A Comparative Review of Current Optimization Algorithms for Maximizing Overcurrent Relay Selectivity and Speed

An exponential growth and complexity in diverse distribution systems have contributed to protection coordination challenges. Initially, protection coordination schemes were achieved by means of conventional techniques; however, the utilisation of such methods is based on trial-and-error principles and laborious. Consequently, current studies have adopted the utilisation of particle swarm optimization, artificial intelligence models, and genetic algorithms to optimise overcurrent relay selectivity and operational speed. Particle swarm optimization, artificial intelligence, and genetic algorithms are optimization techniques that at times converges prematurely due to poor selection of control parameters and lack of optimal values, which results in increased computational time. Therefore, this paper presents a comprehensive review of recent developments in terms of parametric sensitivity analysis, selection of artificial intelligence models based on data availability, and the likelihood of solving overcurrent relay coordination problems. The reviewed literature shows that particle swarm optimization performance is greatly influenced by inertia weight and swarm size, while the number of iterations has insignificant effect. The findings also indicate that crossover rate, mutation probability, and population size affect genetic algorithms behaviour. Artificial intelligence models lack sensitivity study for parametric tuning, that is, number of hidden layers, membership functions, epsilon in support vector machine, and number of fuzzy rules affects the models’ performance.