HPC-Based Intelligent Volt/VAr Control of Unbalanced Distribution Smart Grid in the Presence of Noise

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) scala...

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Vydáno v:IEEE transactions on smart grid Ročník 8; číslo 3; s. 1446 - 1459
Hlavní autoři: Anwar, Adnan, Mahmood, A. N., Taheri, Javid, Tari, Zahir, Zomaya, Albert Y.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.05.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Advanced metering infrastructure
Algorithms
Capacitors
Computational modeling
Computer Science
Control systems
Data processing
Datavetenskap
Feeders
HPC
Measurement
Microprocessors
Noise
Noise measurement
OpenDSS
Optimization
parallel
Parallel processing
Particle swarm optimization
Phase distribution
Power loss
PSO
Random noise
Smart grid
Smart grids
Unbalance
Volt/VAr
Voltage control
ISSN:1949-3053, 1949-3061, 1949-3061
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Shrnutí: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.
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ISSN:1949-3053
1949-3061
1949-3061
DOI:10.1109/TSG.2017.2662229