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Optimal placement of shunt capacitor banks on a sub-transmission network.

The optimal capacitor placement problem is the determination of the optimal location of the
shunt capacitors on the sub-transmission networks such that energy losses are minimised, the
power factor and the network voltage profile are improved. During this period when Eskom is
experiencing an unacceptably low generation reserve margin, it’s quite critical that the electrical
Transmission and Distribution network losses be kept to a minimum to optimise on the scarce
generation that is available to supply South Africa’s current and future power demand.
One of the ways of minimising technical losses is through the optimal placement or installation
of capacitor banks on the network. The placement of shunt capacitors on a bulk Transmission
network is essentially to improve the voltage profile on the network, increase system security
and reduce transmission losses. The optimal placement of shunt capacitors with the above
objectives would assist in minimising the cost of the investment whilst maximising the return on
investment to the utility. This research subject is treated as an optimization problem and hence
optimization solutions were considered to address the “Optimal capacitor placement problem”.
This optimisation problem is solved for all loading levels i.e. peak, standard and off peak
periods and for different seasons in a given typical year.
This thesis investigates the capability of Genetic Algorithms technique in solving this
optimisation problem. Genetic algorithms utilize a guided search principle to develop a robust
solution to this research problem. Given their capability to traverse the complicated search
space with a multivariate objective function, Genetic Algorithm are versatile and robust to
locate the global optimum of the objective function. These Genetic Algorithms (GA’s) were
implemented on real sub transmission networks modelled on DigSilent/ Powerfactory. The
modelled GA’s on DigSilent were then tested on different network types i.e. commercial,
mining, residential and industrial load mixes. The solutions determined by the different GA’s
are then compared in terms of time taken to locate the solution, reliability and robustness. The
most reliable GA is then identified and recommended as the preferred optimisation approach. A
methodology of using GA’s to solve the above mentioned problem is therefore proposed / Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2009.

Date January 2009
ContributorsMbuli, M.
Source SetsSouth African National ETD Portal
Detected LanguageEnglish

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