Research on the dynamic networking of smart meters based on characteristics of the collected data

In order to accurately collect the electricity usage information from the smart meter which uses the powerline for communication, this paper proposes the method of dynamic networking to enhance the reliability of the smartmeter communication. We shall firstly establish a logical topology between the smart meter and the concentrator withreference to their communication paths within the power supply range of the same transformer, and then grade smartmeters, and choose the relay for each level network based on the selection methods of relay, and finally use the improvedant colony algorithm to choose the optimal communication path for smart meters in the communicref1ref1ation range ofmultiple relays. Although the optimal path can be discovered quickly in this way, it cannot establish other communicationpaths in time when the collected data are abnormal. Therefore, on the basis of the characteristics of data, this paperuses the probabilistic neural network to determine the integrity of the data collected. If the collected data is abnormal,we will reconstruct communication network by choosing a new communication path between concentrator and smartmeters. By means of the MATLAB simulation, we can automatically organize the network to improve the reliability ofcommunication between the concentrator and smart meters, which is of great significance for the concentrator to collectthe users’ electricity usage information.

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[1] Himeur Y, Boukabou A. An efficient impulsive noise cancellation scheme for power-line communication systems using ANFIS and chaotic interleaver. Digital Signal Processing 2017; 66: 42-55.
[2] Qi J, Chen X, Liu X. Advances of research on low-voltage power line carrier communication technology. Power System Technology 2010; 34: 161-172 (in Chinese).
[3] Heo J, Lee K, Kang HK, Kim DS, Kwon WH. Adaptive channel state routing for home network systems using power line communications. IEEE Transactions on Consumer Electronics 2007; 53: 1410-1418.
[4] Gao Q, Yu JY, Chong PHJ, So PL, Gunawan E. Solutions for the Silent node problem in an automatic meter reading system using power-line communications. IEEE Transactions on Power Delivery 2008; 23: 150-156.
[5] Xiaosheng L, Liang Z, Yan Z, Dianguo X. Performance analysis of power line communication network model based on spider web. In: IEEE 2011 Power Electronics-ECCE Asia: ”Green World with Power Electronics” Conference, Jeju, Korea. New York, NY, USA: IEEE; 2002. pp. 953-959.
[6] Xiaosheng L, Liang Z, Dianguo X. Networking algorithm and communication protocol of novel power line communication based on cobweb. Power System Protection and Control 2012; 40: 27-33 (in Chinese with an English abstract).
[7] Zhang L, Liu X, Qi J, Zhou Y, Xu D. Study of improved hierarchical ant colony routing algorithm for low-voltage power line communication. Transactions of China Electrotechnical Society 2014; 29: 318-324 (in Chinese).
[8] Chu CH, Gu J, Hou XD, Gu Q. A heuristic ant algorithm for solving QoS multicast routing problem. In: IEEE 2002 Evolutionary Computation Congress, Honolulu, HI, USA. New York, NY, USA: IEEE; 2002. pp. 1630-1635.
[9] Stützle T, Dorigo M. A short convergence proof for a class of ant colony optimization algorithms. IEEE Transactions on Evolutionary Computation 2002; 6: 358-365.
[10] Vasudevan C, Ganesan L. Case-based path planning for autonomous underwater vehicles. Autonomous Robots 1996; 3: 79-89.
[11] Warren CW. A technique for autonomous underwater vehicle route planning. In: IEEE 1990 Autonomous Underwater Vehicle Technology Symposium. Washington, DC, USA. New York, NY, USA: IEEE; 1990 pp. 201-205.
[12] Bonabeau E, Dorigo M, Theraulaz G. Inspiration for optimization from social insect behaviour. Nature 2000; 406: 39-42.
[13] Jackson DE, Holcombe M, Ratnieks FLW. Trail geometry gives polarity to ant foraging networks. Nature 2004; 432: 907-909.
[14] Trentin E, Lusnig L, Cavalli F. Parzen neural networks: Fundamentals, properties, and an application to forensic anthropology. Neural Networks 2018; 97: 137-151.
[15] Swain RR, Khilar PM, Bhoi SK. Heterogeneous fault diagnosis for wireless sensor networks. Ad Hoc Networks 2018; 69: 15-37.
[16] Hajmeer M, Basheer I. A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. Journal of Microbiological Methods 2002; 51: 217-226.
[17] BenAli J, Saidi L, Mouelhi A, Chebel-Morello B, Fnaiech F. Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations. Engineering Applications of Artificial Intelligence 2015; 42: 67-81.
[18] Teles LO, Fernandes M, Amorim J, Vasconcelos V. Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM). Aquatic Toxicology 2015; 165: 241-248.
[19] Barsch R, Jahn H, Lambrecht J, Schmuck F. Test methods for polymeric insulating materials for outdoor HV insulation. IEEE Transactuions on Dielectrics and Electrical Insulation 1999; 6: 668-675.
[20] Ozgonenel O, Yalcin T. A complete motor protection algorithm based on PCA and ann: A real time study. Turkish Journal of Electrical Engineering and Computer Sciences 2011; 19: 317-334.