Guo, Y, Feng, S, Li, K orcid.org/0000-0001-6657-0522 et al. (3 more authors) (2016) Big data processing and analysis platform for condition monitoring of electric power system. In: Proceedings of the 2016 UKACC 11th International Conference on Control (CONTROL). 2016 UKACC 11th International Conference on Control (CONTROL), 31 Aug - 02 Sep 2016, Belfast, UK. IEEE ISBN 978-1-4673-9891-6
Abstract
This paper presents a preliminary study of developing a novel platform for big data management, processing and analysis of modern power systems. The framework comprises a big data acquisition subsystem, a big data analysis subsystem, a decision-making assistance subsystem and an information integration subsystem. For the big data management system, a novel structure is designed according to three different data resources, including database, data files and data stream. Further, powerful open-source computation algorithms and self-developed novel intelligent methods are integrated in the big data analysis system. To be specific, our early work on statistical processing monitoring (Principal Component Analysis (PCA)), advanced modelling methods (Fast Recursive Algorithm (FRA)) and newly developed optimization method (Teaching-Learning Based Optimization (TLBO)) are integrated into a self-developed analysis module. Thus, with the novel big data acquisition structure and data processing engine, the proposed platform can provide a powerful tool for big data analytic based Smart Grid monitoring.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Big data; Monitoring; Circuit breakers; Circuit faults; Power transformer insulation; Oil insulation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Nov 2019 14:32 |
Last Modified: | 05 Nov 2019 14:32 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/control.2016.7737581 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153036 |