Poonnoy, N., Suwanasri, C. & Suwanasri, T. Neural community strategy to dissolved fuel evaluation for fault evaluation in energy transformers. In 2022 International Electrical Engineering Congress (iEECON) (eds Poonnoy, N. et al.) 1–4 (IEEE, 2022).
Google Scholar
Jia, J. et al. Validity analysis of transformer DGA on-line monitoring information in grid edge techniques. IEEE Access 8, 60759–60768 (2020).Article
Google Scholar
Wang, L., Littler, T. & Liu, X. Gaussian course of multi-class classification for transformer fault analysis using dissolved fuel evaluation. IEEE Trans. Dielectr. Electr. Insul. 28(5), 1703–1712 (2021).Article
Google Scholar
Gouda, O. E., El-Hoshy, S. H. & Ghoneim, S. S. Enhancing the diagnostic accuracy of DGA techniques based mostly on IEC-TC10 and associated databases. IEEE Access 9, 118031–118041 (2021).Article
Google Scholar
Cui, H. et al. Impact of load ramping on energy transformer dissolved fuel evaluation. IEEE Access 7, 170343–170351 (2019).Article
Google Scholar
Emara, M. M., Peppas, G. D. & Gonos, I. F. Two graphical shapes based mostly on DGA for energy transformer fault varieties discrimination. IEEE Trans. Dielectr. Electr. Insul. 28(3), 981–987 (2021).Article
Google Scholar
Huang, X., Yuan, Y. and Li, J. A overview of transformer fault analysis based mostly on info system principle and machine learning (2023).Islam, N. et al. Power transformer well being situation analysis: A deep generative mannequin aided clever framework. Electr. Power Systems Res. 218, 109201 (2023).Article
Google Scholar
Kang, Y., Park, H., Smit, B. & Kim, J. A multi-modal pre-training transformer for common switch learning in metal-organic frameworks. Nat. Mach. Intell. 5(3), 309–318 (2023).Article
Google Scholar
Faiz, J. & Soleimani, M. Assessment of computational intelligence and traditional dissolved fuel evaluation strategies for transformer fault analysis. IEEE Trans. Dielectr. Electr. Insul. 25(5), 1798–1806 (2018).Article
CAS
Google Scholar
Buchacz, T., Buchacz, J. & Duval, M. Stray gassing of oil in HV transformers. IEEE Trans. Dielectr. Electr. Insul. 28(5), 1729–1734 (2021).Article
CAS
Google Scholar
Maccartney, G. R., Rappaport, T. S., Samimi, M. Okay. & Sun, S. Millimeter-wave omnidirectional path loss information for small cell 5G channel modeling. IEEE Access 3, 1573–1580 (2015).Article
Google Scholar
Taha, I. B., Ibrahim, S. & Mansour, D.-E.A. Power transformer fault analysis based mostly on DGA using a convolutional neural community with noise in measurements. IEEE Access 9, 111162–111170 (2021).Article
Google Scholar
Duval, M. & Buchacz, J. Identification of arcing faults in paper and oil in transformers—Part I: Using the Duval pentagons. IEEE Electr. Insul. Mag. 38(1), 19–23 (2021).Article
Google Scholar
Yang, D., Qin, J., Pang, Y. & Huang, T. A novel double-stacked autoencoder for energy transformers DGA alerts with an imbalanced information construction. IEEE Trans. Ind. Electron. 69(2), 1977–1987 (2021).Article
Google Scholar
Camponogara, E., Jia, D., Krogh, B. H. & Talukdar, S. Distributed mannequin predictive management. IEEE Control Syst. Mag. 22(1), 44–52 (2002).Article
Google Scholar
Tra, V., Duong, B.-P. & Kim, J.-M. Improving the diagnostic efficiency of an influence transformer using an adaptive over-sampling technique for imbalanced information. IEEE Trans. Dielectr. Electr. Insul. 26(4), 1325–1333 (2019).Article
CAS
Google Scholar
