A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer

For the safety and continuity of service in industrial electrical systems, the availability of transformers is essential. For this reason, it is necessary to develop intelligent fault diagnosis techniques to reduce repair and maintenance costs. Recently, several methods have been developed that use...

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Autores:
Fernández Blanco, Juan Carlos
Corrales Barrios, Luis Benigno
Hernández González, Félix Herminio
Benitez Pina, Israel Francisco
Núñez Alvarez, José Ricardo
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8946
Acceso en línea:
https://hdl.handle.net/11323/8946
https://doi.org/10.1007/978-3-030-89691-1_19
https://repositorio.cuc.edu.co/
Palabra clave:
Power transformer
Fault diagnosis
Fuzzy logic
Dissolved gas analysis
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_bffb63feab22444990111fd793907bbf
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8946
network_acronym_str RCUC2
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repository_id_str
dc.title.spa.fl_str_mv A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
title A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
spellingShingle A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
Power transformer
Fault diagnosis
Fuzzy logic
Dissolved gas analysis
title_short A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
title_full A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
title_fullStr A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
title_full_unstemmed A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
title_sort A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
dc.creator.fl_str_mv Fernández Blanco, Juan Carlos
Corrales Barrios, Luis Benigno
Hernández González, Félix Herminio
Benitez Pina, Israel Francisco
Núñez Alvarez, José Ricardo
dc.contributor.author.spa.fl_str_mv Fernández Blanco, Juan Carlos
Corrales Barrios, Luis Benigno
Hernández González, Félix Herminio
Benitez Pina, Israel Francisco
Núñez Alvarez, José Ricardo
dc.subject.spa.fl_str_mv Power transformer
Fault diagnosis
Fuzzy logic
Dissolved gas analysis
topic Power transformer
Fault diagnosis
Fuzzy logic
Dissolved gas analysis
description For the safety and continuity of service in industrial electrical systems, the availability of transformers is essential. For this reason, it is necessary to develop intelligent fault diagnosis techniques to reduce repair and maintenance costs. Recently, several methods have been developed that use artificial intelligence techniques such as neural networks, support vector machines, hybrid techniques, etc., for the diagnosis of faults in power transformers using gas analysis. These methods, although they present very good results, encounter restrictions to determine the precise moment before the occurrence of multiple fault of small magnitude and are difficult to implement in practice. This document proposes a method to diagnose multiple incipient faults in a power transformer using fuzzy logic. The proposal, based on historical data from the composition of the gases dissolved in the oil, achieves a performance in the classification of multiple incipient fault of 98.3%. With reliable samples of dissolved gas, it guarantees an overall rate of accuracy in detecting incipient faults that is superior to that obtained by the most successful conventional methods in the industry. The proposal does not encounter generalization difficulties and constitutes a simple solution that allows determining the state of the transformer in service without affecting the continuity of the electricity supply.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-12-06T22:42:06Z
dc.date.available.none.fl_str_mv 2021-12-06T22:42:06Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv 0302-9743
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8946
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-030-89691-1_19
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0302-9743
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Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8946
https://doi.org/10.1007/978-3-030-89691-1_19
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. T. Committee. IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers, IEEE Std C57.104™ (2019). https://doi.org/10.1109/IEEESTD.2019.8890040
