Improved Genetic Algorithms (IGA) for Optimal Reactive Power Planning in Loss Minimisation Scheme

The increasing demand of electrical power has caused the system network to be in stressed condition which led to transmission loss in the system. Consequently, voltage profile of the system decays accordingly leading to unpredictable voltage instability phenomenon. The issue of loss minimisation has been progressively investigated in the power system environment. Many techniques have been developed in the past in order to minimise transmission loss towards the improvement of voltage profile. This paper presents the application of Improved Genetic Algorithms (IGA) in order to optimise the reactive power planning (RPP) for loss minimisation in power system. In this study, an IGA engine is developed in order to implement the optimisation for the combination of reactive power dispatch and transformer tap changer setting. Two selection techniques namely the anchor spin and population spin were explored in order to investigate the merit of each technique. The selection technique is a variant of the conventional spin technique; whereby, the spin is conducted on every population of variables for inducing further search space exploration. Elitism technique was also employed in the selection and production process. The proposed IGA technique is tested on the IEEE Reliability Test System (IEEE-RTS), and revealed that the technique has minimised the total loss in power system. Comparative studies on the results obtained from the two selection techniques were performed and results indicated that the population spin performed better in terms of accuracy and iteration number. The proposed IGA technique can be further explored for solving other optimisation problems in power system. Key-Words: Improved Genetic Algorithm, Optimisation, Reactive power planning, Reactive power dispatch and transformer tap changer setting, Transmission loss minimisation, Voltage profile.