Guo, Z., Li, C., Rong, Z. & Qi, B. DGA warning values for PD defects obtained from regular information evaluation on a transformer experimental platform. IEEE Trans. Dielectr. Electr. Insul. 28(5), 1759–1766 (2021).Article
CAS
Google Scholar
Wang, X., Wang, Z., Liu, Q. & Dyer, P. Dissolved fuel evaluation of thermal faults in transformer liquids simulated using the immersed heating technique. IEEE Trans. Dielectr. Electr. Insul. 25(5), 1749–1757 (2018).Article
CAS
Google Scholar
Ali, M. S., Omar, A., Jaafar, A. S. A. & Mohamed, S. H. Conventional strategies of dissolved fuel evaluation using oil-immersed energy transformer for fault analysis: A overview. Electr. Power Syst. Res. 216, 109064 (2023).Article
Google Scholar
Raj, R. A., Sarathkumar, D., Andrews, L. J. B. & Venkatachary, S. Okay. Key gases in transformer oil–an evaluation using self organizing map (SOM) neural networks. In 2023 IEEE twelfth International Conference on Communication Systems and Network Technologies (CSNT) (eds Raj, R. A. et al.) 642–647 (IEEE, 2023).Chapter
Google Scholar
Han, X. et al. A transformer situation recognition technique based mostly on dissolved fuel evaluation options choice and a number of fashions fusion. Eng. Appl. Artif. Intell. 123, 106518 (2023).Article
Google Scholar
Shutenko, O. & Kulyk, O. Recognition of low-temperature overheating in energy transformers by dissolved fuel evaluation. Electr. Eng. 104(4), 2109–2121 (2022).Article
Google Scholar
Soni, R. & Mehta, B. Diagnosis and prognosis of incipient faults and insulation standing for asset administration of energy transformer using fuzzy logic controller & fuzzy clustering means. Electr. Power Syst. Res. 220, 109256 (2023).Article
Google Scholar
Ekojono, Prasojo, R. A., Apriyani, M. E. & Rahmanto, A. N. Investigation on machine learning algorithms to assist transformer dissolved fuel evaluation fault identification. Electr. Eng. 104(5), 3037–3047 (2022).Article
Google Scholar
Banshwar, A., Kumar Sharma, N., Pathak, M., Sharma, B. B. & Kumar, S. An investigation to totally different strategies of well being evaluation in energy transformers. In Advances in Mechanical and Materials Technology: Select Proceedings of EMSME 2020 (eds Govindan, Okay. et al.) 749–756 (Springer, 2022).Chapter
Google Scholar
Velásquez, R. M. A. Support vector machine and tree fashions for oil and Kraft degradation in energy transformers. Eng. Fail. Anal. 127, 105488 (2021).Article
Google Scholar
Liu, J. et al. Classifying transformer winding deformation fault varieties and levels using FRA based mostly on assist vector machine. IEEE Access 7, 112494–112504 (2019).Article
Google Scholar
Wu, Y., Sun, X., Dai, B., Yang, P. & Wang, Z. A transformer fault analysis technique based mostly on hybrid improved gray wolf optimization and least squares-support vector machine. IET Gen. Transm. Distrib. 16(10), 1950–1963 (2022).Article
Google Scholar
Alqudsi, A. & El-Hag, A. Application of machine learning in transformer well being index prediction. Energies 12(14), 2694 (2019).Article
Google Scholar
Moradzadeh, A. & Pourhossein, Okay. Application of assist vector machines to find minor quick circuits in transformer windings. In 2019 54th International Universities Power Engineering Conference (UPEC) (eds Moradzadeh, A. & Pourhossein, Okay.) 1–6 (IEEE, 2019).
Google Scholar
Guo, Y., Mustafaoglu, Z. & Koundal, D. Spam detection using bidirectional transformers and machine learning classifier algorithms. J. Comput. Cogn. Eng. 2(1), 5–9 (2023).
