Call for Paper - Upcoming Issues

Edge-Optimized Embedded System for Low-Latency Fault Detection in Electrical Grids

International Journal of Electrical and Electronics Engineering
© 2025 by SSRG - IJEEE Journal
Volume 12 Issue 11
Year of Publication : 2025
Authors : Divya Kumari Tankala, A. Rajesh Kumar, S. Nagarjuna Reddy, P Jyothi, Elangovan Muniyandy
10.14445/23488379/IJEEE-V12I11P117
pdf
How to Cite?

Divya Kumari Tankala, A. Rajesh Kumar, S. Nagarjuna Reddy, P Jyothi, Elangovan Muniyandy, "Edge-Optimized Embedded System for Low-Latency Fault Detection in Electrical Grids," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 11, pp. 207-218, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I11P117

Abstract:

Accurate fault detection in power grids is critical to maintain an uninterrupted power supply, reduce outages, and safeguard vital infrastructure. Conventional methods based on cloud platforms often face limitations like high latency, high communication overhead, scalability limitations, and high power consumption. Such limitations hinder their applications in real-time grid monitoring, where quick response and energy efficiency are critical. In order to break these constraints, this paper suggests an edge-optimized embedded fault detection framework that integrates adaptive wavelet-based preprocessing with a light-weight machine learning model run on microcontroller-class hardware. The framework is intended to extract fault-related transient features and classify accurately in real time under limited computational resources. Experimental verification shows that the system has 94.6% detection accuracy while cutting latency by 80% and minimizing energy consumption by 85.3% over cloud-based solutions. The findings reveal the potential of embedded edge intelligence as a feasible, scalable, and energy-efficient solution for future-proof smart grid infrastructures.

Keywords:

Edge computing, Fault detection, Embedded systems, Machine Learning, Smart grid, Real-time monitoring.

References:

[1] Anita Mohanty, Subrat Kumar Mohanty, and Ambarish G. Mohapatra, Real-Time Monitoring and Fault Detection in AI-Enhanced Wastewater Treatment Systems, The AI Cleanse: Transforming Wastewater Treatment through Artificial Intelligence: Harnessing Data-Driven Solutions, Springer Nature, pp. 165-199, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Prithwiraj Roy et al., “Noise Resilient Learning for Attack Detection in Smart Grid PMU Infrastructure,” IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 2, pp. 618-635, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Santosh Gore et al., “AI-Based Wireless Communication: Ultra-Reliable MAC Protocols,” International Conference on Intelligent Computing and Networking, Mumbai, India, pp. 271-287, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Yujie Qin, Mustafa A. Kishk, and Mohamed-Slim Alouini, “Drone Charging Stations Deployment in Rural Areas for Better Wireless Coverage: Challenges and Solutions,” IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 148-153, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Muhammad Waqas Ahmad et al., “Intelligent Framework for Automated Failure Prediction, Detection, and Classification of Mission Critical Autonomous Flights,” ISA Transactions, vol. 129, pp. 355-371, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Santosh Gore et al., “Augmented Intelligence in Machine Learning for Cybersecurity: Enhancing Threat Detection and Human-Machine Collaboration,” 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, pp. 638-644, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Adnan Sabovic et al., “Towards Energy-Aware TinyML on Battery-less IoT Devices,” Internet of Things, vol. 22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Marco Giordano et al., “Design and Performance Evaluation of an Ultralow-Power Smart IoT Device with Embedded TinyML for Asset Activity Monitoring,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Raúl de la Fuente, Luciano Radrigan, and Anibal S. Morales, “Enhancing Predictive Maintenance in Mining Mobile Machinery through a TinyML-Enabled Hierarchical Inference Network,” arXiv Preprint, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Chunhe Ni, Jiang Wu, and Hongbo Wang, “Energy-Aware Edge Computing Optimization for Real-Time Anomaly Detection in IoT Networks,” Proceedings of the 7th International Conference on Computing and Data Science, vol. 139, no. 1, pp. 42-53, 2025.
 [CrossRef] [Google Scholar] [Publisher Link]
[11] Lucas C. Silva et al., “Embedded Decision Support System for Ultrasound Nondestructive Evaluation based on Extreme Learning Machines,” Computers & Electrical Engineering, vol. 90, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Kamal Chandra Paul et al., “Series AC Arc Fault Detection using a Decision Tree-Based Machine Learning Algorithm and Raw Current,” 2022 IEEE Energy Conversion Congress and Exposition (ECCE), Detroit, MI, USA, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Zijie Chen, Yiming Gao, and Junrui Liang, “LoPDM: A Low-Power On-Device Predictive Maintenance System based on Self-Powered Sensing and TinyML,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Afaf Taik, Boubakr Nour, and Soumaya Cherkaoui, “Empowering Prosumer Communities in the Smart Grid with Wireless Communications and Federated Edge Learning,” IEEE Wireless Communications, vol. 28, no. 6, pp. 26-33, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Sapardi Djoko Damo, “Adaptive Multilayer Architectures for Intelligent Edge Computing Leveraging Federated Learning and Energy-Efficient Neural Optimization in Distributed Electrical Systems,” International Journal of Information Technology and Electrical Engineering (IJITEE), vol. 14, no. 1, pp. 52-57, 2025.
[Google Scholar]
[16] Mohamad Hazwan Mohd Ghazali, and Wan Rahiman, “A Novel Fault Detection Approach in UAV with Adaptation of Fuzzy Logic and Sensor Fusion,” IEEE/ASME Transactions on Mechatronics, vol. 30, no. 1, pp. 381-391, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Jatinder Kumar et al., “An Adaptive Evolutionary Neural Network Model for Load Management in Smart Grid Environment,” IEEE Transactions on Network and Service Management, vol. 22, no. 1, pp. 242-254, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Guoqian Jiang et al., “A Federated Learning Framework for Cloud-Edge Collaborative Fault Diagnosis of Wind Turbines,” IEEE Internet Things Journal, vol. 11, no. 13, pp. 23170-23185, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Zheshu Jia, “Research on Image Recognition and Classification Algorithms in Cloud Computing Environment based on Deep Neural Networks,” IEEE Access, vol. 13, pp. 19728-19754, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jaromir Konecny et al., “Computational Cost and Implementation Analysis of a Wavelet-Based Edge Computing Method for Energy-Harvesting Industrial IoT Sensors,” IEEE Access, vol. 12, pp. 193607-193621, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Chuan-Jun Su, and Yi Li, “Recurrent Neural Network-Based Real-Time Failure Detection of Storage Devices,” Microsystem Technologies, vol. 28, no. 2, pp. 621-633, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Shubbham Gupta, and Shiv Naresh Shivhare, “Embedded TinyML for Predictive Maintenance: Vibration Analysis on ESP32 with Real-Time Fault Detection in Industrial Equipment,” International Journal on Computational Modelling Applications, vol. 2, no. 2, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Swapnil Sayan Saha, Sandeep Singh Sandha, and Mani Srivastava, “Machine Learning for Microcontroller-Class Hardware: A Review,” IEEE Sensors Journal, vol. 22, no. 22, pp. 21362-21390, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Maria Trigka, and Elias Dritsas, “Wireless Sensor Networks: From Fundamentals and Applications to Innovations and Future Trends,” IEEE Access, vol. 13, pp. 96365-96399, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Jibinraj Antony et al., “Adapting to Changes: A Novel Framework for Continual Machine Learning in Industrial Applications,” Journal of Grid Computing, vol. 22, no. 4, pP. 71, 2024.
[CrossRef] [Google Scholar] [Publisher Link]