Abstract
Broadband over Power Lines (BPL) networks that are deployed across the smart grid can benefit from the usage of machine learning, as smarter grid diagnostics are collected and analyzed. In this paper, the neural network identification methodology of Overhead Low-Voltage (OV LV) BPL networks that aims at identifying the number of branches for a given OV LV BPL topology channel attenuation behavior is proposed, which is simply denoted as NNIM-BNI. In order to identify the branch number of an OV LV BPL topology through its channel attenuation behavior, NNIM-BNI exploits the Deterministic Hybrid Model (DHM), which has been extensively tested in OV LV BPL networks for their channel attenuation determination, and the OV LV BPL topology database of Topology Identification Methodology (TIM). The results of NNIM-BNI towards the branch number identification of OV LV BPL topologies are compared against the ones of a newly proposed TIM-based methodology, denoted as TIM-BNI.
Highlights
The evolution of the today’s traditional power grid hastens the coexistence of this grid with an intelligent IP-based communications network enhanced with a plethora of broadband applications, which is widely referred to as the smart grid [1,2,3,4,5]
Note that there can be no blind approximation by NNIM-BNI and Topology Identification Methodology (TIM)-BNI in the LOS case and for that reason is examined for verification issues; and (iii) The urban case B of 5 branches has been excluded for a further examination due to the high delay that imposes to the TIM Overhead Low-Voltage (OV LV) Broadband over Power Lines (BPL) topology database preparation
On the basis of the default operation settings of the base scenario given in Sec.4.1, the performance metric of root-mean-square deviation (RMSD) is applied in order to assess TIM-BNI and NNIM-BNI approximations
Summary
The evolution of the today’s traditional power grid hastens the coexistence of this grid with an intelligent IP-based communications network enhanced with a plethora of broadband applications, which is widely referred to as the smart grid [1,2,3,4,5]. The approximation performances of the two proposed branch number identification methodologies of this paper, i.e., TIM-BNI and NNIM-BNI, are going to be assessed and compared for indicative OV LV BPL topologies that lie outside the TIM OV LV BPL topology database when different operation settings are examined. The four indicative OV LV BPL topologies shown in green background color in Table 1 (i.e., urban case A, suburban case, rural case and LOS case) are going to be further adopted, so that the approximation performances of the two proposed branch number identification methodologies of this paper, say, NNIM-BNI and TIM-BNI. Note that there can be no blind approximation by NNIM-BNI and TIM-BNI in the LOS case and for that reason is examined for verification issues; and (iii) The urban case B of 5 branches has been excluded for a further examination due to the high delay that imposes to the TIM OV LV BPL topology database preparation
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