Google Scholar
Liu, Y., Li, J., Li, Z., Qiao, L., Chen, S. and Lv, X. Transformer fault analysis mannequin based mostly on iterative nearest neighbor interpolation and ensemble learning. In Proc. of the 2019 2nd International Conference on Data Science and Information Technology, 140–146 (2019).Li, Z. et al. Fault analysis of transformer windings based mostly on choice tree and absolutely linked neural community. Energies 14(6), 1531 (2021).Article
Google Scholar
Xu, Y., Zhou, C., Geng, J., Gao, S. & Wang, P. A technique for diagnosing mechanical faults of on-load faucet changer based mostly on ensemble empirical mode decomposition, Volterra mannequin and choice acyclic graph assist vector machine. IEEE Access 7, 84803–84816 (2019).Article
Google Scholar
Zhong, M. et al. Dissolved fuel in transformer oil forecasting for transformer fault analysis based mostly on HATT-RLSTM. Electr. Power Syst. Res. 221, 109431 (2023).Article
Google Scholar
Shahbazi, N., Bagheri, S. & Gharehpetian, G. Identification and classification of cross-country faults in transformers using Okay-NN and tree-based classifiers. Electr. Power Syst. Res. 204, 107690 (2022).Article
Google Scholar
Sinduja, M., Maheswari, R. & Vigneshwaran, B. Transformer oil high quality evaluation using machine learning techniques. In 2022 International Conference on Computer Communication and Informatics (ICCCI) (eds Sinduja, M. et al.) 1–5 (IEEE, 2022).
Google Scholar
En, B. 60599: 2016. Mineral oil-filled electrical tools in service. Guidance on the interpretation of dissolved and free gases evaluation [Electronic resource]. British Standards Institute (2016).Ma, H., Saha, T. Okay., Ekanayake, C. & Martin, D. Smart transformer for sensible grid—Intelligent framework and techniques for energy transformer asset administration. IEEE Trans. Smart Grid 6(2), 1026–1034 (2015).Article
Google Scholar
Soto, A. R., Lima, S. L. & Saavedra, O. R. Incipient fault analysis in energy transformers by DGA using a machine learning ANN-mean shift strategy. In 2019 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (eds Soto, A. R. et al.) 1–6 (IEEE, 2019).
Google Scholar
Patekar, Okay. D. & Chaudhry, B. DGA evaluation of transformer using Artificial neural community to enhance reliability in Power Transformers. In 2019 IEEE 4th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON) (eds Patekar, Okay. D. & Chaudhry, B.) 1–5 (IEEE, 2019).
Google Scholar
Hussein, A. R., Dakhil, A. M., Rashed, J. R. & Othman, M. Intelligent knowledgeable system for diagnosing faults and assessing high quality of energy transformer insulation oil by DGA technique. Misan J. Eng. Sci. 1(1), 47–57 (2022).Article
Google Scholar
Badawi, M. et al. Reliable estimation for well being index of transformer oil based mostly on novel mixed predictive upkeep techniques. IEEE Access 10, 25954–25972 (2022).Article
Google Scholar
Cui, H., Jia, P., Peng, X. & Li, P. Adsorption and sensing of CO and C2H2 by S-defected SnS2 monolayer for DGA in transformer oil: A DFT examine. Mater. Chem. Phys. 249, 123006 (2020).Article
CAS
Google Scholar
Wang, L., Littler, T. & Liu, X. Hybrid AI mannequin for energy transformer evaluation using imbalanced DGA datasets. IET Renew. Power Gen. 17, 1912–1922 (2023).Article
Google Scholar
Demirci, M., Gözde, H. & Taplamacioglu, M. C. Improvement of energy transformer fault analysis by using sequential Kalman filter sensor fusion. Int. J. Electr. Power Energy Syst. 149, 109038 (2023).Article
Google Scholar
Shutenko, O., Proskurnia, O. & Abramov, V. Comparative evaluation of dangers that are accompanied by the use of typical and boundary gases concentrations for the diagnostics of excessive voltage transformers. Energetika https://doi.org/10.6001/energetika.v64i3.3806 (2018).Article
Google Scholar
Jiang, J. et al. Dynamic fault prediction of energy transformers based mostly on lasso regression and alter level detection by dissolved fuel evaluation. IEEE Trans. Dielectr. Electr. Insul. 27(6), 2130–2137 (2020).Article
Google Scholar
Dhini, A., Faqih, A., Kusumoputro, B., Surjandari, I. & Kusiak, A. Data-driven fault analysis of energy transformers using dissolved fuel evaluation (DGA). Int. J. Technol. 11(2), 388–399 (2020).Article
Google Scholar
Li, S. et al. Interpretation of DGA for transformer fault analysis with complementary SaE-ELM and arctangent rework. IEEE Trans. Dielectr. Electr. Insul. 23(1), 586–595 (2016).Article
Google Scholar
Nemeth, B., Laboncz, S. & Kiss, I. Condition monitoring of energy transformers using DGA and fuzzy logic. In 2009 IEEE Electrical Insulation Conference (eds Nemeth, B. et al.) 373–376 (IEEE, 2009).Chapter
Google Scholar
Zhang, Y. et al. Power transformer fault analysis contemplating information imbalance and information set fusion. High Voltage 6(3), 543–554 (2021).Article
Google Scholar
El-kenawy, E.-S.M. et al. Feature choice and classification of transformer faults based mostly on a novel meta-heuristic algorithm. Mathematics 10(17), 3144 (2022).Article
Google Scholar
Mollmann, A. & Pahlavanpour, B. New tips for interpretation of dissolved fuel evaluation in oil-filled transformers. Electra 186, 31–51 (1999).
