Implementasi Machine Learning pada Sistem Informasi Pemeliharaan Transformator Daya

Hilda Khoirotul Hidayah; Ekojono Ekojono; Endah Septa Sintiya

doi:10.33795/jip.v10i4.6008

Authors

Hilda Khoirotul Hidayah Politeknik Negeri Malang
Ekojono Ekojono Politeknik Negeri Malang
Endah Septa Sintiya Politeknik Negeri Malang

DOI:

https://doi.org/10.33795/jip.v10i4.6008

Keywords:

Health Index, Dissolved Gas Analysis, Duval Triangle Method, Duval Pentagon Method, support vector machine, artificial neural network, random forest

Abstract

Listrik memegang peranan yang tidak tergantikan dalam kehidupan manusia sehari-hari. Untuk memenuhi kebutuhan energi listrik yang semakin meningkat, diperlukan suatu sistem kelistrikan yang handal, seperti transformator daya. Transformator daya memegang peranan penting dalam sistem tenaga listrik, dimana keandalan transformator dalam jangka panjang sangat erat kaitannya dengan keselamatan dan kestabilan sistem tenaga listrik. Oleh karena itu, pemeliharaan transformator harus dilakukan untuk mengantisipasi kegagalan mendadak dan menjamin keandalan sistem tenaga listrik secara keseluruhan. Penting untuk melakukan penilaian kesehatan untuk mengetahui kondisi transformator daya. Penilaian ini dapat dilakukan dengan berbagai cara, termasuk Health Index (Indeks Kesehatan) dan Dissolved Gas Analysis (Analisis Gas Terlarut). Metode Duval Pentagon dan Metode Duval Triangle digunakan dalam Dissolved Gas Analysis untuk memastikan kondisi transformator. Tujuan dari penelitian ini adalah membandingkan tiga model machine learning—Support Vector Machine, Artificial Neural Network, dan Random Forest—menggunakan dataset Duval Pentagon Method dan Duval Triangle Method untuk mendapatkan model dengan akurasi tertinggi. Model dengan akurasi tertinggi akan diimplementasikan pada sistem informasi manajemen transformator untuk mengetahui kondisi transformator. Dalam sistem ini juga diterapkan perhitungan Health Index dengan minyak dan kertas sebagai parameter hasil pengujian. Hasil perhitungan Health Index dan Dissolved Gas Analysis dapat menentukan rekomendasi tindakan transformator yang tepat. Dengan demikian, sistem ini dapat memudahkan tenaga ahli transformator dalam menjaga kesehatan transformator daya.

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References

Abdullah, A. M., Ali, R., Yaacob, S. B., Mansur, T., & Baharudin, N. H. (2021). Prediction of Transformer Health Index Using Condition Situation Monitoring (CSM) Diagnostic Techniques. Journal of Physics: Conference Series, 1878(1). https://doi.org/10.1088/1742-6596/1878/1/012007

Alqudsi, A., & El-Hag, A. (2019). Application of machine learning in transformer health index prediction. Energies, 12(14). https://doi.org/10.3390/en12142694

Amalia, D., Murdiya, F., & Elektro, J. T. (2017). Analisa Gas Terlarut Pada Minyak Transformator Daya 150 kV Dengan Menggunakan Metode Duval Pentagon. In Jom FTEKNIK (Vol. 4, Issue 1).

Bustamante, S., Manana, M., Arroyo, A., Castro, P., Laso, A., & Martinez, R. (2019). Dissolved gas analysis equipment for online monitoring of transformer oil: A review. In Sensors (Switzerland) (Vol. 19, Issue 19). MDPI AG. https://doi.org/10.3390/s19194057

C57.104-2019 - IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers. (2019). IEEE.

Condition assessment of power transformers. (2019).

Foundations_of_Machine_Learning_second_e. (n.d.).

