Email this article Virtual Topologies for Populating Overhead Low-Voltage Broadband over Powerlines Topology Classes by Exploiting Neural Network Topology Generator Methodology (NNTGM) - Part 1: Theory
Abstract
Based on a set of indicative overhead low voltage broadband over power lines (OV LV BPL) topologies of respective OV LV BPL topology classes, Neural Network Topology Generator Methodology (NNTGM) is theoretically proposed in this paper, so that its generated OV LV BPL topologies (NNTGM OV LV BPL topologies) can populate the OV LV BPL topology classes. Given the indicative topology of the OV LV BPL topology class, the NNTGM OV LV BPL topologies can be statistically familiar with the corresponding indicative OV LV BPL topology in terms of the theoretical channel attenuation behavior. Actually, NNTGM is based on the reverse procedure of the Neural Network Identification Methodology for the distribution line and branch Line Length Approximation (NNIM-LLA); say, NNTGM generates theoretical channel attenuation behaviors given the number of branches, the distribution line lengths and the branch line lengths, when appropriate NNTGM default operation settings are assumed. The statistical familiarity between the examined indicative OV LV BPL topology and an NNTGM OV LV BPL topology is examined after the application of appropriate channel attenuation metrics. On the basis of the channel attenuation metrics, class maps are theoretically defined so that the relative positions of indicative OV LV BPL topologies and their respective NNTGM OV LV BPL topologies (NNTGM virtual topologies) can be further studied.
Citation: Lazaropoulos, A. (2024). Virtual Topologies for Populating Overhead Low-Voltage Broadband over Powerlines Topology Classes by Exploiting Neural Network Topology Generator Methodology (NNTGM) - Part 1: Theory. Trends in Renewable Energy, 10(3), 301-314. doi: http://dx.doi.org/10.17737/tre.2024.10.3.00181
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DOI: http://dx.doi.org/10.17737/tre.2024.10.3.00181
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