Paper Infomation
Fault Detection and Prevention Strategies in Power Systems
Full Text(PDF, 49KB)
Author: Fuyang Miao
Abstract: Power systems are critical to modern society, but their complexity has increased with the integration of renewable energy and advanced technologies. This paper explores fault detection and prevention strategies essential for ensuring system reliability. We examine traditional methods such as overcurrent and differential protection, as well as advanced techniques including machine learning (ML) and artificial intelligence (AI). Additionally, we discuss fault prevention strategies like predictive maintenance, redundancy, and self-healing grids. Despite advancements, challenges such as the complexity of modern grids, cybersecurity threats, and economic constraints remain. The paper concludes with future prospects, emphasizing emerging technologies and global research initiatives aimed at enhancing grid resilience and security.
Keywords: Fault Detection, Fault Prevention, Power Systems, Machine Learning, Artificial Intelligence, Predictive Maintenance
References:
[1] Xu Y, Zou Y, Liu L, et al. An Improved Prevention Strategy Based on Fault Probability Detection for Commutation Failure in Line-Commutated Converter-Based High-Voltage Direct Current Transmission Systems[J].Electronics,2024,13(19):3804-3804.
[2] Liu F. Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining[J].International Journal of Reliability, Quality and Safety Engineering,2024,31(06):
[3] Fera F, Spandonidis C. A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework[J].Applied Sciences,2024,14(18):8107-8107.
[4] Kokila SL M, Christopher BV, Ramya G. Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems[J].IET Quantum Communication,2024,5(4):340-348.
[5] Senemmar S, Jacob A R, Zhang J. Non-intrusive fault detection in shipboard power systems using wavelet graph neural networks[J].Measurement: Energy,2024,3100009-100009.
[6] Zhanjun L, Bo H, Tianqi L, et al. Research on risk prevention and control strategy of power grid CPS system based on intrusion tolerance[J].IOP Conference Series: Earth and Environmental Science,2021,675(1):012156-.
[7] Engineering; New Findings on Engineering Described by Investigators at Yonsei University (Dc Power Control Strategy of Mmc for Commutation Failure Prevention In Hybrid Multi-terminal Hvdc System)[J].Energy & Ecology,2020,661-.
[8] Reinforcement Learning; Reports Outline Reinforcement Learning Study Findings from Guangxi University (Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems)[J].Journal of Engineering,2018,1034-.