Prediksi Arus Hubung Singkat Menggunakan Model Regresi Struktural dan Evaluasi Dampaknya terhadap Rating Peralatan Gardu Induk 500 kV


  • Bryan Bima Laksana * Mail IT PLN, Jakarta, Indonesia
  • Tri Wahyudi Adi IT PLN, Jakarta, Indonesia
  • (*) Corresponding Author
Keywords: Short-Circuit Current; 500 kV Transmission System; Prediction; Structural Modeling; Equipment Rating

Abstract

The development of power systems and the strengthening of transmission networks can increase short-circuit current levels, potentially reducing equipment safety margins when fault currents approach or exceed the rated breaking capacity. This study aims to identify the factors influencing short-circuit current variations, analyze the relationships among the relevant variables, predict short-circuit current development for the 2026–2030 period, and evaluate the adequacy of 500 kV substation equipment ratings. The proposed solution is an integrated approach that combines data-driven short-circuit current prediction with technical evaluation of equipment ratings, allowing potential increases in fault current levels to be identified before they exceed the interruption capability of installed equipment. The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) using historical power system data from 2018–2025. The analytical procedure includes the development of the Generation, Network Configuration, System, and Short Circuit constructs, evaluation of the measurement model using outer loading, Composite Reliability, Average Variance Extracted (AVE), and discriminant validity, followed by structural model evaluation using path coefficients, bootstrapping, t-statistics, and p-values. The validated model is subsequently used to generate latent variable scores and project short-circuit current development through 2030. The results indicate that the System variable has a positive and statistically significant effect on Short Circuit, with a path coefficient of 0.173, t-statistic of 2.156, and p-value of 0.016, whereas the direct effects of Generation and Network Configuration on Short Circuit are not statistically significant. The projection results indicate an increasing trend in short-circuit current at several observation points. Further equipment assessment shows that 19 extra-high-voltage substations have predicted short-circuit current levels exceeding the breaking capacity of their installed equipment, indicating the need for further attention in equipment reinforcement and system reliability planning.

Downloads

Download data is not yet available.

References

Alashqar, M., Yang, C., Xue, Y., Liu, Z., Zheng, W., & Zhang, X.-P. (2023). Enhancing transient stability of power systems using a resistive superconducting fault current limiter. Frontiers in Energy Research, 10. https://doi.org/10.3389/fenrg.2022.1106836

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. https://doi.org/10.7717/peerj-cs.623

Gomes Guerreiro, G. M., Sharma, R., Martin, F., Ghimire, P., & Yang, G. (2023). Concerning Short-Circuit Current Contribution Challenges of Large-Scale Full-Converter Based Wind Power Plants. IEEE Access, 11, 64141–64159. https://doi.org/10.1109/ACCESS.2023.3288610

Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of Reflective Measurement Models (pp. 75–90). https://doi.org/10.1007/978-3-030-80519-7_4

He, S., Zhao, S., Wang, S., Huang, J., Zhang, C., & Wang, H. (2024). A fast short circuit capacity calculation model based on regression neural network in complex grid environment. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-3325

Kozarek, Ł., Cichecki, H., Bogacki, M., Tyryk, M., Szulborski, M., Łapczyński, S., Kolimas, Ł., Rasolomampionona, D., Lange, A., Berowski, P., Sul, P., & Owsiński, M. (2023). Impact of the Short-Circuit Current Value on the Operation of Overhead Connections in High-Voltage Power Stations. Energies, 16(8), 3462. https://doi.org/10.3390/en16083462

Mbitu, E. T., Jamlaay, M., & Sahusilawane, F. R. (2025). Short-Circuit Analysis of the Saparua Distribution Feeder Network. International Journal of Science, Technology & Management, 6(4), 608–615. https://doi.org/10.46729/ijstm.v6i4.1337

Mohammadi, S., Mahmoudi, A., Kahourzade, S., Yazdani, A., & Shafiullah, G. (2022). Decaying DC Offset Current Mitigation in Phasor Estimation Applications: A Review. Energies, 15(14), 5260. https://doi.org/10.3390/en15145260

Murugan, R., & Raju, R. (2021). Evaluation of in-service power transformer health condition for Inspection, Repair, and Replacement (IRR) maintenance planning in electric utilities. International Journal of System Assurance Engineering and Management, 12(2), 318–336. https://doi.org/10.1007/s13198-021-01083-1

Nur, A. D. N., T Mangesa, R., Imran, A., Firdaus, F., & Zulhajji, Z. (2023). Short Circuit Fault Analysis In The Electric Power Distribution System At Pt Pln (Persero) UP3 South Makassar Ulp Kalebajeng Using Etap. Journal of Electrical Engineering and Informatics, 1(1), 35–44. https://doi.org/10.59562/jeeni.v1i1.420

