Enhancing Transmission Network Reliability: AI-Powered Fault Diagnosis in Smart Grid
Keywords:
Active Distribution Networks (ADNs), Advanced Metering Infrastructure (AMI), Electric Vehicles (EVs), Phasor Measurement Units (PMUs), Traditional fault detection and diagnosis (FDD), Trusted third party (TTP)Abstract
A smart grid is a modern electricity system that addresses the problems of the traditional power grid. It becomes "smart" when all its parts work intelligently, including power generation, transmission, distribution, control, and smart meters. This paper mainly focuses on smart transmission and distribution to improve the performance of the smart grid. The system was tested under different fault conditions, such as single-line-to-ground faults and double-line-to-ground faults, to check its reliability. The performance was also verified using MATLAB Simulink simulations. This paper explains the cybersecurity of important devices used in Active Distribution Networks (ADNs), such as Phasor Measurement Units (PMUs), smart meters, Advanced Metering Infrastructure (AMI), and protection relays. It also discusses the main technologies that support ADNs, including microgrids, Electric Vehicles (EVs), the Internet of Things (IoT), and smart homes, and explains how they help improve the power system. Traditional Fault Detection and Diagnosis (FDD) methods are not enough for modern power systems because today's power networks are more complex and require fast, real-time monitoring. Deep Learning (DL) provides a better solution by automatically learning important patterns from large amounts of data. This helps detect and identify faults more accurately, quickly, and reliably. This situation increases the risk of cyberattacks because attackers can target the trusted third party (TTP), which may provide services to multiple smart grid operators. To improve security and reliability, the paper proposes a fault-tolerant multi-subset data aggregation scheme that continues to collect and process data correctly even if some parts of the system fail or are attacked. To solve the challenges of fault detection in modern power systems, this study introduces a new method that combines spectral graph theory with deep learning. This approach improves the speed and accuracy of fault detection, making the power system more reliable and efficient. Enhancing transmission network reliability ai-powered fault diagnosis in smart grid.
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