An Application of Fuzzy Logic and ANFIS in Intelligent Fault Detection and Localization in Medium-Voltage Networks
Received: 12 June 2025 | Revised: 12 July 2025, 17 July 2025, and 20 July 2025 | Accepted: 23 July 2025 | Online: 6 October 2025
Corresponding author: Thanh Lam Le
Abstract
Accurate and timely fault diagnosis in medium-voltage networks is essential for enhancing the reliability and operational resilience of power systems. Traditional protection schemes, while fast, often struggle with accuracy in the presence of high fault resistance, dynamic load variations, and unsynchronized measurements. This paper presents a hybrid intelligent framework that integrates a fuzzy logic-based module for fault detection and classification with an Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault location estimation. The proposed method utilizes simple voltage and current indicators as input features and does not rely on GPS synchronization or extensive training datasets. A rule-based fuzzy inference system ensures interpretability and robustness, whereas ANFIS provides accurate fault distance estimation. The system is implemented in MATLAB/Simulink and validated under various fault scenarios. Simulation results demonstrate that the proposed approach can accurately detect, classify, and localize different types of faults, making it suitable for real-time protection in conventional substations and resource-constrained environments.
Keywords:
intelligent protection systems, fault detection, fault location estimation, ANFIS, fuzzy logicDownloads
References
A. Hameurlaine, H. Sayah, and L. Boukezzi, "Design of Protection Strategies and Performance Analysis of an HVDC Link During Multiple Disturbances," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8760–8764, June 2022.
B. P. Ganthia, S. K. Barik, and B. Nayak, "Shunt Connected FACTS Devices for LVRT Capability Enhancement in WECS," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5819–5823, June 2020.
N. N. Bon and T.-L. Le, "Application of JAYA algorithm for optimizing allocation and size of thyristor-controlled series compensator devices," Bulletin of Electrical Engineering and Informatics, vol. 14, no. 3, pp. 1647–1655, June 2025.
B. Gjorgiev and G. Sansavini, "Identifying and assessing power system vulnerabilities to transmission asset outages via cascading failure analysis," Reliability Engineering & System Safety, vol. 217, Jan. 2022, Art. no. 108085.
M. Usama et al., "A Comprehensive Review on Protection Strategies to Mitigate the Impact of Renewable Energy Sources on Interconnected Distribution Networks," IEEE Access, vol. 9, pp. 35740–35765, 2021.
S. Das, S. Santoso, A. Gaikwad, and M. Patel, "Impedance-based fault location in transmission networks: theory and application," IEEE Access, vol. 2, pp. 537–557, 2014.
K. Chen, C. Huang, and J. He, "Fault detection, classification and location for transmission lines and distribution systems: a review on the methods," High Voltage, vol. 1, no. 1, pp. 25–33, Apr. 2016.
R. Bhargav, C. P. Gupta, and B. R. Bhalja, "Unified Impedance-Based Relaying Scheme for the Protection of Hybrid AC/DC Microgrid," IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 913–927, Mar. 2022.
Y. Jiang, "Data-Driven Probabilistic Fault Location of Electric Power Distribution Systems Incorporating Data Uncertainties," IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 4522–4534, Sept. 2021.
P. Chiradeja and A. Ngaopitakkul, "Classification of Lightning and Faults in Transmission Line Systems Using Discrete Wavelet Transform," Mathematical Problems in Engineering, vol. 2018, no. 1, Oct. 2018, Art. no. 1847968.
C. Zhang, G. Song, T. Wang, and L. Yang, "Single-Ended Traveling Wave Fault Location Method in DC Transmission Line Based on Wave Front Information," IEEE Transactions on Power Delivery, vol. 34, no. 5, pp. 2028–2038, Oct. 2019.
B. Jalil, G. R. Leone, M. Martinelli, D. Moroni, M. A. Pascali, and A. Berton, "Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data," Sensors, vol. 19, no. 13, July 2019, Art. no. 3014.
Y. Q. Chen, O. Fink, and G. Sansavini, "Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction," IEEE Transactions on Industrial Electronics, vol. 65, no. 1, pp. 561–569, Jan. 2018.
H. Panahi, R. Zamani, M. Sanaye-Pasand, and H. Mehrjerdi, "Advances in Transmission Network Fault Location in Modern Power Systems: Review, Outlook and Future Works," IEEE Access, vol. 9, pp. 158599–158615, 2021.
M. Kiruthika and S. Bindu, "Classification of Electrical Power System Conditions with Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5759–5768, June 2020.
N. Q. Vinh and T.-L. Le, "Self-learning model-based control for sensorless induction motor drives," Electrical Engineering, June 2025.
J. Han et al., "A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection," Applied Sciences, vol. 9, no. 10, May 2019, Art. no. 2009.
J. Li, P. Li, A. Miao, Y. Chen, M. Cao, and X. Shen, "Online prediction method of icing of overhead power lines based on support vector regression," International Transactions on Electrical Energy Systems, vol. 28, no. 3, Mar. 2018, Art. no. e2500.
V. Veerasamy, N. I. Abdul Wahab, R. Ramachandran, M. Mansoor, M. Thirumeni, and M. Lutfi Othman, "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, vol. 11, no. 12, Dec. 2018, Art. no. 3330.
V.-Q. Nguyen, M.-T. Nguyen, T.-L. Le, and N.-B. Nguyen, "Current Control for Multi-Level Inverters Based on Fuzzy Logic Sliding Mode Control Approach," in 2023 International Conference on System Science and Engineering, Ho Chi Minh, Vietnam, 2023, pp. 444–449.
S. R. Saufi, Z. A. B. Ahmad, M. S. Leong, and M. H. Lim, "Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review," IEEE Access, vol. 7, pp. 122644–122662, 2019.
D. Zhang, X. Han, and C. Deng, "Review on the research and practice of deep learning and reinforcement learning in smart grids," CSEE Journal of Power and Energy Systems, vol. 4, no. 3, pp. 362–370, Sept. 2018.
M. Zhou, N. Kazemi, and P. Musilek, "Distribution Grid Fault Classification and Localization using Convolutional Neural Networks," Smart Grids and Sustainable Energy, vol. 9, no. 1, May 2024, Art. no. 24.
J. Fang, K. Chen, C. Liu, and J. He, "An Explainable and Robust Method for Fault Classification and Location on Transmission Lines," IEEE Transactions on Industrial Informatics, vol. 19, no. 10, pp. 10182–10191, Oct. 2023.
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Copyright (c) 2025 Nguyen Nhan Bon, Dinh Ngoc Khanh, Thanh Lam Le, Phung Son Thanh
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