2. Alzghoul, A., Backe, B., Löfstrand, M., Byström, A., Liljedahl, B.: Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: a hydraulic drive system application. Comput. Ind. 65(8), 1126–1135 (2014). https://doi.org/10.1016/j.compind.2014.06.003
3. Chang, C.-K., Shan, J., Chang, K.-C., Pan, J.-S.: Insulation faults diagnosis of power transformer by decision tree with fuzzy logic. In: Pan, J.-S., Lin, J.-W., Liang, Y., Chu, S.-C. (eds.) ICGEC 2019. AISC, vol. 1107, pp. 310–317. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3308-2_35
4. Guo, C., et al.: Transformer failure diagnosis using fuzzy association rule mining combined with case-based reasoning. IET Gener. Transm. Distrib. 14(11), 2202–2208 (2020). https://doi.org/10.1049/iet-gtd.2019.1423
5. Duan, J., He, Y., Wu, X.: Assisted diagnosis of real-virtual twin space for data insufficiency. In: Chen, W., Yang, Q., Wang, L., Liu, D., Han, X., Meng, G. (eds.) The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering. LNEE, vol. 743, pp. 387–395. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6609-1_34
6. Singh, A.K., Saxena, A., Roy, N., Choudhury, U.: Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller. Trans. Inst. Measur. Control, 01423312211007006 (2021). https://doi.org/10.1177/01423312211007006
7. Sahoo, S., Chowdary, K.V.V.S.R., Das, S.: DGA and AI technique for fault diagnosis in distribution transformer. In: Sherpa, K.S., Bhoi, A.K., Kalam, A., Mishra, M.K. (eds.) ETAEERE 2020. LNEE, vol. 691, pp. 35–46. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7511-2_4
8. Tahir, M., Tenbohlen, S.: Transformer winding condition assessment using feedforward artificial neural network and frequency response measurements. Energies 14(11), 3227 (2021). https://doi.org/10.3390/en14113227
9. Tao, L., Yang, X., Zhou, Y., Yang, L.: A novel transformers fault diagnosis method based on probabilistic neural network and bio-inspired optimizer. Sensors 21(11), 3623 (2021). https://doi.org/10.3390/s21113623
10. Mo, W., Kari, T., Wang, H., Luan, L., Gao, W.: Power transformer fault diagnosis using support vector machine and particle swarm optimization. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017, vol. 1, pp. 511–515: IEEE. Hangzhou, China (2017). https://doi.org/10.1109/ISCID.2017.165
11. Kazemi, Z., Naseri, F., Yazdi, M., Farjah, E.: An EKF-SVM machine learning-based approach for fault detection and classification in three-phase power transformers. IET Sci. Meas. Technol. 15(2), 130–142 (2021). https://doi.org/10.1049/smt2.12015
12. Velásquez, R.M.A.: Support vector machine and tree models for oil and Kraft degradation in power transformers. Eng. Fail. Anal. 127, 105488 (2021). https://doi.org/10.1016/j.engfailanal.2021.105488
13. Hoballah, A., Mansour, D.-E.A., Taha, I.B.: Hybrid grey wolf optimizer for transformer fault diagnosis using dissolved gases considering uncertainty in measurements. IEEE Access 8, 139176–139187 (2020). https://doi.org/10.1109/ACCESS.2020.3012633
14. Shiling, Z.: Application of joint immune ant colony algorithm and fuzzy neural network to path planning and visual image processing of inspection robot in substation. In: 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), 2020, pp. 142–148. IEEE, Chengdu, China (2020). https://doi.org/10.1109/ICAIBD49809.2020.9137437
15. Taha, I.B., Hoballah, A., Ghoneim, S.S.: Optimal ratio limits of rogers’ four-ratios and IEC 60599 code methods using particle swarm optimization fuzzy-logic approach. IEEE Trans. Dielectr. Electr. Insul. 27(1), 222–230 (2020). https://doi.org/10.1109/TDEI.2019.008395
16. Tightiz, L., Nasab, M.A., Yang, H., Addeh, A.: An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis. ISA Trans. 103, 63–74 (2020). https://doi.org/10.1016/j.isatra.2020.03.022
17. Malik, H., Sharma, R., Mishra, S.: Fuzzy reinforcement learning based intelligent classifier for power transformer faults. ISA Trans. 101, 390–398 (2020). https://doi.org/10.1016/j.isatra.2020.01.016
18. Palke, R., Korde, P.: Dissolved Gas Analysis (DGA) to diagnose the internal faults of power transformer by using fuzzy logic method. In: 2020 International Conference on Communication and Signal Processing (ICCSP), 2020, pp. 1050–1053. IEEE, Chennai, India (2020). https://doi.org/10.1109/ICCSP48568.2020.9182279
