Noise Profile of Wireless Channels in High Voltage Substations

Shapoury, Alireza; Kezunovic, Mladen

doi:10.1109/pes.2007.385938

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…This is of paramount importance as EMI plays an important role in scenarios like substations where there are many potential sources of background noise (such as switchgears, transformers, etc.) [ 40 , 41 ]. A FSH3 spectrum analyzer (Rhode & Schwarz ® , Munich, Germany) has been used to this end.…”

Section: Methods and Toolsmentioning

confidence: 99%

Evaluating the More Suitable ISM Frequency Band for IoT-Based Smart Grids: A Quantitative Study of 915 MHz vs. 2400 MHz

Sandoval

García-Sánchez

García‐Sánchez

et al. 2016

Sensors

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IoT has begun to be employed pervasively in industrial environments and critical infrastructures thanks to its positive impact on performance and efficiency. Among these environments, the Smart Grid (SG) excels as the perfect host for this technology, mainly due to its potential to become the motor of the rest of electrically-dependent infrastructures. To make this SG-oriented IoT cost-effective, most deployments employ unlicensed ISM bands, specifically the 2400 MHz one, due to its extended communication bandwidth in comparison with lower bands. This band has been extensively used for years by Wireless Sensor Networks (WSN) and Mobile Ad-hoc Networks (MANET), from which the IoT technologically inherits. However, this work questions and evaluates the suitability of such a “default” communication band in SG environments, compared with the 915 MHz ISM band. A comprehensive quantitative comparison of these bands has been accomplished in terms of: power consumption, average network delay, and packet reception rate. To allow such a study, a dual-band propagation model specifically designed for the SG has been derived, tested, and incorporated into the well-known TOSSIM simulator. Simulation results reveal that only in the absence of other 2400 MHz interfering devices (such as WiFi or Bluetooth) or in small networks, is the 2400 MHz band the best option. In any other case, SG-oriented IoT quantitatively perform better if operating in the 915 MHz band.

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Section: Methods and Toolsmentioning

confidence: 99%

Evaluating the More Suitable ISM Frequency Band for IoT-Based Smart Grids: A Quantitative Study of 915 MHz vs. 2400 MHz

Sandoval

García-Sánchez

García‐Sánchez

et al. 2016

Sensors

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“…The International Special Committee on Radio Interference (CISPR) investigated radio noise originating from high voltage (HV) power equipment and provided recommendations for reducing the radio noise generated in SGs [5]. Impulse noise has been investigated in HV substations including its influence on the performance of wireless channels and modulations [6]. EMI impacts SG wireless communication equipment and this was studied in [7].…”

Section: Adequate Protocol For Crsn In An Sgmentioning

confidence: 99%

Radio Resource Allocation Improvements in CRSN for Smart Grid: A Survey

Ogbodo¹,

Dorrell²,

Abu‐Mahfouz³

2019

Preprint

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A cognitive radio sensor network (CRSN) based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and also different from the conventional SG that uses a static resource allocation technique to allocate resources to sensor nodes and communication devices in the SG network. Due to the challenges associated with competitive sensor nodes and communication devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation (RRA) has been proposed as a solution for allocating radio resources to sensor nodes in a CRSN based smart grid ecosystem (network). These challenges include energy/power constraints, poor quality of service (QoS), interference, delay, spectrum efficiency issues, and excessive spectrum hand-offs. Hence, the optimization of resource allocation criteria, such as energy efficiency, throughput maximization, QoS guarantee, fairness, priority, interference mitigation/avoidance, etc., will go a long way in addressing the problems of RRA in a CRSN based SG. Consequently, this work explores RRA in CRSNs for SGs. Various resource allocation schemes, as well as its architecture in a CRSN for SG environment, are presented. The work reported in this paper introduces a model called the “guaranteed network connectivity channel allocation” for throughput maximization (GNC-TM) and optimal spectrum band determination in RRA for improved throughput criteria in CRSNs for SGs. The results show that the model outperforms the existing protocol in terms of throughput and error probability. Finally, the contribution to knowledge and future research direction, such as energy efficiency and hybrid energy harvesting schemes are highlighted.

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“…Impulsive noise in outdoor environments may result from some other effects such as vehicle ignition. Besides these regular environments, noise characteristics of electric-powersystem environments may be dominated by the presence of impulsive noise as well [46,47]. For instance, gap breakdown discharge phenomenon, that is, mainly caused by circuit breaker opening may result in a very strong impulsive noise in a transformer substation.…”

Section: Noise Characteristicsmentioning

confidence: 99%

A Review of Wireless and PLC Propagation Channel Characteristics for Smart Grid Environments

Guzelgoz

Arslan

Islam

et al. 2011

Journal of Electrical and Computer Engineering

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Wireless, power line communication (PLC), fiber optic, Ethernet, and so forth are among the communication technologies on which smart grid communication infrastructure is envisioned to be built. Among these, wireless and PLC-based solutions are attractive considering the cost of initial deployment. Wireless communication deployment in smart grid covers a variety of environments such as indoor, outdoor, and electric-power-system facilities. Similar diversity is expected in PLC deployment as well covering low voltage (LV), medium voltage (MV), and high voltage (HV) segments of the grid. In spite of being attractive, wireless and PLC channels are very harsh posing great challenges to performance of communication systems. In proposing solutions to smart grid communication needs, two approaches are likely to be followed. One is based on the use of existing wireless and PLC technologies with some modifications, and the other relies upon developing novel communication protocols particularly addressing the smart grid needs. Both of these approaches require an in-depth knowledge of communication channel characteristics. The aim of this study is to reveal the wireless and PLC channel characteristics of smart grid environments in terms of several parameters such as path loss and attenuation, time dispersion, time selectivity, amplitude statistics, and noise characteristics.

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Noise Profile of Wireless Channels in High Voltage Substations

Cited by 6 publications

References 9 publications

Evaluating the More Suitable ISM Frequency Band for IoT-Based Smart Grids: A Quantitative Study of 915 MHz vs. 2400 MHz

Evaluating the More Suitable ISM Frequency Band for IoT-Based Smart Grids: A Quantitative Study of 915 MHz vs. 2400 MHz

Radio Resource Allocation Improvements in CRSN for Smart Grid: A Survey

A Review of Wireless and PLC Propagation Channel Characteristics for Smart Grid Environments

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