A Distributed Computing Platform Supporting Power System Security Knowledge Discovery Based on Online Simulation

Power systems are generating masses of data, including measurement and simulation data. To operate and control power systems more effectively, this paper establishes a distributed platform to store, read, and compute massive amounts of data. Our distributed computing platform can support online simu...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2017-05, Vol.8 (3), p.1513-1524
Main Authors: Huang, Tian-en, Guo, Qinglai, Sun, Hongbin
Format: Article
Language:English
Subjects:
Computational modeling
Computer networks
Computer simulation
Computing time
Cybersecurity
Data analysis
Data management
Data mining
Distributed computing platform
Distributed databases
distributed feature selection
distributed massive sampling simulation
Distributed processing
Electric power distribution
Knowledge discovery
On-line systems
online simulation
Power system security
power system security knowledge discovery
Power system stability
Simulation
System effectiveness
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Summary:Power systems are generating masses of data, including measurement and simulation data. To operate and control power systems more effectively, this paper establishes a distributed platform to store, read, and compute massive amounts of data. Our distributed computing platform can support online simulation based power system security knowledge discovery through big data analysis. First, a framework for a distributed computing platform is designed. Then, distributed algorithms are developed, including a distributed massive sampling simulation method and a distributed feature selection method. Next, the software platform and hardware platform for the distributed computing platform are established. Finally, the platform is applied to the Guangdong Province Power System in China to evaluate its accuracy and efficiency. The simulation results show that the distributed computing platform can improve computing efficiency and perform better than a centralized platform.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2016.2571442