Email this article The Impact of Operating Frequencies and Neural Network Training Program Properties on the Performance of Neural Network Topology Generator Methodology
Abstract
Until now, Neural Network Topology Generator Methodology (NNTGM) has been theoretically proposed so that its generated overhead low-voltage broadband over power lines topologies (NNTGM OV LV BPL topologies) may populate the existing OV LV BPL topology classes. With reference to the OV LV BPL topology class maps, which are defined by the graphical combination of ACA and RMS-DS of the OV LV BPL topologies, and the NNTGM OV LV BPL topology footprints for given indicative OV LV BPL topologies, the impact on the relative position and the size of the NNTGM OV LV BPL topology footprints has been assessed for a number of factors that affect the preparation of the Topology Identification Methodology (TIM) OV LV BPL topology database being used during the NNTGM operation. In this companion paper, the effect of the operating frequencies and the Neural Network (NN) training program properties on the relative position and the size of the NNTGM OV LV BPL topology footprints is here examined. The effect study is supported by suitable Graphical Performance Indicators (GPIs).
Citation: Lazaropoulos, A. (2025). The Impact of Operating Frequencies and Neural Network Training Program Properties on the Performance of Neural Network Topology Generator Methodology. Trends in Renewable Energy, 11(2), 237-254. doi:http://dx.doi.org/10.17737/tre.2025.11.2.00193
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DOI: http://dx.doi.org/10.17737/tre.2025.11.2.00193
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This work is licensed under a Creative Commons Attribution 4.0 License.Copyright @2014-2025 Trends in Renewable Energy (ISSN: 2376-2136, online ISSN: 2376-2144)