@Article{cmes.2022.021198,
AUTHOR = {Liang Luo, Xingmei Li, Kaijiang Yang, Mengyang Wei, Jiong Chen, Junqian Yang, Liang Yao},
TITLE = {Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {3},
PAGES = {1565--1595},
URL = {http://www.techscience.com/CMES/v134n3/49752},
ISSN = {1526-1506},
ABSTRACT = {The emergence of power dispatching automation systems has greatly improved the efficiency of power industry
operations and promoted the rapid development of the power industry. However, with the convergence and
increase in power data flow, the data dispatching network and the main station dispatching automation system have
encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification
of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review
of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss
unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification
over power big data into data acquisition and storage processes, anomaly detection and fault discrimination
processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions
to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and
future directions are outlined.},
DOI = {10.32604/cmes.2022.021198}
}