KNOWLEDGE EXTRACTION TECHNIQUES FOR POWER TRANSFORMER MAINTENANCE DATA: REVIEW

Moise Manyol; Georges Olong; Samuel Eke; Aloys Marie Ibom Ibom

doi:10.29121/granthaalayah.v11.i10.2023.5316

Authors

Moïse Manyol Energy, Materials, Modeling, and Methods Research Laboratory (LE3M), Higher National Polytechnic School, University of Douala, 2701, Pk.17 Logbessou, Douala, Cameroon
Georges Olong Energy, Materials, Modeling, and Methods Research Laboratory (LE3M), Higher National Polytechnic School, University of Douala, 2701, Pk.17 Logbessou, Douala, Cameroon
Samuel Eké Energy, Materials, Modeling, and Methods Research Laboratory (LE3M), Higher National Polytechnic School, University of Douala, 2701, Pk.17 Logbessou, Douala, Cameroon
Aloys Marie Ibom Ibom Energy, Materials, Modeling, and Methods Research Laboratory (LE3M), Higher National Polytechnic School, University of Douala, 2701, Pk.17 Logbessou, Douala, Cameroon

DOI:

https://doi.org/10.29121/granthaalayah.v11.i10.2023.5316

Keywords:

Classifier, Data Mining, Predictive Analysis, Maintenance, Transformer

Abstract [English]

The maintenance of power transformers is time or condition-based and at the end of this one, analysis reports are produced to give its status. These different reports produced over time form a large database called the transformer maintenance asset bank. Extracting knowledge from this power transformer maintenance data is now an important subject for the scientific community given the importance of the transformer in the electric power generation chain. The science of data mining finds a field of application for its analysis techniques, the most used in preventive maintenance are predictive techniques. This work reviews knowledge extraction techniques from power transformer maintenance data. For this purpose, 80 articles from a platform are identified and 7 of them are retained at the end after meeting the criteria. Among the predictive analysis techniques namely regression, classification, and prediction, classification is the most used with its ANN (Artificial Neural Network) algorithm. On the other hand, association rule mining (ARM) has the highest accuracy, 98.21% in 2020. In addition, the combination of a classification algorithm preceded by the descriptive one, namely the principal component analysis (PCA), could offer higher accuracy than when they are used individually.

Downloads

Download data is not yet available.

References

Abdesselam, R. (2014). Analyse des données Polycopié 1 : Méthodes factorielles. Lyon 2 : Université lumière.

Abu-Elanien, A.E.B, & Salama, M.M.A. (2010). Asset Management Techniques for Transformers. Electr Power Syst Res, 80, 456–64. https://doi.org/10.1016/j.epsr.2009.10.008. DOI: https://doi.org/10.1016/j.epsr.2009.10.008

Abu-Elanien, A.E.B., & Salama, M.M.A. (2012). Ibrahim M. Calculation of a Health Index for Oil-Immersed Transformers Rated Under 69 kV Using Fuzzy Logic. IEEE Trans Power Deliv, 27, 2029–36. https://doi.org/10.1109/TPWRD.2012.2205165. DOI: https://doi.org/10.1109/TPWRD.2012.2205165

Agnissey, M.K. (2017). Etude des défauts sur les transformateurs de puissance par analyseur de gaz dissout de type Myrkos : Cas du transformateur T2 du Poste CEB Cotonou Vêdoko, EPAC/UAC.

Aravena, J.L., & Chowdhury, F.N. (1996). A New Approach to Fast Fault Detection in Power Systems. In : Proceedings of International Conference on Intelligent System Application to Power Systems, Orlando, FL, USA, 328–32. https://doi.org/10.1109/ISAP.1996.501093. DOI: https://doi.org/10.1109/ISAP.1996.501093

Ardi, N., Setiawan, N.A., & Bharata, A. T. (2019). Analytical Incremental Learning for Power Transformer Incipient Fault Diagnosis Based on Dissolved Gas Analysis. In 2019 5th International Conference of Science and Technology (ICST), Yogyakarta, Indonesia, 1–4. https://doi.org/10.1109/ICST47872.2019.9166441. DOI: https://doi.org/10.1109/ICST47872.2019.9166441

