Survey on Methods for Detection, Classification and Location of Faults in Power Systems Using Artificial Intelligence
2025/07/14 by Martinez-Velasco, Juan A., Serrano-Fontova, Alexandre, Bosch-Tous, Ricard +1
#FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2507.10011
Abstract
Components of electrical power systems are susceptible to failures caused by lightning strikes, aging or human errors. These faults can cause equipment damage, affect system reliability, and results in expensive repair costs. As electric power systems are becoming more complex, traditional protection methods face limitations and shortcomings. Faults in power systems can occur at anytime and anywhere, can be caused by a natural disaster or an accident, and their occurrence can be hardly predicted or avoided; therefore, it is crucial to accurately estimate the fault location and quickly restore service. The development of methods capable of accurately detecting, locating and removing faults is essential (i.e. fast isolation of faults is necessary to maintain the system stability at transmission levels; accurate and fast detection and location of faults are essential for increasing reliability and customer satisfaction at distribution levels). This has motivated the development of new and more efficient methods. Methods developed to detect and locate faults in power systems can be divided into two categories, conventional and artificial intelligence-based techniques. Although the utilization of artificial intelligence (AI) techniques offer tremendous potential, they are challenging and time consuming (i.e. many AI techniques require training data for processing). This paper presents a survey of the application of AI techniques to fault diagnosis (detection, classification and location of faults) of lines and cables of power systems at both transmission and distribution levels. The paper provides a short introduction to AI concepts, a brief summary of the application of AI techniques to power system analysis and design, and a discussion on AI-based fault diagnosis methods.
Citations
- The Animated Oat Optimization Algorithm: A nature-inspired metaheuristic for engineering optimization and a case study on Wireless Sensor Networks
- Statistical techniques in power systems fault diagnostic: Classifications, challenges, and strategic recommendations
- YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems
- Efficient Fault Detection and Categorization in Electrical Distribution Systems Using Hessian Locally Linear Embedding on Measurement Data
- Power System Fault Diagnosis with Quantum Computing and Efficient Gate Decomposition
- Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
- Deep learning-based fault location framework in power distribution grids employing convolutional neural network based on capsule network
- Detection of single-phase-to-ground faults in distribution networks based on Gramian Angular Field and Improved Convolutional Neural Networks
- A Comprehensive Overview of Large Language Models
- A Survey of Large Language Models
- Applications of Physics-Informed Neural Networks in Power Systems - A Review
- A Review of Federated Learning in Energy Systems
- Systematic Literature Review: Quantum Machine Learning and its applications
- Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions
- Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
- Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
- A Review of Graph Neural Networks and Their Applications in Power Systems
- A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems
- Mathematics for Machine Learning
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
- Efficient Creation of Datasets for Data-Driven Power System Applications
- Federated Learning: Challenges, Methods, and Future Directions
- Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
- Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
- You Only Look Once: Unified, Real-Time Object Detection
- Classification and trend analysis of threats origins to the security of power systems
- Artificial intelligence techniques for photovoltaic applications: A review
- I.—COMPUTING MACHINERY AND INTELLIGENCE
Related