Machine Learning Model Implementation for Predicting Essential Transmission Line Outage via Reliability Index Predicting Essential Transmission Line Outage via Reliability Index

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Published: Oct 26, 2025
DOI: https://doi.org/10.37936/ecti-eec.2525233.258445
Keywords:
Power System Reliability Line outage Machine Learning Gradient Boosting

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Sujit Pani
NIT Nagaland
Dipu Sarkar
-National Institute of Technology Nagaland
Ch Sekhar Gujjarlapudi
NIT Nagaland

Abstract

Currently, electric power transmission systems are operating at maximum loading capacities, frequently operating near their stability thresholds with minimal security margins. In such scenarios, monitoring of important lines for a particular loading level has become a crucial factor in ensuring the efficient operation of contemporary power systems. Thus, precisely assessing reliability for different line outage conditions is an important task for a power engineer. This paper concentrates on presenting the most recent machine learning (ML) techniques, like gradient boosting (GB), K- Nearest Neighbour (KNN), and linear regression (LR), utilized to determine the reliability index for different
outage conditions. Out of the 3 ML techniques, GB demonstrated the best performance with an R_2 score of 0.9309, a mean absolute error (MAE) of 0.2503, a mean squared error (MSE) of 0.1497, and a root mean squared error (RMSE) of 0.3869.

Article Details

How to Cite
Pani, S. ., Sarkar, D., & Gujjarlapudi, C. S. (2025). Machine Learning Model Implementation for Predicting Essential Transmission Line Outage via Reliability Index : Predicting Essential Transmission Line Outage via Reliability Index . ECTI Transactions on Electrical Engineering, Electronics, and Communications, 23(3). https://doi.org/10.37936/ecti-eec.2525233.258445
Issue
Vol. 23 No. 3 (2025): Regular Issue (October 2025)
Section
Electrical Power Systems
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References

Ballireddy, T. R. R., & Ray, P. (2024). Reliability Assessment of a Power System Incorporating Wind and Solar Farms. Journal of The Institution of Engineers (India): Series B, 105(1), 165-174.

Hu, B., Sun, Y., Huang, W., Shao, C., Niu, T., Cheng, X., & Xie, K. (2023). Power system reliability assessment with quantification of demand response uncertainty based on advanced sigmoid cloud model. CSEE Journal of Power and Energy Systems.

Teixeira, T. P., & Borges, C. L. (2020). Operation strategies for coordinating battery energy storage with wind power generation and their effects on system reliability. Journal of Modern Power Systems and Clean Energy, 9(1), 190-198.

Li, Z., Wang, Z., Fu, Y., & Zhao, N. (2021). Energy supply reliability assessment of the integrated energy system considering complementary and optimal operation during failure. IET Generation, Transmission & Distribution, 15(13), 1897-1907.

Adinolfi, G., Ciavarella, R., Graditi, G., Ricca, A., & Valenti, M. (2021). A planning tool for reliability assessment of overhead distribution lines in hybrid AC/DC grids. Sustainability, 13(11), 6099.

Liu, K., Zhang, J., & Chen, F. (2020, June). Power Generation System Reliability Evaluation Base on Cross Entropy Method. In 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE) (pp. 1641-1645). IEEE.

Yang, W., Cao, M., Ge, P., Hu, B., Qu, G., Xie, K., ... & Li, Y. (2020). Risk-oriented renewable energy scenario clustering for power system reliability assessment and tracing. IEEE Access, 8, 183995-184003.

Prajapati, V. K., & Mahajan, V. (2021). Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources. Energy, 215, 119134.

Li, S., Ye, C., Ding, Y., Song, Y., & Bao, M. (2022). Reliability assessment of renewable power systems considering thermally-induced incidents of large-scale battery energy storage. IEEE Transactions on Power Systems, 38(4), 3924-3938.

Yu, D. C., Nguyen, T. C., &Haddawy, P. (1999). Bayesian network model for reliability assessment of power systems. IEEE transactions on power systems, 14(2), 426-432.

Oliveira, G. C., Cunha, S. H. F., & Pereira, M. V. F. (1987). A direct method for multi-area reliability evaluation. IEEE Transactions on Power Systems, 2(4), 934-940.

Allan, R. N., Billinton, R., & Abdel-Gawad, N. M. K. (1986). The IEEE reliability test system-extensions to and evaluation of the generating system. IEEE Transactions on Power Systems, 1(4), 1-7.

Hou, K., Tang, P., Liu, Z., & Dong, Z. (2023). An efficient reliability assessment approach for multiple energy systems based on shadow price. Energy Reports, 9, 829-836.

Urgun, D., & Singh, C. (2020, August). Composite system reliability analysis using deep learning enhanced by transfer learning. In 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) (pp. 1-6). IEEE.

Yarramsetty, C., Moger, T., & Jena, D. (2023, August). Composite Power System Reliability Evaluation Using Artificial Neural Networks. In 2023 International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM) (pp. 1-5). IEEE.

Patil, G. S., Patil, U. S., & Shinde, P. P. (2024, January). Enhancing Power Transformer Reliability through Machine Learning-Based Fault Prediction Using Dissolved Gas Analysis. In 2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T) (pp. 72-76). IEEE.

Breiman, L. (1998). Arcing classifier (with discussion and a rejoinder by the author). The annals of statistics, 26(3), 801-849.

Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.

Breiman, L. (1998). Arcing classifier (with discussion and a rejoinder by the author). The annals of statistics, 26(3), 801-849.

Zhang, B., Ren, J., Cheng, Y., Wang, B., & Wei, Z. (2019). Health data driven on continuous blood pressure prediction based on gradient boosting decision tree algorithm. Ieee Access, 7, 32423-32433.

Jiang, J., Wang, R., Wang, M., Gao, K., Nguyen, D. D., & Wei, G. W. (2020). Boosting tree-assisted multitask deep learning for small scientific datasets. Journal of chemical information and modeling, 60(3), 1235-1244.

Smola, A., & Vishwanathan, S. V. N. (2008). Introduction to machine learning. Cambridge University, UK, 32(34), 2008.

Moshkovitz, M., Dasgupta, S., Rashtchian, C., & Frost, N. (2020, November). Explainable k-means and k-medians clustering. In International conference on machine learning (pp. 7055-7065). PMLR.

Foote, K. D. (2022). The history of machine learning and its convergent trajectory towards AI. Machine Learning and the City: Applications in Architecture and Urban Design, 129-142.

Abhishek, B., & Tyagi, A. K. (2021, October). An Useful Survey on Supervised Machine Learning Algorithms: Comparisons and Classifications. In International Conference on Advances in Electrical and Computer Technologies (pp. 293-307). Singapore: Springer Nature Singapore.