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HPC-Based Intelligent Volt/VAr Control of Unbalanced Distribution Smart Grid in the Presence of Noise
UNSW, Canberra, ACT 2600, Australia..
La Trobe Univ, Bundoora, Vic 3086, Australia..
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Karlstad Univ, Dept Comp Sci, S-65188 Karlstad, Sweden..ORCID iD: 0000-0001-9194-010X
RMIT Univ, Distributed Syst, Melbourne, Vic 3001, Australia..
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2017 (English)In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 8, no 3, p. 1446-1459Article in journal (Refereed) Published
Abstract [en]

The performance of Volt/VAr optimization has been significantly improved due to the integration of measurement data obtained from the advanced metering infrastructure of a smart grid. However, most of the existing works lack: 1) realistic unbalanced multi-phase distribution system modeling; 2) scalability of the Volt/VAr algorithm for larger test system; and 3) ability to handle gross errors and noise in data processing. In this paper, we consider realistic distribution system models that include unbalanced loadings and multi-phased feeders and the presence of gross errors such as communication errors and device malfunction, as well as random noise. At the core of the optimization process is an intelligent particle swarm optimization-based technique that is parallelized using high performance computing technique to solve Volt/VAr-based power loss minimization problem. Extensive experiments covering the different aspects of the proposed framework show significant improvement over existing Volt/VAr approaches in terms of both the accuracy and scalability on IEEE 123 node and a larger IEEE 8500 node benchmark test systems.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 8, no 3, p. 1446-1459
Keywords [en]
Smart grid, Volt/VAr, OpenDSS, noise, HPC, parallel, PSO
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kau:diva-65481DOI: 10.1109/TSG.2017.2662229ISI: 000399990200040OAI: oai:DiVA.org:kau-65481DiVA, id: diva2:1169755
Available from: 2017-12-29 Created: 2017-12-29 Last updated: 2018-06-27Bibliographically approved

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Taheri, Javid

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