Arshad, M., & Islam, S.M. (2006). A Novel Fuzzy Logic Technique for Power Transformer Asset Management. Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting, 276–86. https://doi.org/10.1109/IAS.2006.256536. DOI: https://doi.org/10.1109/IAS.2006.256536

Arshad, M., Islam, S.M., & Khaliq, A. (2004). Power Transformer Asset Management. In 2004 International Conference on Power System Technology, 2004. PowerCon, 2. Singapore, 1395–8. https://doi.org/10.1109/ICPST.2004.1460220. DOI: https://doi.org/10.1109/ICPST.2004.1460220

Bangemann, T., Rebeuf, X., Reboul, D., Schulze, A., Szymanski, J., Thomesse, J-P, Thron, M., Zerhouni, N. (2006). Proteus—Creating Distributed Maintenance Systems Through an Integration Platform. Comput Ind, 57, 539–51. https://doi.org/10.1016/j.compind.2006.02.018. DOI: https://doi.org/10.1016/j.compind.2006.02.018

Bellaouar, P. A. (2013). Fiabilite Maintenabilite Disponibilite. Université Constantine 1.

Benmahamed, Y., Kemari, Y., Teguar, M., & Boubakeur, A. (2018). Diagnosis of Power Transformer Oil Using KNN and Naïve Bayes Classifiers. 2018 IEEE 2nd International Conference on Dielectrics (ICD). Budapest, Hungary. https://doi.org/10.1109/ICD.2018.8514789. DOI: https://doi.org/10.1109/ICD.2018.8514789

Bouroche, J.M., & Gilbert, S. (1980). L’analyse des données, Economie Et Statistique, 1980. Disponible sur.

Cuxac, P., Cadot, M., & François, C. (2005). Analyse Comparative De Classifications : Apport Des Règles d’Association Floues. In : 5èmes Journées d’Extraction et Gestion Des Connaissances (EGC). Paris, France, HAL, 2, 519–30. le Consulté. août 2021. En : 30 ligne]. Sur D.

Dominelli, N. (2004). Equipment Health Rating of Power Transformers. In : Conference Record of the 2004 IEEE International Symposium on Electrical Insulation, Indianapolis, IN, USA, 163–8. https://doi.org/10.1109/ELINSL.2004.1380501. DOI: https://doi.org/10.1109/ELINSL.2004.1380501

Eke, S. (2018). Stratégie d’évaluation de l’état des Transformateurs : Esquisse De Solutions Pour La Gestion Intégrée Des Transformateurs Vieillissants. Lyon, France : Universite de Lyon : HAL Theses, 212.

Fauzi, N.., Ali, N.H.N., Ker, P.J., Thiviyanathan, V.A., Leong, Y.S., Sabry, A.H., Jamaludin, Z.B ., Lo, C. K., & Mun, L. H. (2020). Fault Prediction for Power Transformer Using Optical Spectrum of Transformer Oil and Data Mining Analysis. IEEE Access, 8, 136374–81. https://doi.org/10.1109/ACCESS.2020.3011504. DOI: https://doi.org/10.1109/ACCESS.2020.3011504

Francoeur, D. (2010). Machines à Vecteur De support Une Introduction, CaMUS (Cahiers Mathématiques de l’Université de Sherbrooke), 1, 7–25.

Jahromi, A., Piercy, R., Cress, S., Service, J., Fan, W. (2009). An Approach to Power Transformer Asset Management Using Health Index. IEEE Electr Insul Mag, 25, 20–34. https://doi.org/10.1109/MEI.2009.4802595. DOI: https://doi.org/10.1109/MEI.2009.4802595

Jiang, Y., Cukic, B., & Ma, Y. (2008). Techniques for Evaluating Fault Prediction Models. Empirical Softw Eng., 13, 561–95. https://doi.org/10.1007/s10664-008-9079-3. DOI: https://doi.org/10.1007/s10664-008-9079-3