Google Scholar
Bohatyrewicz, P. & Banaszak, S. Assessment standards of modifications in well being index values over time—A transformer inhabitants examine. Energies 15(16), 6078 (2022).Article
Google Scholar
Jongen, R., Morshuis, P., Smit, J., Janssen, A. & Gulski, E. A statistical strategy to processing energy transformer failure information. In nineteenth International Conference on Electricity Distribution Vol. 4 (eds Jongen, R. et al.) (Cired, 2007).
Google Scholar
Bustamante, S. et al. Dissolved fuel evaluation tools for on-line monitoring of transformer oil: A overview. Sensors 19(19), 4057 (2019).Article
CAS
PubMed
PubMed Central
Google Scholar
Bakar, N. A., Abu-Siada, A. & Islam, S. A overview of dissolved fuel evaluation measurement and interpretation techniques. IEEE Electr. Insul. Mag. 30(3), 39–49 (2014).Article
Google Scholar
Kim, Y., Lee, S., Seo, H., Jung, J. & Yang, H. Development of dissolved fuel evaluation (DGA) knowledgeable system using a brand new diagnostic algorithm for oil-immersed transformers. In 2012 IEEE International Conference on Condition Monitoring and Diagnosis (eds Kim, Y. et al.) 365–369 (IEEE, 2012).Chapter
Google Scholar
Zhao, A.-X., Tang, X.-J., Zhang, Z.-H. & Liu, J.-H. The DGA interpretation technique using relative content material of attribute gases and gas-ratio mixtures for fault analysis of oil-immersed energy transformers. In Proceedings of 2014 International Symposium on Electrical Insulating Materials (eds Zhao, A.-X. et al.) 124–127 (IEEE, 2014).Chapter
Google Scholar
Faiz, J. & Soleimani, M. Dissolved fuel evaluation analysis in electrical energy transformers using typical strategies a overview. IEEE Trans. Dielectr. Electr. Insul. 24(2), 1239–1248 (2017).Article
CAS
Google Scholar
Endo, F. et al. Analysis of gasses generated by electrical discharges in low viscosity silicone oil. In 2008 International Conference on Condition Monitoring and Diagnosis (eds Endo, F. et al.) 272–275 (IEEE, 2008).Chapter
Google Scholar
Oehlmann, Okay. & Mao, L. Evaluation of distribution community transformer dissolved fuel evaluation (DGA) information. In PES T&D 2012 (eds Oehlmann, Okay. & Mao, L.) 1–4 (IEEE, 2012).
Google Scholar
Abbasi, A. R. Fault detection and analysis in energy transformers: A complete overview and classification of publications and strategies. Electr. Power Syst. Res. 209, 107990 (2022).Article
Google Scholar
IEC. Mineral Oil-Impregnated Electrical Equipment in Service-Guide to the Interpretation of Dissolved and Free Gases Analysis (CEI, 2015).
Google Scholar
B. EN60599, “and IEC 60599: 1999 Mineral oil-impregnated electrical tools in service information to the interpretation of dissolved and free gases evaluation,” British Standard Institute (BSI) and International Electrotechnical Commission (IEC) (1999).Singh, J., Singh, S. & Singh, A. Distribution transformer failure modes, results and criticality evaluation (FMECA). Eng. Fail. Anal. 99, 180–191 (2019).Article
Google Scholar
Fernández, I., Ortiz, A., Delgado, F., Renedo, C. & Perez, S. Comparative analysis of different fluids for energy transformers. Electr. Power Syst. Res. 98, 58–69 (2013).Article
Google Scholar
I. E. Commission, “International Standard IEC 60599: 1999+ A1: 2007, Mineral oil-impregnated electrical tools in service–a information to the interpretation of dissolved and free gases evaluation (1999).Landucci Júnior, A. Manutenção pré-corretiva em transformadores de potência: um novo conceito de manutenção (2017).Babukutty, S. & Khule, S. Dissolved fuel evaluation: A proactive strategy for recognization of faults in transformer using MATLAB GUI. J. Adv. Res. Signal Process. Appl. 1(2), 14–19 (2021).
