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. G. (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, 181-217. DOI: 10.17737/tre.2019.5.2.0096
Highlights
Living in the era of Internet of Things, we are witnessing an ever increasing number of devices with ubiquitous intelligence, which are interconnected via embedded systems and networks, that can communicate with humans and other devices changing our lives [1]
Each distribution Broadband over Power Lines (BPL) topology class is filled with statistically equivalent BPL topologies, which are generated by appropriately deploying a random number generator, that are characterized by the same maximum likelihood estimators (MLEs) with the representative topology of the examined class for given power grid type, coupling scheme and channel attenuation statistical distribution
The performance results of the modified statistical hybrid model have been compared against the results of the initial statistical hybrid model when various scenarios occur such as different power grid types, BPL topology classes, electromagnetic interference (EMI) policies and coupling schemes
Summary
Living in the era of Internet of Things, we are witnessing an ever increasing number of devices with ubiquitous intelligence, which are interconnected via embedded systems and networks, that can communicate with humans and other devices changing our lives [1]. In accordance with [6], Weibull and Wald channel attenuation statistical distributions perform the best capacity estimations in OV MV and UN MV power grid types regardless of the examined BPL topology class and the applied coupling scheme when EMI policies committed to the broadband character of BPL technology are adopted (e.g., FCC Part 15 of [23]). When EMI policies less protective to the BPL technology are adopted (e.g., German Reg TP NB30 and the BBC / NATO Proposal of [13], [24], [25]), a more complex situation occurs in terms of the most suitable channel attenuation statistical distribution for the capacity estimations while the number of unsuccessful estimations significantly increases in all the cases examined In this paper, it is investigated the potential of using only one channel attenuation statistical distribution, say, the Empirical channel attenuation statistical distribution, instead of trying to identify the best channel attenuation statistical distribution by taking into consideration each time the current operation settings.
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