research-article

Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system

Authors:

Ganesh Kumar VenayagamoorthyAuthors Info & Claims

Neural Networks, Volume 93, Issue C

Pages 21 - 35

https://doi.org/10.1016/j.neunet.2017.03.008

Published: 01 September 2017 Publication History

Abstract

To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results.

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Cited By

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https://dl.acm.org/doi/10.1155/2022/9928030
Qadir ZEver EBatunlu C(2021)Use of Neural Network Based Prediction Algorithms for Powering Up Smart Portable AccessoriesNeural Processing Letters10.1007/s11063-020-10397-353:1(721-756)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11063-020-10397-3

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cover image Neural Networks

Neural Networks Volume 93, Issue C

September 2017

214 pages

ISSN:0893-6080

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Copyright © Elsevier Ltd.

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Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 September 2017

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Cited By

Zhang LMa X(2022)Analysis on the Path of Digital Villages Affecting Rural Residents’ Consumption UpgradeComputational Intelligence and Neuroscience10.1155/2022/99280302022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9928030
Qadir ZEver EBatunlu C(2021)Use of Neural Network Based Prediction Algorithms for Powering Up Smart Portable AccessoriesNeural Processing Letters10.1007/s11063-020-10397-353:1(721-756)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11063-020-10397-3

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