Problem
Research questions and friction points this paper is trying to address.
fault detection
renewable energy sources
electrical grid
relay protection systems
short circuits
A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids
🤖 AI Summary
本文评估了多种机器学习方法在电力系统中故障检测和线路识别的有效性，旨在解决传统保护系统面对复杂电网挑战时的不足。
📝 Abstract
The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional relay protection systems, which operate based on static rules and predefined thresholds, are inadequate for addressing these challenges, particularly in detecting and isolating faults such as short circuits. Consequently, the conventional methodologies applied to electrical network protection frequently fail to achieve optimal performance in fault detection, especially in terms of adherence to safety standards and the selective limitation of damage. Recent research indicates that machine learning (ML)-based approaches can effectively tackle these issues; however, variations in grid configurations and analysis windows have impeded consistent comparative assessments. In this study, we assess the efficacy of various ML models in detecting electrical faults and pinpointing defective transmission lines within a 10 ms measurement interval - a critical time-frame for real-time operational viability, for the first time. The most effective model attained an F1 score of 0.991 +/- 0.018 and demonstrated a processing time of 0.342ms +/- 0.509ms.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine Learning
Fault Detection
Line Identification
Electrical Power Grids
Real-time Performance
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