https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:11601 k, where k is the number of switches. Because of the exponential complexity, exact methods are prohibitively time-consuming. This work presents a genetic algorithm that provides a rapid answer to network managers in terms of a switching strategy to reconnect the affected area. The method takes into account the radial topology of the power flow and the operational limits of voltage and cable load. Computational tests were conducted on a real network with 96 buses and 16 switches, located within the operational area of Energy Australia. This paper describes the genetic algorithm in detail, presents thorough computational tests, and a complete contingency plan for the test network.]]> Wed 11 Apr 2018 15:01:42 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:1648 Wed 11 Apr 2018 09:51:42 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:19055 Sat 24 Mar 2018 08:05:18 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:29509 Sat 24 Mar 2018 07:29:42 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28208 Sat 24 Mar 2018 07:23:50 AEDT ]]>