Transformer Maintenance Strategies: A K-Means Based Approach for 33 kV DTs

Kittisak  Chaisuwan; Paradon Boonmeeruk; Kiattisak  Wongsopanakul

Authors

Kittisak Chaisuwan Provincial Electricity Authority, Thailand
Paradon Boonmeeruk Prince of Songkla University, Thailand
Kiattisak Wongsopanakul Prince of Songkla University, Thailand.

Keywords:

Distribution transformer (DT) condition assessment, preventive maintenance planning, K-means clustering algorithm, Transformer insulation analysis, Provincial Electricity Authority (PEA)

Abstract

The distribution transformer (DT) is crucial for connecting utility providers to consumers, and its failure can disrupt the distribution network's reliability. The Provincial Electricity Authority (PEA) in Thailand manages a large number of transformers, necessitating efficient maintenance planning to prevent DT failures. This paper introduces a method for classifying the condition of 33 kV DTs without pre-existing cluster data, utilizing the K-means clustering algorithm on data from 150 samples. The dataset includes 7 features from DT annual maintenance records and the Geographic Information System (GIS) of PEA Southern Area 3. Key factors identified are insulation between high voltage and ground, high-low voltage, and low voltage-ground. The method categorizes DT conditions into three clusters: "poor," requiring urgent action; "risk," requiring close monitoring; and "normal," requiring routine maintenance. Validation with K-Nearest Neighbors yields an accuracy of 96.67%, demonstrating the effectiveness of the proposed classification method.

Author Biographies

Kittisak Chaisuwan, Provincial Electricity Authority, Thailand

Provincial Electricity Authority, Chatuchak, Bangkok, 10900, Thailand

Paradon Boonmeeruk, Prince of Songkla University, Thailand

Department of Electrical & Biomedical Engineering, Prince of Songkla University, Hatyai, Songkhla, 90110, Thailand.

Kiattisak Wongsopanakul, Prince of Songkla University, Thailand.

Department of Electrical & Biomedical Engineering, Prince of Songkla University, Hatyai, Songkhla, 90110, Thailand.

References

D. Granados-Lieberman, J. R. Razo-Hernandez, V. Venegas-Rebollar, J. C. Olivares-Galvan, and M. Valtierra-Rodriguez, "Harmonic PMU and fuzzy logic for online detection of short-circuited turns in transformers," Electric Power Systems Research, vol. 190, p. 106862, 2021.

M. Abdillah et al., "Prognostics Health Management (PHM) System for Power Transformer Using Kernel Extreme Learning Machine (K-ELM)," in Proceedings of the 2020 International Conference on Engineering and Information Technology for Sustainable Industry, 2020, pp. 1-6.

X. Zhao et al., "Experimental evaluation of transformer internal fault detection based on V–I characteristics," IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 4108-4119, 2019.

R. M. A. Velásquez, J. V. M. Lara, and A. Melgar, "Converting data into knowledge for preventing failures in power transformers," Engineering Failure Analysis, vol. 101, pp. 215-229, 2019.

A. J. Patil, A. Singh, and R. Jarial, "A novel fuzzy based technique for transformer health index computation," in 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), 2019: IEEE, pp. 1-6.

H. W. Choi, N. M. F. Qureshi, and D. R. Shin, "Comparative Analysis of Electricity Consumption at Home through a Silhouette-score prospective," in 2019 21st International conference on advanced communication technology (ICACT), 2019: IEEE, pp. 589-591.

R. A. Prasojo, K. Diwyacitta, and H. Gumilang, "Correlation of transformer paper deterioration to oil characteristics and dissolved gases," in 2017 International Conference on High Voltage Engineering and Power Systems (ICHVEPS), 2017: IEEE, pp. 40-45.

W. Wattakapaiboon and N. Pattanadech, "The new developed Health Index for transformer condition assessment," in 2016 International Conference on Condition Monitoring and Diagnosis (CMD), 2016: IEEE, pp. 32-35.

R. Kumari, M. Singh, R. Jha, and N. Singh, "Anomaly detection in network traffic using K-mean clustering," in 2016 3rd international conference on recent advances in information technology (RAIT), 2016: IEEE, pp. 387-393.

A. A. Nelson, G. C. Jaiswal, and S. Ballal, "Economical aspects of remote conditon monitoring system for distribution transformer," in 2014 international conference on power, automation and communication (INPAC), 2014: IEEE, pp. 45-49.

A. J. Ghanizadeh and G. Gharehpetian, "ANN and cross-correlation based features for discrimination between electrical and mechanical defects and their localization in transformer winding," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 21, no. 5, pp. 2374-2382, 2014.

M. Abdel-Hafez and A. M. Gaouda, "A new look at classification of transformer normal and abnormal currents," in Melecon 2010-2010 15th IEEE Mediterranean Electrotechnical Conference, 2010: IEEE, pp. 830-834.

X. Zeng, Y.-W. Chen, and C. Tao, "Feature selection using recursive feature elimination for handwritten digit recognition," in 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009: IEEE, pp. 1205-1208.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Machine learning, vol. 46, pp. 389-422, 2002.

I. Sarker, "Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2: 160," ed: ed, 2021.

M. Ahmed, R. Seraj, and S. M. S. Islam, "The k-means algorithm: A comprehensive survey and performance evaluation," Electronics, vol. 9, no. 8, p. 1295, 2020.

A. D. Ashkezari, N. Hosseinzadeh, A. Chebli, and M. Albadi, "Development of an enterprise Geographic Information System (GIS) integrated with smart grid," Sustainable Energy, Grids and Networks, vol. 14, pp. 25-34, 2018.

C.-T. Lee and S.-C. Horng, "Abnormality detection of cast-resin transformers using the fuzzy logic clustering decision tree," Energies, vol. 13, no. 10, p. 2546, 2020.

A. S. Mogos, X. Liang, and C. Y. Chung, "Distribution Transformer Failure Prediction for Predictive Maintenance Using Hybrid One-Class Deep SVDD Classification and Lightning Strike Failures Data," IEEE Transactions on Power Delivery, vol. 38, no. 5, pp. 3250-3261, 2023.

M. Raichura, N. Chothani, and D. Patel, "Efficient CNN‐XGBoost technique for classification of power transformer internal faults against various abnormal conditions," IET Generation, Transmission & Distribution, vol. 15, no. 5, pp. 972-985, 2021.