Classification Method in Fault Diagnosis of Oil-Immersed Power Transformers by Considering Dissolved Gas Analysis
Fault detection in the incipient stage is necessary to avoid hazardous operating conditions and reduce outage rates in transformers. Fault-detected dissolved gas analysis is widely used to detect incipient faults in oil-immersed transformers. This paper proposes fault diagnosis transformers using an artificial neural network based on classification techniques. Data on the condition of transformer oil is assessed for dissolved gas analysis to measure the dissolved gas concentration in the transformer oil. This type of disturbance can affect the gas concentration in the transformer oil. Fault diagnosis is implemented, and fault reference is provided. The result of the NN method is more accurate than the Tree and Random Forest method, with CA and AUC values 0.800 and 0.913. This classification approach is expected to help fault diagnostics in power transformers.
IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers, IEEE Standard C57.104, New York, NY, USA, 2009.
British Standards Institute Staff. Mineral Oil-Impregnated Electrical Equipment in Service-Guide to the Interpretation of Dissolved and Free Gases Analysis, IEC Standard 60599, Geneva, Switzerland, 2015.
H. M. Wilhelm, C. C. Santos and G. B. Stocco, Dissolved gas analysis (DGA) of natural ester insulating fluids with different chemical compositions, IEEE Trans. Dielectr. Electr. Insul., vol. 21, no. 3, pp. 1071-1078, June 2014. DOI: https://doi.org/10.1109/TDEI.2014.6832250
Mineral Oil-Impregnated Electrical Equipment In Service-Guide To The Interpretation Of Dissolved And Free Gases Analysis., IEC Std. 60599, 2015.
M. D and L. Lamarre, The Duval Pentagon-A New Complementary Tool For The Interpretation Of Dissolved Gas Analysis In Transformers, IEEE Electr. Insul. Mag.,vol. 30, no. 6, pp. 9–12, 2014. DOI: https://doi.org/10.1109/MEI.2014.6943428
M. Duval and A. de Pabla, Interpretation Of Gas-In-Oil Analysis Using New Iec Publication 60599 and IEC Tc 10 Databases, IEEE Electr. Insul. Mag., Vol. 17, No. 2, pp. 31-41, 2001. DOI: https://doi.org/10.1109/57.917529
N. Bakar, A. Abu-Siada, and S. Islam, A review of dissolved gas analysis measurement and interpretation techniques, IEEE Elect. Insul. Mag., vol. 30, no. 3, pp. 39–49, May/Jun. 2014. DOI: https://doi.org/10.1109/MEI.2014.6804740
H. C. Sun, Y. C. Huang, and C. M. Huang, A review of dissolved gas analysis in power transformers, Energy Procedia, vol. 14, pp. 1220–1225, Mar. 2012.
A. Velasquez, M. Ricardo, M. Lara, and V. Jennifer, Principal Components Analysis and Adaptive Decision System Based On Fuzzy Logic For Power Transformer, Fuzzy Inf. Eng., vol. 9, no. 4, pp. 493–514, 2017. DOI: https://doi.org/10.1016/j.fiae.2017.12.005
H. Wei, Y. Wang, L. Yang, C. Yan, Y. Zhang, and R. Liao, A New Support Vector Machine Model Based On Improved Imperialist Competitive Algorithm For Fault Diagnosis Of Oil-Immersed Transformers, J. Elect. Eng. Technol., vol. 12, no. 2, pp. 830–839, 2017. DOI: https://doi.org/10.5370/JEET.2017.12.2.830
T. Kari, W. Gao, and D. Zhao, Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm, IET Gener. Transmiss. Distrib., vol. 12, no. 21, pp. 5672–5680, 2018. DOI: https://doi.org/10.1049/iet-gtd.2018.5482
Seifeddine, S., Khmais, B., Abdelkader, C.: Artificial Intelligence Toolsaided-Decision For Power Transformer Fault Diagnosis, Int. J. Comput. Appl., 38, pp. 1–8, 2012.
