:10.3390/en12050960
  • [27] 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.
  • [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
  • [39] Igodan EC, Obe O, Thompson A.F-B. and Owolafe., O. (2022a). Erythemato Squamous Disease Prediction using Ensemble Multi-Feature Selection Approach. International Journal of Computer Science and Information Security (IJCSIS), Vol. 20, No. 2, pp. 95-106.
  • [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.
  • [41] Igodan CE, Ukaoha KC (2019) Using Multilayer Perceptron and Deep Neural Networks for the Diagnosis of Breast Cancer Classification”, 2019 IEEE AfriCon, pp. 1-7, doi:101109/AFRICON46755.2019.9133873.
  • [42] Vapnik CV, (1995) Support-vector networks. Machine Learning 20, 3, 1995, 273. doi:10.1007/BF00994018.
  • [43] Liu J, Ning B, Shi D (2019) Application of Improved Decision Tree C4.5 Algorithms in the Judgment of Diabetes Diagnostic Effectiveness. IOP Conf. Series: Journal of Physics: Conf. Series 1237 (2019) 022116. doi:10.1088/1742-6596/1237/2/022116.
  • [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.
  • [45] Witten IH, Frank E, Hall M A, Pal CJ (2017) Data Mining: Practical Machine Learning Tools and Techniques. Fourth Edition, Cambridge, U.S.A.
  • [46] Seijo-Pardo B, Porto-Diaz I, Bolon-Canedo V, Alonso-Betanzos A (2017) Ensemble Feature Selection: Homogeneous and Heterogeneous Approaches. Knowledge-Based System. 2017; 118:124-139.
  • [47] Liu J, Dong X, Zhao H, Tian Y (2022) Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion. Processes. 10(4):749. https://doi.org/10.3390/pr10040749 [Assessed 27th March 2022
  • [48] Rokach L (2019) Ensemble Learning Pattern Classification Using Ensemble Methods. Second Edition. Volumn 85. ISSN: 1793-0839. https://dokumen.pub/ensemble-learning-pattern-classification-using-ensemble-methods-2nbsped-9811201951-9789811201950.html
  • [49] Tattar PN (2018) Hands-On Ensemble Learning with R: A beginners’ guide to combining the power of machine learning algorithms using ensemble techniques. https://dokumen.pub/hands-on-ensemble-learning-with-r-a-beginners-guide-to-combining-the-power-of-machine-learning-algorithms-using-ensemble-techniques-1788624149-9781788624145.html
  • [50] Villacampa O (2015) Feature Selection and Classification Methods for Decision Making: A Comparative Analysis. Doctoral Dissertation Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. https://nsuworks.nova.edu/gscis_etd/63.
  • [51] Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A (2015) Feature Selection for High-Dimensional Data. Springer.
  • [52] Brownlee J (2019) Statistical methods for machine learning: Discover how to transform data into knowledge with Python. https://machinelearningmastery.com/statistics_for_machine_learning/
  • [53] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16 (2002) 321-357.
  • [54] Shehzad K, Zhenhua T, Shoukat S, Saeed A, Ahmad I, Sarwar Bhatti S, Chelloug, SAA (2023) Deep-Ensemble-Learning-Based Approach for Skin Cancer Diagnosis. Electronics 12, 1342. https://doi.org/10.3390/electronics12061342.
  • [55] Witten IH, Frank E (2000) Data Mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco.
  • [56] Ghoneim SSM, Taha IBM, Elkalashy NI (2016) Integrated ANN Based Proactive Fault Diagnostic Scheme for Power Transformers Using Dissolved Gas Analysis. IEEE Trans. Dielect. Electr. Insul. 23, 1838–1845. doi:10.1109/TDEI.2016.005301
  • [57] Li J, Chen X, Wu C (2009) Application of Comprehensive Relational Grade Theory in Expert System of Transformer Fault Diagnosis. In 2009 International Workshop on Intelligent Systems and Applications. IEEE, 1-4. doi:10.1109/iwisa.2009.5072742
  • [58] Zarkovic M, Stojkovic Z (2017) Analysis of Artificial Intelligence Expert Systems for Power Transformer Condition Monitoring and Diagnostics. Electric Power Syst. Res. 149, 125–136. doi:10.1016/j.epsr.2017.04.025
  • [59] Zhu X, Xiong J, Liang Q (2018) Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm. IEEE Access 6, 33583–33588. doi:10.1109/ACCESS.2018.2789933
  • [60] Zhou Z-H (2012) Ensemble methods: foundation and algorithms. CRC Press.
  • [61] Manisha, Kaur, K, Sharma NK, Singh J, Bhalla D (2022) Performance Assessment of IEEE/IEC Method and Duval Triangle technique for Transformer Incipient Fault Diagnosis
There are 61 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Michael Osajeh 0009-0000-8917-9446

Efosa Igodan 0000-0003-2102-3597

Linda Usiosefe 0009-0000-7425-4204

Early Pub Date August 29, 2024
Publication Date August 31, 2024
Submission Date January 3, 2024
Acceptance Date July 1, 2024
Published in Issue Year 2024Volume: 7 Issue: 2

Cite

IEEE M. Osajeh, E. Igodan, and L. Usiosefe, “Predictive Model for Incipient Faults in Oil-Filled Transformers”, SAUCIS, vol. 7, no. 2, pp. 302–313, 2024, doi: 10.35377/saucis...1414115.

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