19. Prasojo, R.A., Gumilang, H., Maulidevi, N.U., Soedjarno, B.A.: A fuzzy logic model for power transformer faults’ severity determination based on gas level, gas rate, and dissolved gas analysis interpretation. Energies 13(4), 1009 (2020). https://doi.org/10.3390/en13041009
20. Abdo, A., Liu, H., Zhang, H., Guo, J., Li, Q.: A new model of faults classification in power transformers based on data optimization method. Electric Power Syst. Res. 200, 107446 (2021). https://doi.org/10.1016/j.epsr.2021.107446
21. Tenbohlen, S., Jagers, J., Vahidi, F., Standardized survey of transformer reliability: on behalf of CIGRE WG A2. 37. In: 2017 International Symposium on Electrical Insulating Materials (ISEIM), 2017, vol. 2, pp. 593–596. IEEE, Toyohashi, Japan (2017). https://doi.org/10.23919/ISEIM.2017.8166559
22. Blanco, J.C.F., González, F.H.H., Barrios, L.B.C.: Método de lógica difusa para el diagnóstico de fallos incipientes en un transformador de 40MVA. Rev. Ing. Electrón. Autom. y Com. 42(2), 76–88 (2021). 1815-5928
23. Li, E., Wang, L., Song, B.: Fault diagnosis of power transformers with membership degree. IEEE Access 7, 28791–28798 (2019). https://doi.org/10.1109/ACCESS.2019.2902299
24. Mohamad, F., Hosny, K., Barakat, T.: incipient fault detection of electric power transformers using fuzzy logic based on roger’s and IEC method. In: 2019 14th International Conference on Computer Engineering and Systems (ICCES), 2019, pp. 303–309. IEEE, Cairo, Egypt (2019). https://doi.org/10.1109/ICCES48960.2019.9068132
25. Niţu, M.-C., Aciu, A.-M., Nicola, C.-I., Nicola, M.: Power transformer fault diagnosis using fuzzy logic technique based on dissolved gas analysis and furan analysis. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) and 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), 2017, pp. 184–189. IEEE, Brasov, Romania (2017). https://doi.org/10.1109/OPTIM.2017.7974968
26. Duval, M., Lamarre, L.: The duval pentagon-a new complementary tool for the interpretation of dissolved gas analysis in transformers. IEEE Electr. Insul. Mag. 30(6), 9–12 (2014). https://doi.org/10.1109/MEI.2014.6943428
27. Duval, M., DePabla, A.: Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases. IEEE Electr. Insul. Mag. 17(2), 31–41 (2001) %@ 0883–7554. https://doi.org/10.1109/57.917529
28. Faiz, J., Soleimani, M.: Dissolved gas analysis evaluation in electric power transformers using conventional methods a review. IEEE Trans. Dielectr. Electr. Insul. 24(2), 1239–1248 (2017). https://doi.org/10.1109/TDEI.2017.005959
29. Mahmoudi, N., Samimi, M.H., Mohseni, H.: Experiences with transformer diagnosis by DGA: case studies. IET Gener. Transm. Distrib. 13(23), 5431–5439 (2019). https://doi.org/10.1049/iet-gtd.2019.1056
30. Rahman, O., Wani, S.A., Parveen, S., Khan, S.A.: Detection of incipient fault in transformer using DGA based integrated intelligent method. In: 2019 International Conference on Power Electronics, Control and Automation (ICPECA), 2019, pp. 1–6. IEEE, New Delhi, India (2019). https://doi.org/10.1109/ICPECA47973.2019.8975638
31. Pattanadech, N., Wattakapaiboon, W.: Application of Duval pentagon compared with Other DGA interpretation techniques: case studies for actual transformer inspections including experience from power plants in Thailand. In: 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2019, pp. 1–4. IEEE, Luang Prabang, Laos (2019). https://doi.org/10.1109/ICEAST.2019.8802523
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spelling Fernández Blanco, Juan CarlosCorrales Barrios, Luis BenignoHernández González, Félix HerminioBenitez Pina, Israel FranciscoNúñez Alvarez, José Ricardo2021-12-06T22:42:06Z2021-12-06T22:42:06Z20210302-97431611-3349https://hdl.handle.net/11323/8946https://doi.org/10.1007/978-3-030-89691-1_19Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/For the safety and continuity of service in industrial electrical systems, the availability of transformers is essential. For this reason, it is necessary to develop intelligent fault diagnosis techniques to reduce repair and maintenance costs. Recently, several methods have been developed that use artificial intelligence techniques such as neural networks, support vector machines, hybrid techniques, etc., for the diagnosis of faults in power transformers