Temperature classification in high voltage electrical equipment using Support Vector Machine (SVM)

Giovanni Dimas Prenata, S.T., M.T.; Ryan Agus Setiyawan; Izzah Aula Wardah

doi:10.24036/voteteknika.v13i2.133644

Temperature classification in high voltage electrical equipment using Support Vector Machine (SVM)

Giovanni Dimas Prenata, S.T., M.T.⁽¹⁾, Ryan Agus Setiyawan⁽²⁾, Izzah Aula Wardah⁽³⁾,
(1) Universitas 17 Agustus 1945 Surabaya

Indonesia
(2) Universitas 17 Agustus 1945 Surabaya

Indonesia
(3) Universitas 17 Agustus 1945 Surabaya

Indonesia

Corresponding Author

DOI : https://doi.org/10.24036/voteteknika.v13i2.133644

Full Text:

Language : id

Abstract

The implementation of artificial intelligence to determine the condition of a transformer based on temperature is something new. This study is an initial study to facilitate the process of monitoring or supervising transformers based on temperature. By processing thermal images obtained from the AMG8833 thermal sensor, an analysis of the temperature conditions of the transformer and its surrounding environment can be carried out. The thermal images obtained provide an overview or represent the distribution of temperature around the object observed by the AMG8833 thermal sensor. Furthermore, the thermal image is processed to obtain the percentage of white and non-white colors. The use of the SVM method aims to assist the justification process based on the percentage of white (high temperature) and non-white (low temperature). By using 10 training data that have obtained the percentage values of white and non-white colors, it produces 90% accuracy in recognizing the conditions of training data in high/low temperature conditions which can be interpreted as dangerous or safe. The hyperplane line equation obtained from the SVM method is used to distinguish between dangerous or safe categories. This line is a dividing line on the training data to categorize between high temperature data and low temperature data. Furthermore, the line is used to test 2 test data in determining dangerous or safe conditions. The results obtained 100% accuracy in determining dangerous or safe conditions on the test data. The SVM method successfully classifies the test data well even though the accuracy is not 100% when classifying training data.

References

J. Vocational, T. Elektronika, A. Kehandalan, F. Modes, and E. Analysis, “VoteTEKNIKA,” vol. 12, no. 1, 2024.

G. D. Prenata, “Klasifikasi Keandalan Sistim Distrbusi Tenaga Listrik Di Pt. Pln (Persero) Up3 Surabaya Selatan Menggunakan Metode K-Nearest Neighbor (Knn),” J. Inform. dan Tek. Elektro Terap., vol. 11, no. 3s1, 2023, doi: 10.23960/jitet.v11i3s1.3397.

G. D. Prenata, “Klasifikasi Keandalan Sistim Distribusi Tenaga Listrik Di Pt. Pln (Persero) Up3 Surabaya Selatan Menggunakan Support Vector Machine (Svm),” J. Tek. Elektro, vol. 16, no. 2, pp. 62–70, 2023, doi: 10.9744/jte.16.2.62-70.

G. D. Prenata and N. A. Basyarach, “Prediksi Impedansi Rele menggunakan metode PSO sebagai validasi pengukuran rele,” pp. 181–186, 1945.

A. Dwi cahyo, S. Anardani, and Y. P. Yuda, “Prediksi Beban Daya Listrik (W) Menggunakan Metode Support Vector Machine (SVM) Pada Listrik Token Rumah Tangga,” J. Sains dan Teknol., vol. 4, no. 1, pp. 70–77, 2024, doi: 10.47233/jsit.v4i1.1543.

N. S. Syam et al., “Model Support Vector Machine untuk Prediksi pada Penggunaan Energi Listrik di Rumah Hemat Energi,” J. Inform., vol. 1, no. 2, pp. 56–59, 2022, [Online]. Available: https://jurnal.uniraya.ac.id/index.php/JI

H. A. Illias and W. Z. Liang, “Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation,” PLoS One, vol. 13, no. 1, pp. 1–15, 2018, doi: 10.1371/journal.pone.0191366.

R. Latuconsina, P. N. Ambon, C. Digital, I. Pendahuluan, P. Transformator, and T. Karakteristik, “Menentukan Kualitas Minyak Transformator Dengan Pengolahan Citra Digital,” Semin. Nas. IPTEK Terap., pp. 15–17, 2017.

A. Murdianto, A. Rabi, R. Dwi, J. Kartika, T. Elektro, and U. M. Malang, “Metode Pohon Keputusan Untuk Identifikasi Insulator Flashover Dengan Pengolahan Citra Digital,” vol. 14, no. 3, pp. 185–189, 2023, doi: 10.22441/jte.2023.v14i3.010.

T. Rahmawati, P. Studi, M. Informatika, and C. Digital, “Abstrak,” pp. 91–101.

A. Jamaah, “Pengaruh Harmonik Terhadap Peningkatan Panas Pada Transformator 400 kVA di Politeknik Negeri Semarang,” Jtet, vol. 1, no. 3, pp. 186–191, 2012.

J. O. Wuwung, “Pengaruh Pembebanan Terhadap Kenaikan Suhupada Belitantransformator Daya Jenisterendam Minyak,” Tekno, vol. 7, no. 52, pp. 29–39, 2010.

R. Berwick and V. Idiot, “An Idiot ’ s guide to Support vector machines ( SVMs ) Key Ideas Organization,” ReCALL, pp. 1–27, 1990.

K. Ramachandra, P. G. Vaidya, and K. G. Bhat, “An introduction to trigonometry. A new outlook.,” p. 36~p., 2008.

A. Ab, “An Introduction to Trigonometry and its Applications,” An Introd. to Trigonometry its Appl., 2014, doi: 10.2174/97816080595081140101.

B. a B. Persamaan and G. Lurus, “Bab 1: persamaan garis lurus,” vol. 4, no. C, pp. 4–5.

Giovanni Dimas Prenata, Aswanda Harja, and Izzah Aula Wardah, “TEMPERATURE CLASSIFICATION IN HIGH VOLTAGE ELECTRICAL EQUIPMENT USING K-NEAREST NEIGHBOR”, TESLA, vol. 27, no. 1, pp. 39–49, May 2025.