Nur Annisa Dewi Nayla, T Mangesa, R., Imran, A., Firdaus, F., & Zulhajji, Z. (2023). Short Circuit Fault Analysis In The Electric Power Distribution System At Pt Pln (Persero) UP3 South Makassar Ulp Kalebajeng Using Etap. Journal of Electrical Engineering and Informatics, 1(1), 35–44. https://doi.org/10.59562/jeeni.v1i1.420

Okumus, H., Nuroglu, F. M., & McLoone, S. (2025a). Ensemble learning for short circuit fault location estimation in distribution networks. Engineering Applications of Artificial Intelligence, 159, 111640. https://doi.org/10.1016/j.engappai.2025.111640

Okumus, H., Nuroglu, F. M., & McLoone, S. (2025b). Ensemble learning for short circuit fault location estimation in distribution networks. Engineering Applications of Artificial Intelligence, 159, 111640. https://doi.org/10.1016/j.engappai.2025.111640

Plevris, V., Solorzano, G., Bakas, N., & Ben Seghier, M. (2022). Investigation of performance metrics in regression analysis and machine learning-based prediction models. 8th European Congress on Computational Methods in Applied Sciences and Engineering. https://doi.org/10.23967/eccomas.2022.155

Putra, P. D., & Marbun, M. P. (2022). Adaptive Defense Scheme Implementation in Muarakarang Subsystem to Prevent Island Operation Failure. 2022 11th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), 88–93. https://doi.org/10.1109/EECCIS54468.2022.9902921

Qays, M. O., Ahmad, I., Habibi, D., Aziz, A., & Mahmoud, T. (2023). System strength shortfall challenges for renewable energy-based power systems: A review. Renewable and Sustainable Energy Reviews, 183, 113447. https://doi.org/10.1016/j.rser.2023.113447

Qian, W., Zhang, R., Zou, Y., Zhou, N., Wang, Q., & Yang, T. (2024). Quantifying the Inverter-Interfaced Renewable Energy Critical Integration Capacity of a Power Grid Based on Short-Circuit Current Over-Limits Probability. Electronics, 13(8), 1486. https://doi.org/10.3390/electronics13081486

Ruikai, Y., Huifang, W., Bashir, T., & Yixiang, Z. (2024). Fast and accurate method for short-circuit current calculation in distribution network with IIDGs. International Journal of Electrical Power & Energy Systems, 155, 109622. https://doi.org/10.1016/j.ijepes.2023.109622

Silos-Sanchez, A., Villafafila-Robles, R., & Lloret-Gallego, P. (2020). Novel fault location algorithm for meshed distribution networks with DERs. Electric Power Systems Research, 181, 106182. https://doi.org/10.1016/j.epsr.2019.106182

Silva, J. M. C. S., & Winkelmann, R. (2026). Misspecified Exponential Regressions: Estimation, Interpretation, and Average Marginal Effects. Review of Economics and Statistics, 1–8. https://doi.org/10.1162/rest_a_01443

Song, X., Deng, L., Wang, H., Zhang, Y., He, Y., & Cao, W. (2024). Deep learning-based time series forecasting. Artificial Intelligence Review, 58(1), 23. https://doi.org/10.1007/s10462-024-10989-8

Stoffels, M., Torre, D. M., Sturgis, P., Koster, A. S., Westein, M. P. D., & Kusurkar, R. A. (2023). Steps and decisions involved when conducting structural equation modeling (SEM) analysis. Medical Teacher, 45(12), 1343–1345. https://doi.org/10.1080/0142159X.2023.2263233

Wang, M., Wei, X., & Zhao, Z. (2022). Short-Circuit Fault Current Parameter Prediction Method Based on Ultra-Short-Time Data Window. Energies, 15(23), 8861. https://doi.org/10.3390/en15238861

Yu, R., Jahdi, S., Mellor, P., Liu, L., Yang, J., Shen, C., Alatise, O., & Ortiz-Gonzalez, J. (2023). Degradation Analysis of Planar, Symmetrical and Asymmetrical Trench SiC MOSFETs Under Repetitive Short Circuit Impulses. IEEE Transactions on Power Electronics, 38(9), 10933–10946. https://doi.org/10.1109/TPEL.2023.3290387


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Prediksi Arus Hubung Singkat Menggunakan Model Regresi Struktural dan Evaluasi Dampaknya terhadap Rating Peralatan Gardu Induk 500 kV

Dimensions Badge
Article History
Published: 2026-08-31
Abstract View: 0 times
PDF Download: 0 times
Issue
Section
Articles