DEVELOPMENT OF A FAULT DETECTION AND CLASSIFICATION SYSTEM IN MICROGRID USING ARTIFICIAL NEURAL NETWORKS
DOI:
https://doi.org/10.33003/fjs-2026-1002-4100Keywords:
Artificial Neural Network, Microgrids, Fault detection and classification, MATLABAbstract
As microgrids grow in significance, enabling consumers to manage their energy demands themselves, they present complexities such as fluctuating loads, two-way (bidirectional) power flow, and low fault currents that make traditional protection approaches such as Overcurrent protection and Impedance-based techniques less effective. This study explores the development of a fault detection and classification system based on Artificial Neural Networks (ANN) specifically designed for microgrids to address these complications. This research simulates a range of fault scenarios applicable in a microgrid setting using MATLAB/Simulink, extracts relevant voltage and current data, develops and trains an ANN model, and assesses its ability to accurately identify and categorize faults. The performance analysis shows the detection and classification models demonstrate remarkable accuracy, achieving low Mean Squared Error (MSE) values of 3.72 × 10⁻¹⁰ and 0.014393 and regression correlations as high as 1 and 0.998 across training, validation, and testing datasets. This research has improved microgrid systems' reliability through artificial neural networks by simultaneously reducing downtime. The findings establish a foundation for the future development of fault detection systems and incorporation into smart grid technologies.
References
Abubakar, J., & Abdulkareem, A. (2022). Critical Review of Fault Detection, Fault Classification and Fault Location Techniques for Transmission Network. Journal of Engineering Science and Technology Review, 15(2), 156–166. https://doi.org/10.25103/jestr.152.18
Alhanaf, A. S., & Balik, H. H. (2023). Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks. 1–19.
Awasthi, S., Singh, G., & Ahamad, N. (2024). Classifying Electrical Faults in a Distribution System Using K-Nearest Neighbor (KNN) Model in Presence of Multiple Distributed Generators. Journal of The Institution of Engineers (India): Series B, 105(3), 621–634. https://doi.org/10.1007/s40031-024-00994-4
Baghaee, H. R., Mlakic, D., Nikolovski, S., & Dragicevic, T. (2020). Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks. IEEE Journal of Emerging and Selected Topics in Power Electronics, 8(3), 2385–2403. https://doi.org/10.1109/JESTPE.2019.2916621
Bello, A. S., & Dodo, U. A. (2025). Performance Evaluation of Training Functions in Neural Networks for Optimal Biomass Energy Content Prediction. FUDMA Journal of Sciences, 9(11), 441–448. https://doi.org/10.33003/fjs-2025-0911-4009
Cano, A., Arévalo, P., Benavides, D., & Jurado, F. (2024). Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids. International Journal of Electrical Power and Energy Systems, 155(September 2023). https://doi.org/10.1016/j.ijepes.2023.109616
Fahim, S. R., Sarker, S. K., Muyeen, S. M., Sheikh, M. R. I., & Das, S. K. (2020). Microgrid fault detection and classification: Machine learning based approach, comparison, and reviews. Energies, 13(13), 1–21. https://doi.org/10.3390/en13133460
Grci, I. (2021). Fault Detection in DC Microgrids Using Short-Time Fourier Transform.
Jasim, A. M., Jasim, B. H., Neagu, B. C., & Alhasnawi, B. N. (2023). Coordination Control of a Hybrid AC/DC Smart Microgrid with Online Fault Detection, Diagnostics, and Localization Using Artificial Neural Networks. Electronics (Switzerland), 12(1). https://doi.org/10.3390/electronics12010187
Jayasinghe, J. A. R. R., Malindi, J. H. E., Rajapaksha, R. M. A. M., Logeeshan, V., & Wanigasekara, C. (2024). Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural Networks. IEEE Open Access Journal of Power and Energy, 11(July), 303–313. https://doi.org/10.1109/OAJPE.2024.3422387
Joshi, M. K., & Patel, R. R. (2024). Fault detection through discrete wavelet transforms and radial basis function neural network in shunt compensated distribution systems. Engineering Research Express, 6(2). https://doi.org/10.1088/2631-8695/ad46e7
Kishor, N., & Kumar, P. (2022). Distribution System Fault Detection and Classification using Wavelet Transform and Artificial Neural Networks. 8914, 69–75.
