Abstract
A transformer is an electrical static machine used for changing the level of voltage as a ratio of the primary to secondary turns. At the distributor side of the power system network, transformers are required to step down the voltage from distributor voltage (high voltage) to the level required by industries and consumers (230 V). Oil is used in oil-core transformers for the purpose of cooling as well as insulation. The maintenance of transformer oil is essential as chemical reactions and dielectric breakdown occur due to the presence of anomalies and even at nominal operating conditions with ageing. This paper deals with various offline and online transformer oil monitoring methods which have been used until recent times. The pros and cons of the various methods and interpretation techniques for fault detection and fault-type prediction have been presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Copiun, M.K.: Coieitioy Monitoring of HV Transformers Using Oil Analysis Techniques, pp. 1–2
Xianyong, L., Fangjie, Z., Fenglei, H.: Research on on-line DGA using FTIR. In: Proceedings International Conference on Power System Technology 2002, pp. 1875–1880 (2002)
Kita, D.M., Lin, H., Agarwal, A., Richardson, K., Luzinov, I., Gu, T., Hu, J.: On-chip infrared spectroscopic sensing: redefining the benefits of scaling. IEEE J. Sel. Top. Quantum Electroni. 23(2), 1–10 (2017)
Gray, I.A.R.: Transformer Chemistry Services A Guide to Transformer Oil Analysis, pp. 1–13
Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers IEEE Standard, C57.104 (2008)
Product Information Dissolved gas analysis and supervision of oil condition in transformers and reactors, ABB company. http://www.abb.com
Sun, C., Ohodnicki, P.R., Stewart, E.M.: Chemical sensing strategies for real-time monitoring of transformer oil: a review. IEEE Sens. J. 17(18), 5786–5806 (2017)
Thangam, G.K., Rajasaranya, T.: TRANS-INFORMER—an integrated system for health monitoring of power transformers. Int. J. Comput. Sci. Mobile Comput., IJCSMC 2(4), 105–110 (2013)
Schwarz, R., Muhr, M.: Diagnostic methods for transformers. In: International Conference on Condition Monitoring and Diagnosis, Beijing, China (2008)
Kim, Y.M., Lee, S.J., Seo, H.D., Jung, J.R., Yang, H.J.: Development of dissolved gas analysis (DGA) expert system using new diagnostic algorithm for oil—immersed transformers. In: IEEE International Conference on Condition Monitoring and Diagnosis, pp. 365–369, Bali, Indonesia (2012)
Hamrick, L.: Dissolved Gas Analysis for Transformers Neta World (2010). http://www.netaworld.org
Life Management Techniques for Power Transformers. Technical Brochure 227—CIGRE (2013)
Duval, M., Dukarm, J.: Improving the Reliability of transformer gas-in-oil diagnosis. IEEE Electr. Insul. Mag. 21(4), 21–27 (2005)
Abu-Elanien, A.E.B., Salama, M.M.A.: Survey on the transformer condition monitoring. In: Large Engineering Systems Conference on Power Engineering, pp. 187–191 (2007)
Chang, W., Hao N., (2011) Prediction of dissolved gas content in transformer oil based on genetic programming and DGA. In: International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), pp. 1133–1136, Changchun, China (2011)
Abu Bakar, N., Abu-Siada, A.: High voltage power transformer dissolved gas analysis. In: Measurement and Interpretation Techniques IDC Technologies, pp. 1–17 (2017)
Abu Bakar, N., Abu-Siada A., Islam, S.: A review of dissolved gas analysis measurement and interpretation techniques. IEEE Electr. Insul. Mag. 30(3), 39–49 (2014)
Makky, M.: Review of Transformer On-Line Condition Monitoring (2014)
Abu Bakar, N., Abu-Saida, A.: A new method to detect dissolved gases in transformers oil using NIR-IR spectroscopy. IEEE Trans. Dielectr. Electr. Insul. 24(1), 409–419 (2017)
Geibler D., Jaya, M., Leibfried, T.: Analysis of frequency domain spectroscopy measurements on power transformers by the use of a finite element based model. In: IEEE International Conference on Condition Monitoring and Diagnosis, pp. 206–210, Bali, Indonesia (2012)
Aburaghiega, E., Farrag, M.E., Hepburn Glasgow, D.M., Garcia, B.: Power transformer health monitoring: a shift from off-line to on-line detection. In: 50th International Universities Power Engineering Conference (UPEC) (2015)
