Development of a method for reliability assessment of distribution power networks up to 110 kV

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.322920

Keywords:

distribution networks, power reliability, Markov processes, redundancy assessment, failure rate

Abstract

The study focuses on the reliability assessment of distribution power networks operating at voltages up to 110 kV, addressing the challenges of increasing loads, aging infrastructure, and the integration of renewable energy sources. A novel method and model for reliability assessment are proposed, incorporating failure rates, recovery times, and topological characteristics of networks. The research identifies critical factors influencing network reliability, including the level of redundancy, operational conditions, and climatic impacts. Notable findings show that network points with multiple feeder connections demonstrate the highest reliability, exceeding 99.99 %, while those with single-transformer configurations are the most vulnerable to failures. The average failure rate for overhead lines is calculated at 1.29 failures per 100 km annually, with recovery times reaching up to 40 hours for critical nodes.

The results are explained by the interplay of structural and operational factors, where redundancy significantly enhances reliability, and outdated equipment increases vulnerability. The study’s distinguishing feature lies in its use of Markov processes to dynamically model failures and recoveries, offering a comprehensive framework compared to traditional static methods. The practical applications of the results include improving network design through enhanced redundancy, optimizing maintenance strategies for critical elements, and supporting the integration of Smart Grid technologies. These findings contribute to the development of more resilient and efficient power distribution networks, adaptable to modern operational demands