Google Scholar
Modi, M. M. & Patel, R. A. Intelligent strategy to interpret incipient faults of energy transformer from DGA database. J. Inst. Eng. (India) Ser. B 104, 1–8 (2023).
Google Scholar
J. Costa, D. Silva, and P. Branco, “Large-Power Transformers: Time Now for Addressing Their Monitoring and Failure Investigation Techniques. Energies 2022, 15, 4697, ed: s Note: MDPI stays impartial about jurisdictional claims in printed …, (2022).Robalino, D. M. & Mahajan, S. M. Dissolved fuel evaluation of a thermally overloaded oil-immersed present transformer. In 2010 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America (T&D-LA) (eds Robalino, D. M. & Mahajan, S. M.) 645–652 (IEEE, 2010).Chapter
Google Scholar
Merita, A. Okay., Sharma, R. & Chauhan, S. Study and analysis of key gases to evaluate the situation of energy transformers. In 2011 third International Conference on Electronics Computer Technology Vol. 4 (eds Merita, A. Okay. et al.) 343–347 (IEEE, 2011).Chapter
Google Scholar
Irungu, G., Akumu, A. & Munda, J. Fault diagnostics in oil-filled electrical tools: Review of duval triangle and the likelihood of alternate options. In 2016 IEEE Electrical Insulation Conference (EIC) (eds Irungu, G. et al.) 174–177 (IEEE, 2016).Chapter
Google Scholar
Putra, M. A. A., Prasojo, R. A., Novfowan, A. D. & Neelmani, N. Dissolved fuel evaluation of generator step up transformer in Grati energy plant using random forest based mostly technique. J. Telecommun. Netw. (Jurnal Jaringan Telekomunikasi) 13(1), 51–58 (2023).Article
Google Scholar
Duval, M. A overview of faults detectable by gas-in-oil evaluation in transformers. IEEE Electr. Insul. Mag. 18(3), 8–17 (2002).Article
Google Scholar
Bulut, M. & Özcan, E. A brand new strategy to find out upkeep durations of essentially the most essential hydroelectric energy plant tools. Reliab. Eng. Syst. Saf. 205, 107238 (2021).Article
Google Scholar
Saroja, S., Haseena, S. & Madhavan, R. Dissolved fuel evaluation of transformer: An strategy based mostly on ML and MCDM. IEEE Trans. Dielectr. Electr. Insul. https://doi.org/10.1109/TDEI.2023.3271609 (2023).Article
Google Scholar
Baker, E., Nese, S. V. & Dursun, E. Hybrid situation monitoring system for energy transformer fault analysis. Energies 16(3), 1151 (2023).Article
Google Scholar
Manoj, T. & Ranga, C. An intelligence knowledgeable system for high quality well being monitoring of energy transformers using testing attributes. Eng. Res. Express 5(2), 025033 (2023).Article
Google Scholar
Zhang, D. et al. Transformer upkeep choice based mostly on situation monitoring and fuzzy chance hybrid reliability evaluation. IET Gen. Transm. Distrib. 17(4), 976–992 (2023).Article
Google Scholar
Altayef, E., Anayi, F., Packianather, M., Benmahamed, Y. & Kherif, O. Detection and classification of lamination faults in A 15 kVA three-phase transformer core using SVM, KNN and DT algorithms. IEEE Access 10, 50925–50932 (2022).Article
Google Scholar
Han, X. et al. A novel energy transformer fault analysis mannequin based mostly on harris-hawks-optimization algorithm optimized kernel excessive learning machine. J. Electr. Eng. Technol. 17(3), 1993–2001 (2022).Article
Google Scholar
Behkam, R., Karami, H., Naderi, M. S. & Gharehpetian, G. Condition monitoring of distribution transformers using frequency response traces and synthetic neural community to detect the extent of windings axial displacements. In 2022 twenty sixth International Electrical Power Distribution Conference (EPDC) (eds Behkam, R. et al.) 18–23 (IEEE, 2022).Chapter
Google Scholar
Jiang, J., Chen, R., Chen, M., Wang, W. & Zhang, C. Dynamic fault prediction of energy transformers based mostly on hidden Markov mannequin of dissolved gases evaluation. IEEE Trans. Power Deliv. 34(4), 1393–1400 (2019).Article
Google Scholar
Ma, X., Hu, H. & Shang, Y. A brand new technique for transformer fault prediction based mostly on multi-feature enhancement and refined lengthy short-term reminiscence. IEEE Trans. Instrum. Meas. 70, 1–11 (2021).
