Open AccessEnhanced evolutionary heuristic approaches for remote metering smart grid networks
Author(s) -
Miao Hui,
Chen Guo,
Dong Zhaoyang
Publication year - 2016
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/iet-net.2016.0003
Subject(s) - smart grid , computer science , heuristic , cluster analysis , smart meter , distributed computing , evolutionary algorithm , reliability (semiconductor) , particle swarm optimization , grid , metering mode , machine learning , artificial intelligence , engineering , mathematics , mechanical engineering , power (physics) , physics , geometry , quantum mechanics , electrical engineering
For a smart grid network, the communication infrastructure is an important part to provide the stability and reliability. To build an efficient communication network is crucial for the success of a smart grid network. This study improves the previous published work by proposing two novel evolutionary heuristic approaches (discrete differential evolutionary clustering approach and particle swarm optimisation clustering approach) to obtain the minimum number and location of the powerful nodes to fulfil the connectivity requirement of a smart meters network. The contribution of this study includes the designing and implementing of the two novel evolutionary heuristic approaches which could significantly improve the optimal solution [the least number of the local data centres (powerful nodes)] for a smart meter network which has arbitrary number of smart meters (ordinary nodes) with arbitrary transmission range. Comprehensive case studies and numerical statistical analysis demonstrate that the approaches could efficiently obtain better optimal results, and the connectivity of the smart meter network could be fulfilled with minimum number of the powerful nodes.