Author Biographies

Fariza Abilzhanova, Abylkas Saginov Karaganda Technical University

Master of Engineering Sciences, PhD Student

Department of Energy Systems

Felix Bulatbaev, Abylkas Saginov Karaganda Technical University

Candidate of Technical Sciences, Associate Professor

Department of Energy Systems

Aizada Kuanyshtaeva, Abylkas Saginov Karaganda Technical University

PhD Student

Department of Automation of Production Processes

References

  1. Singh, S., Singh, S. (2024). Advancements and Challenges in Integrating Renewable Energy Sources Into Distribution Grid Systems: A Comprehensive Review. Journal of Energy Resources Technology, 146 (9). https://doi.org/10.1115/1.4065503
  2. Kaverin, V., Abisheva, D., Em, G., Kalinin, A., Yugay, V. (2022). Studying Partial Discharge Currents of High Voltage Power Line Suspension Insulators. International Journal on Energy Conversion (IRECON), 10 (3), 88. https://doi.org/10.15866/irecon.v10i3.21769
  3. Kayumov, D., Bulatbaev, F., Kayumova, I., Breido, J., Bulatbayeva, Y. (2023). An engineering approach for the qualitative assessment of the luminous flux of led lamps. International Journal of Energy for a Clean Environment, 24 (1), 31–43. https://doi.org/10.1615/interjenercleanenv.2022043776
  4. Wang, Y., Zheng, Q., Guo, M., Xiao, H., Si, C., Chen, W. (2022). Reliability Improvement of Distribution Network with Distributed Generation Sources and Diversified Loads. LOW VOLTAGE APPARATUS, 2, 63–67. Available at: http://www.eaes-seari.com/Jwk_dqynxgljs/EN/abstract/abstract480.shtml
  5. Soltan, S., Mazauric, D., Zussman, G. (2017). Analysis of Failures in Power Grids. IEEE Transactions on Control of Network Systems, 4 (2), 288–300. https://doi.org/10.1109/tcns.2015.2498464
  6. Wu, Y., Fan, T., Huang, T. (2020). Electric Power Distribution System Reliability Evaluation Considering the Impact of Weather on Component Failure and Pre-Arranged Maintenance. IEEE Access, 8, 87800–87809. https://doi.org/10.1109/access.2020.2993087
  7. Tatkeyeva, G., Bauyrzhanuly, M., Gaukhar, A., Assainov, G., Khabdullina, G., Tangirbergen, A. et al. (2024). Development of the logical system for forecasting wind characteristics in the urban conditions. EUREKA: Physics and Engineering, 2, 55–69. https://doi.org/10.21303/2461-4262.2024.003305
  8. Li, S., She, Y., Shi, K., Chen, Z. (2022). A Method for Evaluating Reliability and Failure Rate of DC Circuit Breakers. 2022 Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT), 578–581. https://doi.org/10.1109/gcrait55928.2022.00126
  9. Cao, H., Song, Y., Wang, S., Dai, F., Liu, J., Cheng, Q. (2024). Failure Mode Analysis and Identification Method Based on the External Characteristics of DC Circuit Breaker. 2024 China International Conference on Electricity Distribution (CICED), 01–05. https://doi.org/10.1109/ciced63421.2024.10754216
  10. Pande, P., Hussain, K., Pravallika, B., Al Ansari, M. S., Tharsanee, R. M., Acharya, S. (2024). Predictive Maintenance of Power Transformers in Distribution Network with Energy Management Using Deep Learning. 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 581–586. https://doi.org/10.1109/icicv62344.2024.00098
  11. Biradar, V., Kakeri, D., Agasti, A. (2024). Machine Learning based Predictive Maintenance in Distribution Transformers. 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA), 1–5. https://doi.org/10.1109/iccubea61740.2024.10774993
  12. Reddy Shabad, P. K., Alrashide, A., Mohammed, O. (2021). Anomaly Detection in Smart Grids using Machine Learning. IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 1–8. https://doi.org/10.1109/iecon48115.2021.9589851
  13. Papaspiliotopoulos, V. A., Korres, G. N., Hatziargyriou, N. D. (2015). Protection coordination in modern distribution grids integrating optimization techniques with adaptive relay setting. 2015 IEEE Eindhoven PowerTech, 1–6. https://doi.org/10.1109/ptc.2015.7232558
  14. Rêma, G. S., Bonatto, B. D., de Lima, A. C. S., de Carvalho, A. T. (2024). Emerging Trends in Power Transformer Maintenance and Diagnostics: A Scoping Review of Asset Management Methodologies, Condition Assessment Techniques, and Oil Analysis. IEEE Access, 12, 111451–111467. https://doi.org/10.1109/access.2024.3441523
  15. Carletti, E., Amadei, F., Franzone, G., Rizzati, J., Cocchi, L., Bolognesi, M., Moschella, P. (2023). The reliability of the electrical distribution system using the Markov Modeling methodology. IET Conference Proceedings, 2023 (6), 1115–1119. https://doi.org/10.1049/icp.2023.0655
  16. Borges, C. L. T., Cantarino, E. (2011). Microgrids Reliability Evaluation with Renewable Distributed Generation and Storage Systems. IFAC Proceedings Volumes, 44 (1), 11695–11700. https://doi.org/10.3182/20110828-6-it-1002.01090
  17. Song, H., Zhang, B., Wang, M., Xiao, Y., Zhang, L., Zhong, H. (2022). A Fast Phase Optimization Approach of Distributed Scatterer for Multitemporal SAR Data Based on Gauss–Seidel Method. IEEE Geoscience and Remote Sensing Letters, 19, 1–5. https://doi.org/10.1109/lgrs.2021.3077493
  18. Hu, W. J. (2021). Momentum Method for Improving the Convergence of Newton-Raphson Method for Nonlinear Circuit Transient Simulations. 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE), 296–303. https://doi.org/10.1109/csaiee54046.2021.9543125
  19. Wei, J., Cai, H., Jiang, T., Westermann, D. (2021). Research on Power System Network Equivalent with Different Methods. PESS 2021; Power and Energy Student Summit.
  20. Gupta, A. P., Mohapatra, A., Singh, S. N. (2018). Power System Network Equivalents: Key Issues and Challenges. TENCON 2018 - 2018 IEEE Region 10 Conference, 2291–2296. https://doi.org/10.1109/tencon.2018.8650397
  21. Aghili, J., Franck, E., Hild, R., Michel-Dansac, V., Vigon, V. (2025). Accelerating the convergence of Newton’s method for nonlinear elliptic PDEs using Fourier neural operators. Communications in Nonlinear Science and Numerical Simulation, 140, 108434. https://doi.org/10.1016/j.cnsns.2024.108434
  22. Ramli, S. P., Usama, M., Mokhlis, H., Wong, W. R., Hussain, M. H., Muhammad, M. A., Mansor, N. N. (2021). Optimal directional overcurrent relay coordination based on computational intelligence technique: a review. Turkish Journal of Electrical Engineering and Computer Sciences, 29 (3), 1284–1307. https://doi.org/10.3906/elk-2012-98
Development of a method for reliability assessment of distribution power networks up to 110 kV

Downloads

Published

2025-02-27

How to Cite

Abilzhanova, F., Bulatbaev, F., & Kuanyshtaeva, A. (2025). Development of a method for reliability assessment of distribution power networks up to 110 kV. Eastern-European Journal of Enterprise Technologies, 1(8 (133), 15–23. https://doi.org/10.15587/1729-4061.2025.322920

Issue

Section

Energy-saving technologies and equipment