Mohammed El A. S., Mostefa B., Issouf F., Combining and Comparing Various Machine-Learning Algorithms To Improve Dissolved Gasanalysis Interpretation, IET The Institution of Engineering and Technology, vol. 12 pp. 3673-3679, June, 2018. DOI: https://doi.org/10.1049/iet-gtd.2018.0059
Yin, Y. Zhu and G. Yu, Power Transformer Fault Diagnosis Based on Support Vector Machine with Cross Validation and Genetic Algorithm, 2011 International Conference on Advanced Power System Automation and Protection, pp. 309-313, 2011. DOI: https://doi.org/10.1109/APAP.2011.6180419
J. Watada, S. Chen, and Y. Yabuuchi, A Rough Set Approach To Data Imputation And Its Application To A Dissolved Gas Analysis Dataset, Proc. IEEE Int. Conf. Comput. Meas. Control Sensor Netw. pp. 24–27, May 2017. DOI: https://doi.org/10.1109/CMCSN.2016.48
H.-C. Sun, Y.-C. Huang, and C.-M. Huang, A review of dissolved gas analysis in power transformers, Energy Procedia, vol. 14, pp. 1220– 1225, 2012. DOI: https://doi.org/10.1016/j.egypro.2011.12.1079
T. Suwanasri, E. Chaidee, and C. Adsoongnoen, Failure statistics and power transformer condition evaluation by dissolved gas analysis technique, International Conference on Condition Monitoring and Diagnosis, CMD 2008, pp. 492–496, 2008. DOI: https://doi.org/10.1109/CMD.2008.4580333
Witten, I., Frank, E., Data mining: practical machine learning tools andtechniques with java implementations, Morgan Kaufmann Press, SanFrancisco 2nd edn, CA, USA, March, 2005,
S. Sardari, M. Eftekhari, and F. Afsari, Hesitant Fuzzy Decision Tree Approach For Highly Imbalanced Data Classification, Appl. Soft Comput., vol. 61, pp. 727–741, Dec. 2017. DOI: https://doi.org/10.1016/j.asoc.2017.08.052
V. T. Tran, B.-S. Yang, M.-S. Oh, and A. C. C. Tan, Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference, Expert Syst. Appl., vol. 36, no. 2, pp. 1840–1849, March. 2009. DOI: https://doi.org/10.1016/j.eswa.2007.12.010
Y. Cui, J. Shi, and Z. Wang, Analog circuit fault diagnosis based on quantum clustering based multi-valued quantum fuzzification decision tree (QC-MQFDT), Measurement, vol. 93, pp. 421–434, Nov. 2016. DOI: https://doi.org/10.1016/j.measurement.2016.07.018
W. Deng, Y. Guo, J. Liu, Y. Li, D. Liu and L. Zhu, A missing power data filling method based on improved random forest algorithm, Chinese Journal of Electrical Engineering, vol. 5, no. 4, pp. 33-39, Dec. 2019. DOI: https://doi.org/10.23919/CJEE.2019.000025
L Breiman. Random forests. Machine Learning, 45(1): 5-32, 2001. DOI: https://doi.org/10.1023/A:1010933404324
T. K. Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8): 832-844, Aug. 1998 DOI: https://doi.org/10.1109/34.709601
B. Yegnanarayana, Artificial NNs. Prentice-Hall of India, New York, NY, USA, 2009.
Edgar A. J, Joselito M, Juan C. O, Juan C. S., Alejandro R., Hot-Spot Temperature Forecasting of the Instrument Transformer Using an Artificial NN, IEEE Access, vol. 8, pp. 164392-164406, Aug. 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3021673
G. Chakraborty and B. Chakraborty, A novel normalization technique for unsupervised learning in ANN, IEEE Trans. Neural Netw., vol. 11, no. 1, pp. 253–257, Jan. 2000. DOI: https://doi.org/10.1109/72.822529
E. Li, L. Wang and B. Song, Fault Diagnosis of Power Transformers With Membership Degree, IEEE Access, vol. 7, pp. 28791-28798, March. 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2902299
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