Title :
Big data analytic empowered grid applications — Is PMU a big data issue?
Author :
Bo Yang ; Yamazaki, June ; Saito, Nao ; Kokai, Yutaka ; Da Xie
Author_Institution :
Big Data Lab., Santa Clara, CA, USA
Abstract :
With the proliferation of digital measurement devices, such as smart meter on the distribution systems and phasor measurement units on the transmission systems, power companies find themselves inundated with increasingly growing data and long for efficient tools and analytical techniques to identify, digest and utilize critical information to improve the efficiency and reliability of grid operations. Many power researchers believe that the PMU related power system analytics falls under the category of Big Data Science and are keen to apply typical technologies for solution, including machine learning, data mining, cloud based computation and so on. This paper explores the reason behind such presumption, challenges to deal with PMU data, and trends of analytical techniques.
Keywords :
Big Data; cloud computing; data mining; learning (artificial intelligence); phasor measurement; power distribution reliability; power engineering computing; power grids; smart meters; transmission networks; Big Data science; PMU data; analytic empowered grid application; cloud based computation; data mining; digital measurement devices; distribution systems; grid operations; machine learning; phasor measurement units; power companies; power system analytics; smart meter; transmission systems; Analytical models; Big data; Data visualization; Monitoring; Phasor measurement units; Power system stability; Software; Synchrophasor; big data; grid operation; measurements; power system analysis;
Conference_Titel :
European Energy Market (EEM), 2015 12th International Conference on the
Conference_Location :
Lisbon
DOI :
10.1109/EEM.2015.7216718