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All Journal Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains TESLA: Jurnal Teknik Elektro JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) E-JOINT (Electronica and Electrical Journal Of Innovation Technology) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Renewable Energy (IJEERE) BATOBO: Jurnal Pengabdian Kepada Masyarakat Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Electrical Today
Boy Ihsan
Universitas Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Other

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Intelligent Fault Detection In a 25 MVA Transformer Using ANFIS Zakri, Azriyenni; Firdaus, Hari; Ihsan, Boy; Usman, Jafaru; Sunanda, Wahri
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 13 No 1 (2026): Jurnal Ecotipe, April 2026
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/39mt0605

Abstract

This study aims to develop an intelligent fault prediction model for a 25 MVA power transformer using the Adaptive Neuro-Fuzzy Inference System (ANFIS), to improve classification accuracy and ensure selective, reliable protection decisions in power systems. The research is grounded in the limitations of traditional differential relay protection, which struggles to distinguish between internal and external faults during transient conditions. ANFIS combines fuzzy logic’s ability to handle uncertainty with the adaptive learning of neural networks, making it a suitable tool for non-linear fault data analysis. A simulation was carried out in MATLAB/Simulink. Current signals from CTs on both primary and secondary sides served as input features. The model was trained using 270 data samples and tested with 30 samples. Two membership functions generalized bell-shaped (Gbell) and triangular (Tri) were evaluated. RMSE was used as the performance metric. The ANFIS model with Gbell MF yielded a lower RMSE (0.0116) compared to Tri MF (0.0445), indicating better prediction accuracy and stability. The system consistently identified internal faults (output 1), and external faults (output 0) based on a 0.5 decision threshold. The findings validate the potential of ANFIS for integration into digital relay systems, enhancing real-time transformer protection through adaptive learning.
Co-Authors Ali, Nurhalim Dani Antonius Rajagukguk, Antonius Apriyal, Derry Arif, Muhammad Fahmi Azhari Zakri, Azriyenni Deny Hamdani Deny Hamdani Dian Yayan Sukma, Dian Yayan Dicky Pangindra Dwi Sahidin Firdaus Firdaus, Hari Imam Suri Tauladan Jikro Pikri Kurniawan, Reyhan Exsa Marpaung, Mario Johannes Nanang Hariyanto Nurhalim Rosma, Iswadi Hasyim Sahar, Muzni Sultana, Beenish Syamsir, Hendri Novia Tri Desmana Rachmildha, Tri Desmana Usman, Jafaru Wahri Sunanda Zakri, Azriyenni