Identification of Transformer Anomalies Utilizing the AdaBoost Machine Learning Algorithm
Article Sidebar
Read Counter : 12
Download : 9
Main Article Content
Abstract
Electrical energy is one of the most important needs in the local area, concerning the provision of electrical power a transformer is needed to distribute electrical power to each house. The use of transformers in each area requires testing the oil content in them to assess the state of the transformer. The Duval Pentagon Method (DPM) and the Duval Triangle Method (DTM) can be used in tests to identify transformer interference. Owing to the vast quantity of transformers utilized in public energy distribution, the Adaboost machine learning method was applied to identify transformer disruptions. By categorizing test data on a dataset derived from tests conducted with the earlier DTM and DPM techniques, the AdaBoost algorithm predicts transformer disruptions. According to the findings of tests conducted using the best dataset, the division used 80% of the data for training and reserved 20% for testing, using a learning rate of 1 and an estimator of 400 for DTM. This resulted in an accuracy level of 91.1%, which is an excellent classification. In contrast, the DPM approach divides the dataset into 80% training and 20% testing, employs an estimator of 500, and has a learning rate of 0.5. This leads to an excellent classification accuracy rate of 84.9%.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The proposed policy for journals that offer open access
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
References
Ahmad, N., & Taqijudin, H. M. A. (1991). Indikator Kegagalan Kerja Dengan Uji Dissolved Gas Analysis Di Gardu Induk Probolinggo. Unisma.
Afrida, Y., & Fitriono. (2022). Analisa Kondisi Minyak Trafo Berdasarkan Hasil Uji Dissolved Gas Analisys Pada Trafo Daya #1 Di Pt.Pln (Persero) Gardu Induk Kotabumi. Electrician, 16(3), 355–358. https://doi.org/10.23960/elc.v16n3.2408
Bakar, N. A., Abu-Siada, A., Cui, H., & Li, S. (2017). Improvement of DGA interpretation using scoring index method. In ICEMPE 2017 - 1st Int. Conf. Electr. Mater. Power Equip. (pp. 502–506). https://doi.org/10.1109/ICEMPE.2017.7982139
Pattanadech, N., & Wattakapaiboon, W. (2019). Application of Duval pentagon compared with other DGA interpretation techniques: Case studies for actual transformer inspections including experience from power plants in Thailand. In Proceeding - 5th Int. Conf. Eng. Appl. Sci. Technol. ICEAST 2019 (pp. 1–4). Https://doi.org/10.1109/ICEAST.2019.8802523
Siburian, J. (2019). Karakteristik Transformator. J. Teknol. Energi UDA, VIII(21), 21, 23.
Zulkhulaifah, B. H. R. (2021). Analisis Pengaruh Ketidakseimbangan BebanTrafo Distribusi 20 Kv Terhadap Rugi-Rugi Daya dan Efisiensi pada Penyulang Hertasning Baru PT PLN (Persero) ULP Panakukkang Makassar. Pros. Semin. Nas. Tek. Elektro dan Inform., 1–6.
Yue, Y., Yang, D., & Han, D. (2022). Application of Duval Pentagon in State Diagnosis of on Load Tap Changer. J. Phys. Conf. Ser., 2247(1). https://doi.org/10.1088/1742-6596/2247/1/012021
Setiawan, K. (2023). Evaluasi partial discharge transformator 60 mva dengan metode duval triangle di pltu rembang. Retrieved from http://repository.unissula.ac.id/29939/1/Teknik Elektro_30601700016_fullpdf.pdf
Misto, M., & Haryono, H. (2019). Analisis Gas Terlarut pada Minyak Isolasi sebagai Indikator Kegagalan Transformator Daya dengan Metode Dissolved Gas Analysis. J. Tek. Elektro dan Komputasi, 1(2), 99–112. https://doi.org/10.32528/elkom.v1i2.3091
Gupta, A., Jain, K., Sood, Y. R., & Sharma, N. K. (2019). Comparative study of duval triangle with the new DGA interpretation scheme. In Vol. 509. Springer Singapore. https://doi.org/10.1007/978-981-13-0665-5_24
Prasojo, R. A., et al. (2023). Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique. Electron. Power Syst. Res., 220, 109361. https://doi.org/10.1016/j.epsr.2023.109361
Hu, H., Wang, D., Fu, J., Mao, Y., Zhang, Z., & Wu, D. (2020). Software implementation of automatic fault identification based on the Duval Pentagon. J. Phys. Conf. Ser., 1633(1). https://doi.org/10.1088/1742-6596/1633/1/012135
Duval, M., & Lamarre, L. (2014). The Duval Pentagon — A New Complementary Tool for the. IEEE Electr. Insul. Mag., 30(6), 9–12.
Bisri, A., & Wahono, R. S. (2015). Penerapan Adaboost untuk Penyelesaian Ketidakseimbangan Kelas pada Penentuan Kelulusan Mahasiswa dengan Metode Decision Tree. J. Intell. Syst., 1(1), 27–32.
Rohmah, A. (2020). Pandemi covid-19 dan dampaknya terhadap perilaku konsumen di indonesia. J. Inov. Penelit., 1(3), 1–4.
Khesya, N. (2021). Mengenal Flowchart dan Pseudocode Dalam Algoritma dan Pemrograman. Preprints, 1, 1–15. Retrieved from https://osf.io/dq45ef
Saepudin, S., Pudarwati, E., Warman, C., Sihabudin, S., & Giri, G. (2022). Perancangan Arsitektur Sistem Pemesanan Tiket Wisata Online Menggunakan Framework Zachman. J. Sisfokom (Sistem Inf. dan Komputer), 11(2), 162–171. https://doi.org/10.32736/sisfokom.v11i2.1415
Radhitya, M. L., & Sudipa, G. I. (2020). Pendekatan Z-Score Dan Fuzzy Dalam Pengujian Akurasi Peramalan Curah Hujan. SINTECH (Science Inf. Technol. J., 3(2), 149–156. https://doi.org/10.31598/sintechjournal.v3i2.567
Nurlaela, D. (2020). Penerapan Adaboost Untuk Meningkatkan Akurasi Naive Bayes Pada Prediksi Pendapatan Penjualan Film. INTI Nusa Mandiri, 14(2), 181–188. https://doi.org/10.33480/inti.v14i2.1220
Purnajiwa Arimbawa, I. G. A., & Sanjaya ER, N. A. (2020). Penerapan Metode Adaboost Untuk Multi-Label Classification Pada Dokumen Teks. JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), 9(1), 127. https://doi.org/10.24843/jlk.2020.v09.i01.p13
Zhang, Q., & Kong, X. (2020). Design of Automatic Lung Nodule Detection System Based on Multi-Scene Deep Learning Framework. IEEE Access, 8, 90380–90389. https://doi.org/10.1109/ACCESS.2020.2993872
Argina, A. M. (2020). Penerapan Metode Klasifikasi K-Nearest Neighbor pada Dataset Penderita Penyakit Diabetes. Indonesian Journal of Data and Science, 1(2), 29–33.
Rokani, V. (2023). Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques. Mathematics, 11(22), 4693. https://doi.org/10.3390/math11224693