Google Scholar
Li, J., Li, G., Hai, C. & Guo, M. Transformer fault analysis based mostly on multi-class AdaBoost algorithm. IEEE Access 10, 1522–1532 (2021).Article
Google Scholar
Rodríguez, J., Contreras, J. & Gaytán, C. Evaluation and interpretation of dissolved fuel evaluation of soybean-based pure ester insulating liquid. IEEE Trans. Dielectr. Electr. Insul. 28(4), 1343–1348 (2021).Article
Google Scholar
Laayati, O. et al. Toward smarter energy transformers in microgrids: A multi-agent reinforcement learning for diagnostic. In Digital Technologies and Applications: Proceedings of ICDTA’23, Fez, Morocco Vol. 2 (eds Motahhir, S. & Bossoufi, B.) 640–649 (Springer, 2023).Chapter
Google Scholar
Li, J., Hai, C., Feng, Z. & Li, G. A transformer fault analysis technique based mostly on parameters optimization of hybrid kernel excessive learning machine. IEEE Access 9, 126891–126902 (2021).Article
Google Scholar
Boonsaner, N., Chancharoensook, P., Bunnag, C., Suwantaweesuk, A. & Vongphanich, Okay. The examine and evaluation of oil-immersed energy transformer by using synthetic neural community for designing program apply within the business of testing oil-immersed transformers. In 2020 eighth International Conference on Condition Monitoring and Diagnosis (CMD) (eds Boonsaner, N. et al.) 274–277 (IEEE, 2020).Chapter
Google Scholar
Suksagoolpanya, S. et al. Dissolved fuel evaluation of palm oil in contrast with mineral oil from differing types of breakdown voltage. In 2020 eighth International Conference on Condition Monitoring and Diagnosis (CMD) (eds Suksagoolpanya, S. et al.) 330–333 (IEEE, 2020).Chapter
Google Scholar
Shutenko, O. & Kulyk, O. Comparative evaluation of the defect sort recognition reliability in high-voltage energy transformers using totally different strategies of DGA outcomes interpretation. In 2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP) (eds Shutenko, O. & Kulyk, O.) 1–6 (IEEE, 2020).
Google Scholar
Syafruddin, H. & Nugroho, H. P. Dissolved fuel evaluation (DGA) for analysis of fault in oil-immersed energy transformers: A case examine. In 2020 4th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM) (eds Syafruddin, H. & Nugroho, H. P.) 57–62 (IEEE, 2020).
Google Scholar
Patel, R. & Thakur, H. Integrated fuzzy strategy for incipient fault detection in energy transformers. In 2016 IEEE Industrial Electronics and Applications Conference (IEACon) (eds Patel, R. & Thakur, H.) 211–218 (IEEE, 2016).Chapter
Google Scholar
Rai, A. S. & Maheshan, C. Artificial bee colony algorithm based mostly interpretation of dissolved fuel evaluation. In 2019 4th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT) (eds Rai, A. S. & Maheshan, C.) 106–110 (IEEE, 2019).Chapter
Google Scholar
Mohamad, F., Hosny, Okay. & Barakat, T. Incipient fault detection of electrical energy transformers using fuzzy logic based mostly on Roger’s and IEC technique. In 2019 14th International Conference on Computer Engineering and Systems (ICCES) (eds Mohamad, F. et al.) 303–309 (IEEE, 2019).Chapter
Google Scholar
Ghoneim, S., Mansour, D., Bedir, I. & Alharthi, M. A call transformer fault diagnostics system based mostly on dissolved fuel evaluation. In 2019 twenty first International Middle East Power Systems Conference (MEPCON) (eds Ghoneim, S. et al.) 76–80 (IEEE, 2019).Chapter
Google Scholar
Zeng, B. et al. A transformer fault analysis mannequin based mostly on hybrid gray wolf optimizer and LS-SVM. Energies 12(21), 4170 (2019).Article
Google Scholar
Benmahamed, Y., Teguar, M. & Boubakeur, A. Diagnosis of energy transformer oil using PSO-SVM and KNN classifiers. In 2018 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM) (eds Benmahamed, Y. et al.) 1–4 (IEEE, 2018).
