Abstract
Distribution systems continue to grow and becoming more complex with increasing operational challenges such as protection miscoordination. Initially, conventional methods were favoured to solve overcurrent relay coordination problems; however, the implementation of these methods is 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 paper presents a comprehensive sensitivity analysis to evaluate the effect of the discrete control parameters on particle swarm optimizer and genetic algorithms performance, alternatively on the behaviour of overcurrent relays. Optimization algorithms aim to minimise overcurrent relay time multiplier settings and accomplish optimal protection coordination. 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 30% crossover, 2% mutation, and smaller population size yield faster convergence rate and optimise fitness function, which improves genetic algorithms performance. Sensitivity analysis results are verified by comparing the performance of particle swarm optimization with the genetic algorithms which show the former parameter setting outperforms the latter. The relay operational speed is reduced by 15% for particle swarm optimization and system selectivity is maximised. The optimal protection coordination achieved using particle swarm optimization showed superiority of the algorithm, its ability to circumvent premature convergence, consistency, and efficiency.
Highlights
Due to rising emphasis on substation automation, SCADA, and monitoring control [1], operational speed and protection coordination form the most important aspect and are prime factors in any protection system [2], [3]
In order to gain insight into overcurrent relay behaviour while easing sensitivity analysis efforts, the evaluation technique for comparing algorithms is as follows: a) The algorithm that succeeded in obtaining the best fitness value is preferred, whereas any algorithm that yields poor performance due to premature convergence is not further considered
The objective of this paper was to study the effects of particle swarm optimization and genetic algorithms control parameters on overcurrent relay sensitivity and speed
Summary
Due to rising emphasis on substation automation, SCADA, and monitoring control [1], operational speed and protection coordination form the most important aspect and are prime factors in any protection system [2], [3]. Protection miscoordination may occur due to poor overcurrent relay settings [5], [6] Operational challenges such as a greater percentage of power network equipment damage and customer service disruptions caused by breakdowns and faults in the distribution feeders as overhead power systems are subjected to either partial or permanent faults [6]. Protection coordination is of paramount importance since the failure of protective devices to operate under faulty conditions can damage some essential parts due to fire that may result from massive-short circuits; the system loses synchronism of the machinery and equipment [10], [11] This necessitates the need to optimize overcurrent relay operating time and maximize selectivity [12]
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