Jinshuang, M. U., Gao, Z., & Zhou, F. (2021). Defect Analysis of Secondary Equipment Based on Power Dictionary and Apriori Algorithm. E3S Web of Conferences, 256. https://doi.org/10.1051/e3sconf/202125602028. DOI: https://doi.org/10.1051/e3sconf/202125602028

Kogan, V.I., Fleeman, J.A., Provanzana, J.H., & Shih, C.H. (1988). Failure Analysis of EHV Transformers. IEEE Trans Power Delivery. avr, 3, 672–83. https://doi.org/10.1109/61.4306. DOI: https://doi.org/10.1109/61.4306

Lajnef, M.-A., Ben Ayed, M., Kolski, C. (2005). Convergence possible des processus du data mining et de conception-évaluation d’Ihm : Adaptation du modèle en U, IHM ‘05: Proceedings of the 17th Conference on l’Interaction Homme-Machine, 243–6. https://doi.org/10.1145/1148550.1148587. DOI: https://doi.org/10.1145/1148550.1148587

Lu, B., Durocher, D., Stemper, P. (2009). Predictive Maintenance Techniques. IEEE Ind Appl Mag, 15, 52–60. https://doi.org/10.1109/MIAS.2009.934444. DOI: https://doi.org/10.1109/MIAS.2009.934444

Mirowski, P., & LeCun, Y. (2012). Statistical Machine Learning and Dissolved Gas Analysis : A Review. IEEE Trans Power Delivery, 27, 1791–9. https://doi.org/10.1109/TPWRD.2012.2197868. DOI: https://doi.org/10.1109/TPWRD.2012.2197868

Morais, J., Pires, Y., Cardoso, C., & Klautau, A. (2009). ‘An Overview of Data Mining Techniques Applied to Power Systems’. in Data Mining and Knowledge Discovery in Real Life Applications, 01. Intech, 21. https://doi.org/10.5772/6463. DOI: https://doi.org/10.5772/6463

Nemeth, B., Voros, C., Cselko, R., & Gocsei, G. (2011). New Method for Improving the Reliability of Dissolved Gas Analysis. In : Annual Report Conference on Electrical Insulation and Dielectric Phenomena. Cancún, Mexico, IEEE, 296–301. https://doi.org/10.1109/CEIDP.2011.6232655. DOI: https://doi.org/10.1109/CEIDP.2011.6232655

Orille-Fernandez, A.L., Khalil, N., & Rodriguez, S. (2006). Failure Risk Prediction Using Artificial Neural Networks for Lightning Surge Protection of Underground MV Cables. IEEE Trans Power Delivery, 21, 1278–82. https://doi.org/10.1109/TPWRD.2006.874643. DOI: https://doi.org/10.1109/TPWRD.2006.874643

Qi, B., Zhang, P., Rong, Z., & Li, C. (2020). Differentiated Warning Rule of Power Transformer Health Status Based on Big Data Mining. Int J Electr Power Energy Syst, 121. https://doi.org/10.1016/j.ijepes.2020.106150. DOI: https://doi.org/10.1016/j.ijepes.2020.106150

Ravi, N. N., Drus, S. M., Krishnan, P.S. (2019). Data Mining Techniques for Transformer Failure Prediction Model : A Systematic Literature Review. In : IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE), Malaysia, avr, 305–9. https://doi.org/10.1109/ISCAIE.2019.8743987. DOI: https://doi.org/10.1109/ISCAIE.2019.8743987

Ray, P., & Mishra, D.P. (2016). Support Vector Machine Based Fault Classification and Location of a Long Transmission Line. Eng Sci Technol An Int J., 19, 1368–80. https://doi.org/10.1016/j.jestch.2016.04.001. DOI: https://doi.org/10.1016/j.jestch.2016.04.001

Rouanet, H., & Lepine, D. (1976). A propos de l’Analyse des données » selon Benzécri : Présentation et commentaires. L’Année Psychologique, 76, 133–44. https://doi.org/10.3406/psy.1976.28132. DOI: https://doi.org/10.3406/psy.1976.28132

Samuel, E., Guy, C., Thomas, A.K.A. (2016). Stratégie d’évaluation de l’Etat des Transformateurs : Esquisse de Solutions pour la gestion Intégrée des Transformateurs Vieillissants, 7.

