2017/12/24 by Fan Yang, Xing He, Yang, Fan +6
Computer Science · Engineering · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Fault Detection and Control Systems #Network Security and Intrusion Detection #Smart Grid Security and Resilience #stat.AP
paper · pdf · doi:10.48550/arxiv.1712.08871
7 pages, 2 figures
arxiv created 2017/12/24 · openalex publication_date 2017/12/24 · arxiv updated 2017/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multi-event detection and recognition in real time is of challenge for a modern grid as its feature is usually non-identifiable. Based on factor model, this paper porposes a data-driven method as an alternative solution under the framework of random matrix theory. This method maps the raw data into a high-dimensional space with two parts: 1) the principal components (factors, mapping event signals); and 2) time series residuals (bulk, mapping white/non-Gaussian noises). The spatial information is extracted form factors, and the termporal infromation from residuals. Taking both spatial-tempral correlation into account, this method is able to reveal the multi-event: its components and their respective details, e.g., occurring time. Case studies based on the standard IEEE 118-bus system validate the proposed method.