Google Scholar
Ghosh, A. & Banerjee, S. A comparability between classical and superior controllers for a lift converter. In 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (eds Ghosh, A. & Banerjee, S.) 1–6 (IEEE, 2018).
Google Scholar
Zope, N., Ali, S. I., Padmanaban, S., Bhaskar, M. S. & Mihet-Popa, L. Analysis of 132kV/33kV 15MVA energy transformer dissolved fuel using transport-X Kelman Kit by means of Duval’s triangle and Roger’s Ratio prediction. In 2018 IEEE International Conference on Industrial Technology (ICIT) (eds Zope, N. et al.) 1160–1164 (IEEE, 2018).Chapter
Google Scholar
Hamid, M. et al. Dissolved fuel evaluation (DGA) of vegetable oils beneath electrical stress. In 2017 International Conference on High Voltage Engineering and Power Systems (ICHVEPS) (eds Hamid, M. et al.) 29–34 (IEEE, 2017).Chapter
Google Scholar
Wattakapaiboon, W. & Pattanadech, N. The state of the artwork for dissolved fuel evaluation based mostly on interpretation techniques. In 2016 International Conference on Condition Monitoring and Diagnosis (CMD) (eds Wattakapaiboon, W. & Pattanadech, N.) 60–63 (IEEE, 2016).Chapter
Google Scholar
Su, C. Q. A brand new fuzzy logic technique for transformer incipient fault analysis. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (ed. Su, C. Q.) 324–327 (IEEE, 2016).Chapter
Google Scholar
Wannapring, E., Suwanasri, C. & Suwanasri, T. Dissolved fuel evaluation strategies for distribution transformers. In 2016 thirteenth International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (eds Wannapring, E. et al.) 1–6 (IEEE, 2016).
Google Scholar
Illias, H. A. & Chai, X. R. Hybrid modified evolutionary particle swarm optimization-time various acceleration coefficient-artificial neural community for energy transformer fault analysis. Measurement 90, 94–102 (2016).Article
Google Scholar
Ghoneim, S. S., Taha, I. B. & Elkalashy, N. I. Integrated ANN-based proactive fault diagnostic scheme for energy transformers using dissolved fuel evaluation. IEEE Trans. Dielectr. Electr. Insul. 23(3), 1838–1845 (2016).Article
Google Scholar
Bhalla, D., Bansal, R. Okay. & Gupta, H. O. Transformer incipient fault analysis based mostly on DGA using fuzzy logic. In India International Conference on Power Electronics 2010 (IICPE2010) (eds Bhalla, D. et al.) 1–5 (IEEE, 2011).
Google Scholar
Malik, H. & Mishra, S. Feature choice using RapidMiner and classification by means of a probabilistic neural community for fault diagnostics of energy transformer. In 2014 Annual IEEE India Conference (INDICON) (eds Malik, H. & Mishra, S.) 1–6 (IEEE, 2014).
Google Scholar
Nagpal, T. & Brar, Y. S. Neural community based mostly transformer incipient fault detection. In 2014 International Conference on Advances in Electrical Engineering (ICAEE) (eds Nagpal, T. & Brar, Y. S.) 1–5 (IEEE, 2014).
