Embedded system design for fault detection in power distribution networks
Department of Electrical and Electronics Engineering Government Polytechnic Kushalnagar Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2022, 13(02), 625-632
Article DOI: 10.30574/wjarr.2022.13.2.0069
Publication history:
Received on 02 February 2022; revised on 10 February 2022; accepted on 20 February 2022
Abstract:
Power distribution networks are critical for ensuring a stable and uninterrupted supply of electricity. However, faults in these networks can lead to severe disruptions, increased maintenance costs, and potential safety hazards. Rapid and accurate fault detection is essential to minimize downtime, enhance grid reliability, and prevent large-scale power failures. This research paper presents the design and implementation of an embedded system for real-time fault detection in power distribution networks. The proposed system integrates advanced sensing technologies, microcontrollers, and communication modules to detect, classify, and localize faults efficiently. The system employs voltage and current sensors to monitor network conditions and utilizes wireless communication protocols to transmit fault data to a central monitoring unit. Additionally, machine learning algorithms are implemented for predictive maintenance, enabling early fault prediction and proactive intervention. Performance evaluation is conducted through experimental simulations and real-time testing, demonstrating the system’s capability to enhance fault detection accuracy and response speed. The paper includes comprehensive analyses of system performance, fault classification accuracy, and efficiency improvements through figures, tables, and bar charts. The findings suggest that integrating embedded systems with intelligent fault detection techniques can significantly improve the resilience and efficiency of modern power distribution networks.
Keywords:
Embedded Fault Detection; Power Distribution Networks; Real-Time Monitoring; Iota-Based Fault Detection; Machine Learning for Fault Classification; Smart Grid Fault Diagnosis
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