Fault Location Method for Distribution Network Based on Support Vector Machine

DOI: https://doi.org/10.62517/jes.202502420

Author(s)

Huanhuan Xing*, Ruipeng Wang, Huixing Zhang, Yingyang Cao, Xiaonan Zhang, Yanan Zhang

Affiliation(s)

State Grid Jia Xian Electric Power Supply Company of Henan Electric Power Company, Pingdingshan, China *Corresponding Author

Abstract

Fault location in distribution networks is a critical task for ensuring reliable power delivery and minimizing downtime. This paper proposes a fault location method for distribution networks based on Support Vector Machine (SVM) algorithms. The method utilizes voltage and current measurements from various network nodes to identify fault characteristics. A set of key features, including voltage sag, current surge, and their time-domain characteristics, are extracted from the data. The SVM classifier is trained on these features to differentiate between fault and normal conditions and accurately locate the fault. Experimental results using simulated data demonstrate that the proposed method significantly improves fault detection accuracy and reduces the fault location time compared to traditional techniques. The effectiveness of this method is further validated under different fault scenarios and network configurations, showing its robustness and potential for practical deployment in real-world distribution networks.

Keywords

Fault Location; Distribution Network; Support Vector Machine; Fault Detection; Voltage Sag; Current Surge

References

[1]Fadhil J, Zeebaree S R M. Blockchain for distributed systems security in cloud computing: A review of applications and challenges. The Indonesian Journal of Computer Science, 2024, 13(2) 134655-134689. [2]Yan Y, Liu Y, Fang J, et al. Application status and development trends for intelligent perception of distribution network. High Voltage, 2021, 6(6): 938-954. [3]Alonge E O, Eyo-Udo N L, Ubanadu B C, et al. Real-time data analytics for enhancing supply chain efficiency. Journal of Supply Chain Management and Analytics, 2023, 10(1): 49-60. [4]Ge L, Li Y, Li Y, et al. Smart distribution network situation awareness for high-quality operation and maintenance: a brief review. Energies, 2022, 15(3): 828-836. [5]Zerui C, Yanhong X, Jiaxiang O, et al. Dynamic Monitoring and Real-Time Data Acquisition Technology for Wire Status Based on Artificial Intelligence Timing Analysis. International Journal of High Speed Electronics and Systems, 2024: 2540173. [6]Medattil Ibrahim A H, Sharma M, Subramaniam Rajkumar V. Integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks using μPMUs. Energies, 2023, 16(11): 4262. [7]Jiang L, Ming Y, Yangyang C, et al. A Novel Voltage Injection Based Offline Parameters Identification for Current Controller Auto Tuning in SPMSM Drives. Energies. 2020; 13(11): 3010. [8]Shirko O, Askar S. A novel security survival model for quantum key distribution networks enabled by software-defined networking. IEEE Access, 2023, 11: 21641-21654. [9]Linh N T. A Novel Combination of Genetic Algorithm, Particle Swarm Optimization, and Teaching-Learning-Based Optimization for Distribution Network Reconfiguration in Case of Faults. Engineering, Technology & Applied Science Research, 2024, 14(1): 12959-12965. [10]Sharma S, Saxena N K. Capability identification for new charging station into distribution system with electric, economic and traffic considerations. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 2024, 9: 100669. [11]Huang B, Wang J. Applications of physics-informed neural networks in power systems-a review. IEEE Transactions on Power Systems, 2022, 38(1): 572-588. [12]Liu Z, Zhou Y, Xu Y, et al. Simplenet: A simple network for image anomaly detection and localization//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 20402-20411. [13]De La Cruz J, Gómez-Luna E, Ali M, et al. Fault location for distribution smart grids: Literature overview, challenges, solutions, and future trends. Energies, 2023, 16(5): 2280. [14]Nsaif Y M, Lipu M S H, Ayob A, et al. Fault detection and protection schemes for distributed generation integrated to distribution network: Challenges and suggestions. IEEE access, 2021, 9: 142693-142717. [15]Panahi H, Zamani R, Sanaye-Pasand M, et al. Advances in transmission network fault location in modern power systems: Review, outlook and future works. IEEE Access, 2021, 9: 158599-158615. [16]Liu K, Kang T, Ye X. A fault location method of distribution network based on XGBoost and SVM algorithm. IET Cyber‐Physical Systems: Theory & Applications, 2021, 6(4): 254-264. [17]Dashtdar M, Dashtdar M. Fault location in the distribution network based on scattered measurement in the network. Energy Systems, 2022, 13(2): 539-562.