Google Scholar
Gómez, N., Wilhelm, H., Santos, C. & Stocco, G. Dissolved fuel evaluation (DGA) of pure ester insulating fluids with totally different chemical compositions. IEEE Trans. Dielectr. Electr. Insul. 21(3), 1071–1078 (2014).Article
Google Scholar
Ahmed, M., Geliel, M. & Khalil, A. Power transformer fault analysis using a fuzzy logic approach based mostly on dissolved fuel evaluation. In twenty first Mediterranean Conference on Control and Automation (eds Ahmed, M. et al.) 584–589 (IEEE, 2013).Chapter
Google Scholar
Mehta, A. Okay., Sharma, R., Chauhan, S. & Saho, S. Transformer diagnostics beneath dissolved fuel evaluation using assist vector machine. In 2013 International Conference on Power, Energy, and Control (ICPEC) (eds Mehta, A. Okay. et al.) 181–186 (IEEE, 2013).Chapter
Google Scholar
Ghunem, R. A., Assaleh, Okay. & El-Hag, A. H. Artificial neural networks with stepwise regression for predicting transformer oil furan content material. IEEE Trans. Dielectr. Electr. Insul. 19(2), 414–420 (2012).Article
CAS
Google Scholar
Neyman, J. & Pearson, E. S. IX. On the issue of essentially the most environment friendly exams of statistical hypotheses. Philos. Trans. R. Soc. Lond. Ser. A Contain. Pap. Math. Phys. Charact. 231(694–706), 289–337 (1933).
Google Scholar
Afrasiabi, S., Afrasiabi, M., Parang, B. & Mohammadi, M. Designing a composite deep learning based mostly differential safety scheme of energy transformers. Appl. Soft Comput. 87, 105975 (2020).Article
Google Scholar
Rajesh, Okay. N., Rao, U. M., Fofana, I., Rozga, P. & Paramane, A. Influence of information balancing on transformer DGA fault classification with machine learning algorithms. IEEE Trans. Dielectr. Electr. Insul. 30(1), 385–392 (2022).Article
Google Scholar
Shamsudin, M. S., Yakub, F., Shapiai, M. I., Mohmad, A. & Abd Hamid, N. A. Investigation on on-line DGA monitoring to find out transformer well being index using machine learning. J. Phys. Conf. Ser. 2128(1), 012024 (2021).Article
Google Scholar
Singh, S., Raj, N., Gera, R. & Dinkar, A. National convention on the failure of main tools’s of sub-station-case research Twenty third-Twenty fourth February 2023, New Delhi. Water Energy Int. 65(12), 55–58 (2023).
Google Scholar
Siregar, Y. & Lumbanraja, T. J. H. Analysis of interference strategies on transformers based mostly on the outcomes of dissolved fuel evaluation exams. Int. J. Electr. Comput. Eng https://doi.org/10.11591/ijece.v13i4.pp3672-3685 (2023).Article
Google Scholar
Manoj, T., Ranga, C., Ghoneim, S. S., Rao, U. M. & Abdelwahab, S. A. M. Alternate and efficient dissolved fuel interpretation to grasp the transformer incipient faults. IEEE Trans. Dielectr. Electr. Insul. https://doi.org/10.1109/TDEI.2023.3237795 (2023).Article
Google Scholar
Standard, I. Mineral Oil-Impregnated Electrical Equipment in SERVICE-GUIDE to the Interpretation of Dissolved and Free Gases Analysis (IEC, 2015).
Google Scholar
Taha, I. B., Ghoneim, S. S. & Zaini, H. G. Improvement of Rogers 4 ratios and IEC Code strategies for transformer fault analysis based mostly on Dissolved Gas Analysis. In 2015 North American Power Symposium (NAPS) (eds Taha, I. B. et al.) 1–5 (IEEE, 2015).
Google Scholar
Taha, I. B., Ghoneim, S. S. & Duaywah, A. S. Refining DGA strategies of IEC Code and Rogers 4 ratios for transformer fault analysis. In 2016 IEEE Power and Energy Society General Meeting (PESGM) (eds Taha, I. B. et al.) 1–5 (IEEE, 2016).
Google Scholar
Duval, M. Dissolved fuel evaluation: It can save your transformer. IEEE Electr. Insul. Mag. 5(6), 22–27 (1989).Article
Google Scholar
Duval, M. & Lamarre, L. The Duval pentagon-a new complementary software for the interpretation of dissolved fuel evaluation in transformers. IEEE Electr. Insul. Mag. 30(6), 9–12 (2014).Article
Google Scholar
International Electrotechnical Commission. Interpretation of the evaluation of gases in transformers and different oil-filled electrical tools in service. Publication 599, 21–25 (1978).
Google Scholar
https://www.nature.com/articles/s41598-023-50833-7