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Access Selection Algorithm of Heterogeneous Wireless Networks for Smart Distribution Grid Based on Entropy-Weight and Rough Set

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Published/Copyright: November 28, 2017
International Journal of Emerging Electric Power Systems
From the journal International Journal of Emerging Electric Power Systems Volume 18 Issue 6

Abstract

To improve the reliability of communication service in smart distribution grid (SDG), an access selection algorithm based on dynamic network status and different service types for heterogeneous wireless networks was proposed. The network performance index values were obtained in real time by multimode terminal and the variation trend of index values was analyzed by the growth matrix. The index weights were calculated by entropy-weight and then modified by rough set to get the final weights. Combining the grey relational analysis to sort the candidate networks, and the optimum communication network is selected. Simulation results show that the proposed algorithm can implement dynamically access selection in heterogeneous wireless networks of SDG effectively and reduce the network blocking probability.

Keywords: smart distribution grid; network access selection; entropy-weight; rough set; network blocking probability

Acknowledgements

This research was supported by Science Project of State Grid Corporation of China (Grant No. SGJSSZOOFZJS1501091), and Research Project of Advanced and Application Foundation in Chongqing (Grant No. cstc2015jcyjA40007 and No. cstc2015jcyjA40009), and the National High Technology Research and Development Program of China (Grant No. 2015AA043801).

Appendix

A
Figure 1: Communication network architecture of SDG.
Figure 1:

Communication network architecture of SDG.

Figure 2: Access selection process of heterogeneous wireless networks for SDG.
Figure 2:

Access selection process of heterogeneous wireless networks for SDG.

Figure 3: Network model of access layer for SDG.
Figure 3:

Network model of access layer for SDG.

Figure 4: Comparison of network blocking probability. a Comparison of network blocking probability between E-RS and SAW. b Comparison of network blocking probability between E-RS and GAAS.
Figure 4:

Comparison of network blocking probability. a Comparison of network blocking probability between E-RS and SAW. b Comparison of network blocking probability between E-RS and GAAS.

Figure 5: Comparison of the average network blocking probability.
Figure 5:

Comparison of the average network blocking probability.

Figure 6: Comparison of the variance of network blocking probability.
Figure 6:

Comparison of the variance of network blocking probability.

Figure 7: Comparison of packet loss rate for protection and control category service.
Figure 7:

Comparison of packet loss rate for protection and control category service.

Figure 8: Comparison of average delay for protection and control category service.
Figure 8:

Comparison of average delay for protection and control category service.

B
Table 1:

Requirements of different communication service in wireless communication for SDG.

Distribution businessTransmission rateTransmission distanceTransmission delayPacket loss rateBit error rateSecurityReliability
Pilot protection>2Mbps>2Km<40ms<0.2 %<0.1 %very highvery high
Distribution automationremote measure>30Kbps>2Km<100ms<0.2 %<0.1 %very highvery high
remote sign>30Kbps>2Km<100ms<0.2 %<0.1 %very highvery high
remote control>30Kbps>2Km<100ms<0.2 %<0.1 %very highvery high
Control and management of distributed energy station>100Kbps>3Km<200ms<0.3 %<0.1very highvery high
Equipment condition monitoring>60Kbps>2Km<3s<0.5 %<0.2 %highhigh
Video surveillance>2Mbps>2Km<5s<0.5 %<0.2 %highhigh
Table 2:

The index requirements of communication service in SDG.

Communication service of SDGTransmission delayTransmission rateTransmission reliability
Protection category<40ms2MbpsExtremely reliable
Control category<100ms30KbpsExtremely reliable
Information monitoring<3s60KbpsReliable
Video category<5s2MbpsReliable
Table 3:

Decision table.

Conditional attribute set EDecision attribute set F
Nonempty finite set UReceived signal strength

(e1)		Network bandwidth

(e2)		Network delay

(e3)		Packet loss rate

(e4)		…Network performance level
LTE-230MHza11a12a13a14…d1n
WiMAXa21a22a23a24…d2n
WIFIa31a32a33a34…d3n
SWCa41a42a43a44…d4n
…………………
Table 4:

Performance comparison of different access selection algorithms.

Algorithm categoryWhether to reflect overall performanceWhether to consider communication business needsAlgorithm complexityAlgorithm flexibility
Single indexNoNolowlow
Cost functionYesNolowhigher
Intelligence theoryYesYeshighhigher
Multiple attributesYesYeslowerhigher

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Received: 2016-9-5
Accepted: 2017-11-16
Published Online: 2017-11-28

© 2017 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/ijeeps-2016-0188
Keywords for this article
smart distribution grid; network access selection; entropy-weight; rough set; network blocking probability

Articles in the same Issue

  2. Placement of Synchronized Measurements for Power System Observability during Cascaded Outages
  3. A Methodology to Prevent Failure in Single Pole Reclosing Operations
  4. Load Model Verification, Validation and Calibration Framework by Statistical Analysis on Field Data
  5. A Kalman Filter Based Technique for Stator Turn-Fault Detection of the Induction Motors
  6. Multi-objective Design Method for Hybrid Active Power Filter
  7. A Study on Effect of Concrete Foundations on Resistance and Surface Potentials of Gas Insulated Substation Grounding Systems
  8. Access Selection Algorithm of Heterogeneous Wireless Networks for Smart Distribution Grid Based on Entropy-Weight and Rough Set
  9. Voltage Sag due to Pollution Induced Flashover Across Ceramic Insulator Strings
  10. Study on a Novel Bearing Fault Diagnosis Method from Frequency and Energy Perspective
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