Schittkowski, K. (2005). Optimal Parameter Selection in Support Vector Machines. J Ind Manag Optim, 1, 465–76. https://doi.org/10.3934/jimo.2005.1.465. DOI: https://doi.org/10.3934/jimo.2005.1.465

Shah, C., & Jivani A. (2015). Comparison of Data Mining Classification Algorithms for Breast Cancer Prediction. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 5. https://doi.org/10.1109/ICCCNT.2013.6726477. DOI: https://doi.org/10.1109/ICCCNT.2013.6726477

Shrivastava, K., & Choubey, A. (2012). A Novel Association Rule Mining With IEC Ratio Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers. Int J Adv Comput Res, 2, 34–44.

Touzet, C. (2016). Les RESEAUX de Neurones Artificiels, Introduction au Connexionnisme, HAL. Disponible sur.

Transfo Elec (2017, Décembre). Laboratoire OKSMAN Ceverin, « Transformateur Electrique Haute tension ».

Trappey, A.J.C., Trappey, C.V., Ma, L., & Chang, J.C.M. (2015). Intelligent Engineering Asset Management System for Power Transformer Maintenance Decision Supports Under Various Operating Conditions. Comput Ind Eng, 84, 3–11. https://doi.org/10.1016/j.cie.2014.12.033. DOI: https://doi.org/10.1016/j.cie.2014.12.033

Wagle, A.M., Lobo, A.M., Santosh, A., Patil, S., & Venkatasami, A. (2008). Real Time Web Based Condition Monitoring System for Power Transformers - Case Study. 2008 International Conference on Condition Monitoring and Diagnosis, 1307–9. https://doi.org/10.1109/CMD.2008.4580216. DOI: https://doi.org/10.1109/CMD.2008.4580216

Wang, D., Tee, S.J., Liu, Q., & Wang, Z. (2018). Factorial Analysis for Ageing Assessment of In-Service Transformers. IET Gener Transm Amp Distrib, 12, 3177–85. https://doi.org/10.1049/iet-gtd.2017.1531. DOI: https://doi.org/10.1049/iet-gtd.2017.1531

Wang, M., Vandermaar, A.J., & Srivastava, K. D. (2002). Review of Condition Assessment of Power Transformers in Service. IEEE Electr Insul Mag, 18, 12–25. https://doi.org/10.1109/MEI.2002.1161455. DOI: https://doi.org/10.1109/MEI.2002.1161455

Yang, Z., Tang, W.H., Shintemirov, A., & Wu, Q.H. (2009). Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers. IEEE Trans Syst Man Cybern C, 39, 597–610. https://doi.org/10.1109/TSMCC.2009.2021989. DOI: https://doi.org/10.1109/TSMCC.2009.2021989

Yong-Li Z, Fang W, & Lan-qin G. (2006). Transformer Fault Diagnosis Based on Naive Bayesian Classifier and SVR. In : TENCON 2006 - 2006 IEEE Region 10 Conference. Hong Kong, China, 1–4. https://doi.org/10.1109/TENCON.2006.343895. DOI: https://doi.org/10.1109/TENCON.2006.343895

Zhao, P., Kurihara, M., Tanaka, J., Noda, T., Chikuma, S., Suzuki, T. (2017). Advanced Correlation-Based Anomaly Detection Method for Predictive Maintenance. In : IEEE International Conference on Prognostics and Health Management (ICPHM), Dallas, TX, USA, 78–83. https://doi.org/10.1109/ICPHM.2017.7998309. DOI: https://doi.org/10.1109/ICPHM.2017.7998309

Zhu, Y., Wu, L., Li, X., & Yuan, J. (2005). A Transformer Condition Assessment Framework Based on Data Mining. IEEE Power Eng Soc Gen Meeting, 597–602. https://doi.org/10.1109/PES.2005.1489207. DOI: https://doi.org/10.1109/PES.2005.1489207