Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
Energies
; 15(7):2417, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1785582
ABSTRACT
The grid operation and communication network are essential for smart grids (SG). Wi-SUN channel modelling is used to evaluate the performance of Wi-SUN smart grid networks, especially in the last-mile communication. In this article, the distribution approximation of the received signal strength for IEEE 802.15.4g Wi-SUN smart grid networks was investigated by using the Rician distribution curve fitting with the accuracy improvement by the biased approximation methodology. Specifically, the Rician distribution curve fitting was applied to the received signal strength indicator (RSSI) measurement data. With the biased approximation method, the Rician K-factor, a non-centrality parameter (rs), and a scale parameter (σ) are optimized such that the lower value of the root-mean squared error (RMSE) is acheived. The environments for data collection are selected for representing the location of the data concentrator unit (DCU) and the smart meter installation in the residential area. In summary, the experimental results with the channel model parameters are expanded to the whole range of Wi-SUN’s frequency bands and data rates, including 433.92, 443, 448, 923, and 2440 MHz, which are essential for the successful data communication in multiple frequency bands. The biased distribution approximation models have improved the accuracy of the conventional model, by which the root mean-squared error (RMSE) is reduced in the percentage range of 0.47–3.827%. The proposed channel models could be applied to the Wi-SUN channel simulation, smart meter installation, and planning in smart grid networks.
Energy; received signal strength distribution approximation; Wi-SUN channel modeling; smart grid networks; Wireless communications; Performance evaluation; Bandwidths; Modelling; Data communication; Residential areas; Internet of Things; Signal strength; Frequencies; Smart grid; Data collection; COVID-19; Wireless networks; Electricity; Energy industry; Curve fitting; Root-mean-square errors; Approximation method; Automatic meter reading; Communications networks; Mathematical models; Error reduction; Networks; Coronaviruses; Parameters; Approximation; Thailand
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Energies
Year:
2022
Document Type:
Article
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