Email this article Enhancing the Statistical Hybrid Model Performance in Overhead and Underground Medium Voltage Broadband over Power Lines Channels by Adopting Empirical Channel Attenuation Statistical Distribution
Abstract
Statistical hybrid model is a statistical channel model suitable for the broadband over power lines (BPL) networks while it is based on the statistical processing of channel attenuation and capacity values of preassumed BPL topology classes. One of the key operation elements of the statistical hybrid model, which affects its results fidelity, is the selection of the appropriate channel attenuation statistical distribution among a set of well-known channel attenuation statistical distributions (i.e., such as Gaussian, Lognormal, Wald, Weibull and Gumbel distributions). The selection of the appropriate channel attenuation statistical distribution becomes a hard task since it depends on a number of factors such as the power grid type -either overhead (OV) or underground (UN) power grid-, the representative distribution BPL topology of the examined class, the applied electromagnetic interference (EMI) policies and the used coupling scheme type. The contribution of this paper is to identify the conditions whether the Empirical channel attenuation statistical distribution can act as the default distribution of statistical hybrid model (modified statistical hybrid model) thus replacing the required comparison analysis prior to the selection of the aforementioned distributions of the initial statistical hybrid model. The evaluation comparison is based on the already applied metrics of capacity percentage change and average absolute capacity percentage change.
Citation: Lazaropoulos, A. (2019). Enhancing the Statistical Hybrid Model Performance in Overhead and Underground Medium Voltage Broadband over Power Lines Channels by Adopting Empirical Channel Attenuation Statistical Distribution. Trends in Renewable Energy, 5(2), 181-217. doi:http://dx.doi.org/10.17737/tre.2019.5.2.0096
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DOI: http://dx.doi.org/10.17737/tre.2019.5.2.0096
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