Redding, B., Liew, S.F., Sarma, R., Cao, H.: On-chip random spectrometer. In: Conference on Lasers and Electro-Optics Europe and International Quantum Electronics Conference CLEO EUROPE/IQEC (2013)
Arvind, D., Khushdeep, S., Deepak, K.: Condition monitoring of power transformer: a review. In IEEE/PES Transmission and Distribution Conference and Exposition (2008)
Hussain, M., Salman, M., Rohit, Subhan, A., Khalid, H., Zaidi, S.S.H.: Condition based health monitoring of transformers. In: International Conference on Computing, Mathematics and Engineering Technologies—iCoMET (2018)
Chen, G., Xu, M., Liu, T., Ni, J., Xie, D., Zhang, Y.: Research on condition monitoring and evaluation method of power transformer. In: Third International Conference on Intelligent System Design and Engineering Applications, pp. 1155–1158 (2013)
Avinash Nelson, A., Jaiswal, G.C., Ballal, M.S., Tutakne, D.R.: Remote condition monitoring system for distribution transformer. In: IEEE Transaction on Instrumentation and Measurement (2014)
Rahman, S., Dey, S.K., Bhawmick, B.K., Das, N.K.: Design and implementation of real time transformer health monitoring system using GSM technology. In: International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 258–261, Bangladesh (2017)
Pawar, R.R., Deosarkar, S.B.: Health condition monitoring system for distribution transformer using internet of things (IoT). In: IEEE International Conference on Computing Methodologies and Communication (ICCMC), pp. 117–122 (2017)
Fan, J., Wang, F., Sun, Q., Bin, F., Ding, J., Ye, H.: SOFC detector for portable gas chromatography: high-sensitivity detection of dissolved gases in transformer oil. IEEE Trans. Dielectr. Electr. Insul. 24(5), 2854–2863 (2017)
Tenbohlen, S., Figel, F.: On-line condition monitoring of power transformers. In: IEEE Winter Meeting, pp. 1–5., Singapore (2002)
Malik, H., Singh, S., Mantosh, K.R., Jarial, R.K.: UV/VIS response based fuzzy logic for health assessment of transformer oil. In: International Conference on Communication Technology and System Design, vol. 30, 905–912 (2011)
de Pablo, A., Fergusan, W., Mudryk, A., Golovan, D.: On-line condition monitoring of power transformers—a case history. In: Electrical Insulation Conference, pp 285–288. Annapolis, Maryland (2011)
Mineral oil-impregnated equipment in service- interpretation of dissolved and free gases analysis IEC 60599 (2007)
Pan, C., Chen, W., Yun, Y.: Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network .IET Electr. Power Appl. 2(1), 71–76 (2008)
Hao, X., Sun, C.: Artificial immune network classification algorithm for fault diagnosis of power transformer, IEEE Trans. Power Deliv. 22(2), 930–935 (2007)
Duval, M.: A review of faults detectable by gas-in-oil analysis in transformers. IEEE Insul. Mag., 2002 18(3), 8–17 (2002)
Geetha, R.: Fault diagnosis of power transformer using duval triangle based artificial intelligence techniques. Int. J. Recent Trends Eng. Res. (IJRTER) 2(10), 78–88 (2016)
Muhamad, N.A., Phung, B.T., Blackburn, T.R.: Comparative study and analysis of DGA methods for mineral oil using fuzzy logic. In: 8th International Power Engineering Conference, pp. 1301–1306 (2007)
Illias, H.A., Chai, X.R. Abu Bakar, H., Mokhlis, H.: Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimization Techniques, pp. 1–16 (2015)
AzilIllia H., Zhao Liang, W.: Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimization, pp. 1–15 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rengaraj, R., Venkatakrishnan, G.R., Moorthy, P., Pratyusha, R., Ritika, Veena, K. (2021). Transformer Oil Health Monitoring Techniques—An Overview. In: Suresh, P., Saravanakumar, U., Hussein Al Salameh, M. (eds) Advances in Smart System Technologies. Advances in Intelligent Systems and Computing, vol 1163. Springer, Singapore. https://doi.org/10.1007/978-981-15-5029-4_12
Download citation
DOI: https://doi.org/10.1007/978-981-15-5029-4_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5028-7
Online ISBN: 978-981-15-5029-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)