Furqaranda, R., Bintoro, A., Asri, A., Al-Ani, W. K. A., & Shrestha, A. (2022). Analysis Oil Condition of Transformer PT-8801-A by Using the Method TDCG, Rogers Ratio, Key Gas, and Duval Triangle: A Case Study at PT. Perta Arun Gas. Journal of Renewable Energy, Electrical, and Computer Engineering, 2(2), 47. https://doi.org/10.29103/jreece.v2i2.8567

Gouda, O. E., El-Hoshy, S. H., & Tamaly, H. H. E. L. (2019). Condition assessment of power transformers based on dissolved gas analysis. IET Generation, Transmission and Distribution, 13(12), 2299–2310. https://doi.org/10.1049/iet-gtd.2018.6168

Guo, H., & Guo, L. (2022). Health index for power transformer condition assessment based on operation history and test data. Energy Reports, 8, 9038–9045. https://doi.org/10.1016/j.egyr.2022.07.041

International Electrotechnical Commission., & International Electrotechnical Commission. Technical Committee 10. (2013). Mineral insulating oils in electrical equipment--supervision and maintenance guidance = Huiles minérales isolantes dans les matériels électriques--lignes directrices pour la maintenance et la surveillance. International Electrotechnical Commission.

Introduction_to_Machine_Learning_fourth. (n.d.).

Isaac Abiodun, O., Jantan, A., Esther Omolara, A., Victoria Dada, K., AbdElatif Mohamed, N., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4, e00938. https://doi.org/10.1016/j.heliyon.2018

Khatib Sulaiman, J., Agung Nurcahyo, J., Bayu Sasongko, T., Amikom Yogyakarta, U., & Kunci, K. (n.d.). Hyperparameter Tuning Algoritma Supervised Learning untuk Klasifikasi Keluarga Penerima Bantuan Pangan Beras. Indonesian Journal of Computer Science.

Martínez Gomez, M. (2023). Transformers. In J. García (Ed.), Encyclopedia of Electrical and Electronic Power Engineering (pp. 247–255). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-821204-2.00126-4

Prasojo, R. A., & Suwarno. (2018). Power transformer paper insulation assessment based on oil measurement data using SVM-classifier. International Journal on Electrical Engineering and Informatics, 10(4), 661–673. https://doi.org/10.15676/ijeei.2018.10.4.4

Rahman Hidayat, A., Jamal, A., Nur Nazilah Chamim, A., Syahputra, R., Zainal Abidin Pagar Alam, J., Ratu, L., & Lampung, B. (2019). Analysis of Power Transformer Insulation: A Case Study in 150 kV Bantul Substation. Journal of Electrical Technology UMY (JET-UMY), 3(2).

Rahman Khalil, B., & Mahmood Faqe Hussein, M. (2023). HEART FAILURE PATIENTS ARE CLASSIFIED USING THE RANDOM FOREST AND NAÏVE BAYES ALGORITHMS. https://dbdxxb.cn/

Rediansyah, D., Prasojo, R. A., & Suwarno. (2021). Study on Artificial Intelligence Approaches for Power Transformer Health Index Assessment. Proceedings of the International Conference on Electrical Engineering and Informatics. https://doi.org/10.1109/ICEEI52609.2021.9611109

Safety of machinery - electrical equipment of machinesnPart 1, General requirements. (n.d.).

Tamma, W. R., Prasojo, R. A., & Suwarno. (2021). High voltage power transformer condition assessment considering the health index value and its decreasing rate. High Voltage, 6(2), 314–327. https://doi.org/10.1049/hve2.12074

Tamma, W. R., Prasojo, R. A., & Suwarno, S. (2020). Assessment of High Voltage Power Transformer Aging Condition Based on Health Index Value Considering Its Apparent and Actual Age. ICITEE 2020 - Proceedings of the 12th International Conference on Information Technology and Electrical Engineering, 292–296. https://doi.org/10.1109/ICITEE49829.2020.9271778

Togatorop, P. R., Sianturi, M., Simamora, D., & Silaen, D. (2022). Optimizing Random Forest using Genetic Algorithm for Heart Disease Classification. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 13(1), 60. https://doi.org/10.24843/lkjiti.2022.v13.i01.p06