Kolla, S., & Onwonga, P. (2020). Identification of Faults in Microgrid Using Artificial Neural Networks. 115–120.
Kumar, Y. V. P., & Amir, M. (2024). Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks. Electrical Engineering. https://doi.org/10.1007/s00202-024-02329-4
Leh, A. M., Pinang, P., Hamid, A., Pinang, P., Zain, F. M., Pinang, P., Muhammad, Z., Pinang, P., Rosli, D., & Pinang, P. (2020). Fault Detection Method Using ANN for Power Transmission Line. August, 21–22.
Lin, Z., Duan, D., Yang, Q., Hong, X., Cheng, X., Yang, L., & Cui, S. (2020). Data-driven fault localization in distribution systems with distributed energy resources. Energies, 13(1), 1–16. https://doi.org/10.3390/en13010275
Liu, S., Yin, H., Zhang, Y., Liu, X., & Li, C. (2022). Fault Location Method for Distribution Network with Distributed Generation Based on Deep Learning. 2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES), 1157–1162. https://doi.org/10.1109/SPIES55999.2022.10082217
Mbey, C. F. (2023). Fault detection and classification using deep learning method and neuro-fuzzy algorithm in a smart distribution grid. June 2022, 1–19. https://doi.org/10.1049/tje2.12324
Mousavi, A., Mousavi, R., Mousavi, Y., Tavasoli, M., Arab, A., & Fekih, A. (2024). Artificial Neural Networks-Based Fault Localization in Distributed Generation Integrated Networks Considering Fault Impedance. IEEE Access, 12(June), 82880–82896. https://doi.org/10.1109/ACCESS.2024.3412991
Musa Oruma, A., Mahmud, I., Alhaji Adamu, U., Yakubu Wakawa, S., Idris, G., & Mustapha, M. (2024). Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line. International Journal of Innovative Science and Research Technology (IJISRT), 9(4), 896-902. https://doi.org/10.38124/ijisrt/ijisrt24apr651
Msheliza, S. A., & Dodo, U. A. (2025). Performance Comparison of Different Activation Functions in Neural Networks for Biomass Energy Content Prediction. FUDMA Journal of Sciences, 9(4), 285–294. https://doi.org/10.33003/f js-2025-0904-3493
Nsed, V., Etim, A & Eko, A. (2024). Fault Detection and Classification of Transmission Lines using Artificial Neural Networks with Multiple Datasets. Nigerian Journal of Engineering Research UNICROSS (NJERSU), 1(2), 58-64.
Pan, P., & Mandal, R. K. (2023). Fault detection and classification in DC microgrid clusters.
Pan, P., Mandal, R. K., Rahman, M., & Akanda, R. (2022). Fault Classification with Convolutional Neural Networks for Microgrid Systems. 2022. https://doi.org/10.1155/2022/8431450
Phafula, I., & Nixon, K. (2020). Preliminary Study of Fault Detection on an Islanded Microgrid Using Artificial Neural Networks. 1–6.
Roy, B., Adhikari, S., Datta, S., Ustun, T. S., & Devi, K. J. (2023). Deep Learning Based Relay for Online Fault Detection, Classification, and Fault Location in a Grid-Connected Microgrid. IEEE Access, 11(June), 62674–62696. https://doi.org/10.1109/ACCESS.2023.3285768
Sahoo, B. K. (2020). Fault detection in Electrical Power Transmission System using Artificial Neural Network. 29–32
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2026 Anas Faskari Shehu, Joel Oghenetega Akemu, Abdullahi Yusuf Sada
This work is licensed under a Creative Commons Attribution 4.0 International License.
