Saha, Akshay Kumar.Langazane, Sethembiso Nonjabulo.2022-08-292022-08-2920212021https://researchspace.ukzn.ac.za/handle/10413/20779Masters Degree. University of KwaZulu- Natal, Durban.Distribution systems continues to grow and becoming more complex with increasing operational challenges such as protection miscoordination. Initially, conventional methods were favoured to optimize protection coordination; however, the implementation process is laborious and time-consuming. “Therefore, recent studies have adopted the utilisation of particle swarm optimization and genetic algorithms to solve overcurrent relay coordination problems and maximise system selectivity and operational speed. Particle swarm optimization and genetic algorithms are evolutionary algorithms that at times suffer from premature convergence due to poor selection of control parameters. Consequently, this thesis aims to present a comprehensive sensitivity analysis to evaluate the effect of the discrete control parameters on the performance of particle swarm optimizer and genetic algorithms, alternatively on the behaviour of overcurrent relays. The main objectives of this research work also include modelling and simulation of distribution system protection scheme, employment of evolutionary algorithms with control parameters that perform efficiently and effectively to maximise protection coordination between relays, optimize relay operating time and maintain the stipulate coordination time interval, and lastly, to outline future recommendations. The distribution network understudy was modelled and simulated on a real-time digital simulator to validate protection settings, and the verification of evolutionary algorithms performance was displayed on Matlab/Simulink. An extensive parametric sensitivity analysis was conducted to understand the impact of the individual control parameters and their respective influence on the performance of evolutionary algorithms. The findings indicate that particle swarm optimization is more sensitive to inertia weight and swarm size while the number of iterations has minimal effect. The results also depict that genetic algorithms’ performance is mostly influenced by crossover probability, mutation probability, and population size. Sensitivity analysis results were verified by comparing the performance of particle swarm optimizer with genetic algorithms, which demonstrated that particle swarm optimization performs efficiently and robustly in solving the considered problem, especially in terms of convergence speed. Furthermore, overcurrent relays were more sensitive, selective, and the operational speed was reduced for particle swarm optimizer compared to other algorithms. The optimal protection coordination achieved using particle swarm optimization showed superiority of the algorithm, its ability to circumvent premature convergence, consistency, and” efficiency.enPremature convergence.Evolutionary algorithms.Swarm optimization.Digital simulator.Control parameters.Thesis