using gas analysis. These methods, although they present very good results, encounter restrictions to determine the precise moment before the occurrence of multiple fault of small magnitude and are difficult to implement in practice. This document proposes a method to diagnose multiple incipient faults in a power transformer using fuzzy logic. The proposal, based on historical data from the composition of the gases dissolved in the oil, achieves a performance in the classification of multiple incipient fault of 98.3%. With reliable samples of dissolved gas, it guarantees an overall rate of accuracy in detecting incipient faults that is superior to that obtained by the most successful conventional methods in the industry. The proposal does not encounter generalization difficulties and constitutes a simple solution that allows determining the state of the transformer in service without affecting the continuity of the electricity supply.Fernández Blanco, Juan Carlos-will be generated-orcid-0000-0002-0707-0787-600Corrales Barrios, Luis Benigno-will be generated-orcid-0000-0003-3064-2066-600Hernández González, Félix Herminio-will be generated-orcid-0000-0003-2057-5517-600Benitez Pina, Israel Francisco-will be generated-orcid-0000-0003-2359-9768-600Núñez Alvarez, José Ricardo-will be generated-orcid-0000-0002-6607-7305-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Computer Sciencehttps://link.springer.com/chapter/10.1007/978-3-030-89691-1_19Power transformerFault diagnosisFuzzy logicDissolved gas analysisA fuzzy logic proposal for diagnosis multiple incipient faults in a power transformerPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion1. T. Committee. IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers, IEEE Std C57.104™ (2019). https://doi.org/10.1109/IEEESTD.2019.88900402. Alzghoul, A., Backe, B., Löfstrand, M., Byström, A., Liljedahl, B.: Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: a hydraulic drive system application. Comput. Ind. 65(8), 1126–1135 (2014). https://doi.org/10.1016/j.compind.2014.06.0033. Chang, C.-K., Shan, J., Chang, K.-C., Pan, J.-S.: Insulation faults diagnosis of power transformer by decision tree with fuzzy logic. In: Pan, J.-S., Lin, J.-W., Liang, Y., Chu, S.-C. (eds.) ICGEC 2019. AISC, vol. 1107, pp. 310–317. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3308-2_354. Guo, C., et al.: Transformer failure diagnosis using fuzzy association rule mining combined with case-based reasoning. IET Gener. Transm. Distrib. 14(11), 2202–2208 (2020). https://doi.org/10.1049/iet-gtd.2019.14235. Duan, J., He, Y., Wu, X.: Assisted diagnosis of real-virtual twin space for data insufficiency. In: Chen, W., Yang, Q., Wang, L., Liu, D., Han, X., Meng, G. (eds.) The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering. LNEE, vol. 743, pp. 387–395. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6609-1_346. Singh, A.K., Saxena, A., Roy, N., Choudhury, U.: Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller. Trans. Inst. Measur. Control, 01423312211007006 (2021). https://doi.org/10.1177/014233122110070067. Sahoo, S., Chowdary, K.V.V.S.R., Das, S.: DGA and AI technique for fault diagnosis in distribution transformer. In: Sherpa, K.S., Bhoi, A.K., Kalam, A., Mishra, M.K. (eds.) ETAEERE 2020. LNEE, vol. 691, pp. 35–46. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7511-2_48. Tahir, M., Tenbohlen, S.: Transformer winding condition assessment using feedforward artificial neural network and frequency response measurements. Energies 14(11), 3227 (2021). https://doi.org/10.3390/en141132279. Tao, L., Yang, X., Zhou, Y., Yang, L.: A novel transformers fault diagnosis method based on probabilistic neural network and bio-inspired optimizer. Sensors 21(11), 3623 (2021). https://doi.org/10.3390/s2111362310. Mo, W., Kari, T., Wang, H., Luan, L., Gao, W.: Power transformer fault diagnosis using support vector machine and particle swarm optimization. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017, vol. 1, pp. 511–515: IEEE. Hangzhou, China (2017). https://doi.org/10.1109/ISCID.2017.16511. Kazemi, Z., Naseri, F., Yazdi, M., Farjah, E.: An EKF-SVM machine learning-based approach for fault detection and classification in three-phase power transformers. IET Sci. Meas. Technol. 