PREDICTIVE MAINTENANCE ANALYSIS OF LIGHTNING RODS BASED ON INFRARED THERMOGRAPHY AT 150 KV GARUT SUBSTATION

anisa sabilatul

doi:10.23960/jitet.v14i1.8344

Authors

anisa sabilatul universitas siliwangi

DOI:

https://doi.org/10.23960/jitet.v14i1.8344

Abstract Views: 33 File Views: 18

Keywords:

Lightning arrester, predictive maintenance, infrared thermovision, emissivity

Abstract

A vital component in a substation is a lightning arrester that plays a role in protecting components from overvoltage due to lightning strikes and switching maneuvers. This study aims to analyze the application of a predictive maintenance method based on infrared thermography to detect early arrester degradation in the 150 KV Garut Substation. The research method is carried out by measuring the temperature on the connection clamp and conductor using a thermovision camera, calculating ∆T (delta-T) according to PLN standards, determining the emissivity value, and validating the method with accuracy and precision calculations. The measurement results from 68 connection points show that all points are still in the good condition category with ∆T values below the danger threshold. From 28 emissivity measurement samples, an average of 0.9990 was obtained with a coefficient of variation of 0.19% and an accuracy of 99.91%, indicating that the measurement method is suitable for use as an equipment condition monitoring system. These findings prove that infrared thermography is effective as an early detection tool for thermal anomalies and supports increasing the reliability of the electricity transmission system

Downloads

Download data is not yet available.

References

I. Ullah et al., “Predictive maintenance of power substation equipment by infrared thermography using a machine-learning approach,” Energies, vol. 10, no. 12, 2017, doi: 10.3390/en10121987.

G. K. Balakrishnan et al., “A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations,” Energies, vol. 15, no. 16, 2022, doi: 10.3390/en15166000.

T. G. Zacarias and W. C. Sant’Ana, “A Bibliometric and Comprehensive Review on Condition Monitoring of Metal Oxide Surge Arresters,” Sensors, vol. 24, no. 1, pp. 1–26, 2024, doi: 10.3390/s24010235.

M. Molęda, B. Małysiak-Mrozek, W. Ding, V. Sunderam, and D. Mrozek, “From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry,” Sensors, vol. 23, no. 13, 2023, doi: 10.3390/s23135970.

C. Xia et al., “Infrared thermography-based diagnostics on power equipment: State-of-the-art,” High Volt., vol. 6, no. 3, pp. 387–407, 2021, doi: 10.1049/hve2.12023.

P. J. Zarco-Periñán, F. J. Zarco-Soto, I. M. Zarco-Soto, and J. L. Martínez-Ramos, “Conducting Thermographic Inspections in Electrical Substations: A Survey,” Appl. Sci., vol. 12, no. 20, 2022, doi: 10.3390/app122010381.

S. J. Huang and C. H. Hsieh, “A method to enhance the predictive maintenance of ZnO arresters in energy systems,” Int. J. Electr. Power Energy Syst., vol. 62, pp. 183–188, 2014, doi: 10.1016/j.ijepes.2014.04.041.

C. C. Hung, M. H. Wang, S. Der Lu, and C. C. Kuo, “Detection of failures in HV surge arrester using chaos pattern with deep learning neural network,” IET Sci. Meas. Technol., vol. 18, no. 9, pp. 534–546, 2024, doi: 10.1049/smt2.12214.

S. Han, F. Yang, H. Jiang, G. Yang, D. Wang, and N. Zhang, “Statistical analysis of infrared thermogram for CNN-based electrical equipment identification methods,” Appl. Artif. Intell., vol. 36, no. 1, 2022, doi: 10.1080/08839514.2021.2004348.

T. G. Zacarias et al., “Detection of Failures in Metal Oxide Surge Arresters Using Frequency Response Analysis,” Sensors, vol. 23, no. 12, pp. 1–18, 2023, doi: 10.3390/s23125633.

R. Hardiansyah and T. Wrahatnolo, “Long-Term Prediction of Leakage Current in Lightning Arrester Using Linear Programming Method,” Ina. Indones. J. Electr. Eletronics Eng., vol. 8, no. 1, pp. 17–24, 2024, doi: 10.26740/inajeee.v8n1.p17-24.

E. K. Ukiwe, S. A. Adeshina, T. Jacob, and B. B. Adetokun, “Deep learning model for detection of hotspots using infrared thermographic images of electrical installations,” J. Electr. Syst. Inf. Technol., vol. 11, no. 1, 2024, doi: 10.1186/s43067-024-00148-y.

S. A. Hosseini, M. Mirzaie, and T. Barforoshi, “Reliability Analysis of Surge Arrester Location Effect in High voltage substations,” TELKOMNIKA Indones. J. Electr. Eng., vol. 12, no. 8, pp. 5814–5826, 2014, doi: 10.11591/telkomnika.v12i8.6033.

R. R. Putra, “Thermovisi Dalam Melihat Hot Point Pada Gardu Induk 150 kV Palur,” Fak. Tek. Elektro, Univ. Muhammadiyah Surakarta, pp. 1–19, 2018.

A. R. Dwiputra and U. Latifa, “Pengujian Arus Bocor Pada Lightning Arrester Menggunakan Alat Leakage Current Monitor (Lcm) Di Gi Xyz,” J. Inform. dan Tek. Elektro Terap., vol. 13, no. 3, 2025, doi: 10.23960/jitet.v13i3.6623.

S. Suhendar, “Perencanaan Pembangkit Listrik.Pdf,” 2022