15(2), 130–142 (2021). https://doi.org/10.1049/smt2.1201512. Velásquez, R.M.A.: Support vector machine and tree models for oil and Kraft degradation in power transformers. Eng. Fail. Anal. 127, 105488 (2021). https://doi.org/10.1016/j.engfailanal.2021.10548813. Hoballah, A., Mansour, D.-E.A., Taha, I.B.: Hybrid grey wolf optimizer for transformer fault diagnosis using dissolved gases considering uncertainty in measurements. IEEE Access 8, 139176–139187 (2020). https://doi.org/10.1109/ACCESS.2020.301263314. Shiling, Z.: Application of joint immune ant colony algorithm and fuzzy neural network to path planning and visual image processing of inspection robot in substation. In: 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), 2020, pp. 142–148. IEEE, Chengdu, China (2020). https://doi.org/10.1109/ICAIBD49809.2020.913743715. Taha, I.B., Hoballah, A., Ghoneim, S.S.: Optimal ratio limits of rogers’ four-ratios and IEC 60599 code methods using particle swarm optimization fuzzy-logic approach. IEEE Trans. Dielectr. Electr. Insul. 27(1), 222–230 (2020). https://doi.org/10.1109/TDEI.2019.00839516. Tightiz, L., Nasab, M.A., Yang, H., Addeh, A.: An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis. ISA Trans. 103, 63–74 (2020). https://doi.org/10.1016/j.isatra.2020.03.02217. Malik, H., Sharma, R., Mishra, S.: Fuzzy reinforcement learning based intelligent classifier for power transformer faults. ISA Trans. 101, 390–398 (2020). https://doi.org/10.1016/j.isatra.2020.01.01618. Palke, R., Korde, P.: Dissolved Gas Analysis (DGA) to diagnose the internal faults of power transformer by using fuzzy logic method. In: 2020 International Conference on Communication and Signal Processing (ICCSP), 2020, pp. 1050–1053. IEEE, Chennai, India (2020). https://doi.org/10.1109/ICCSP48568.2020.918227919. Prasojo, R.A., Gumilang, H., Maulidevi, N.U., Soedjarno, B.A.: A fuzzy logic model for power transformer faults’ severity determination based on gas level, gas rate, and dissolved gas analysis interpretation. Energies 13(4), 1009 (2020). https://doi.org/10.3390/en1304100920. Abdo, A., Liu, H., Zhang, H., Guo, J., Li, Q.: A new model of faults classification in power transformers based on data optimization method. Electric Power Syst. Res. 200, 107446 (2021). https://doi.org/10.1016/j.epsr.2021.10744621. Tenbohlen, S., Jagers, J., Vahidi, F., Standardized survey of transformer reliability: on behalf of CIGRE WG A2. 37. In: 2017 International Symposium on Electrical Insulating Materials (ISEIM), 2017, vol. 2, pp. 593–596. IEEE, Toyohashi, Japan (2017). https://doi.org/10.23919/ISEIM.2017.816655922. Blanco, J.C.F., González, F.H.H., Barrios, L.B.C.: Método de lógica difusa para el diagnóstico de fallos incipientes en un transformador de 40MVA. Rev. Ing. Electrón. Autom. y Com. 42(2), 76–88 (2021). 1815-592823. Li, E., Wang, L., Song, B.: Fault diagnosis of power transformers with membership degree. IEEE Access 7, 28791–28798 (2019). https://doi.org/10.1109/ACCESS.2019.290229924. Mohamad, F., Hosny, K., Barakat, T.: incipient fault detection of electric power transformers using fuzzy logic based on roger’s and IEC method. In: 2019 14th International Conference on Computer Engineering and Systems (ICCES), 2019, pp. 303–309. IEEE, Cairo, Egypt (2019). https://doi.org/10.1109/ICCES48960.2019.906813225. Niţu, M.-C., Aciu, A.-M., Nicola, C.-I., Nicola, M.: Power transformer fault diagnosis using fuzzy logic technique based on dissolved gas analysis and furan analysis. 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IEEE, Luang Prabang, Laos (2019). https://doi.org/10.1109/ICEAST.2019.8802523PublicationORIGINALA Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power Transformer.pdfA Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power Transformer.pdfapplication/pdf126339https://repositorio.cuc.edu.co/bitstreams/572a09a5-1108-44a0-9ddb-8a84dea47d69/downloadc846e6e49b3454363d14b113e6e604c7MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/975986da-91c3-41f3-9663-064e8c323c8c/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/45117da0-0bdc-4fe8-8262-28dad67e915c/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILA Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power Transformer.pdf.jpgA Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power 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