Coordination of Directional Overcurrent Relay Using PSO-DE Algorithm

Tresnadi, Handy Agung; Nugroho, Teguh Aryo; Abdillah, Muhammad; Pertiwi, Nita Indriani

doi:10.1109/ict-pep53949.2021.9601118

Cited by 2 publications

(3 citation statements)

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“…A number of PSO algorithm variations have been introduced either by incorporating theories of PSO algorithm with other meta-heuristic techniques or by developing novel mechanisms. In [48], PSO was coupled with differential evolution (DE) for constraint handling by using feasibility principles to obtain best personal solution of individual particles in the swarm. Furthermore, a study in [15] introduced the weight expression into velocity update equation to improve search capability of particles by stabilising search abilities.…”

Section: A Particle Swarm Optimizationmentioning

confidence: 99%

“…In CVI-PSO, constraints are handled by attracting problem solutions to the feasible search regions through interval arithmetic that normalises overall violations and the subsequent objective function is executed by means of basic lexicographic approach. PSO -DE [48] In an attempt to minimise premature convergence in the original PSO, differential evolution (DE) was introduced to handle constraints through the utilisation of feasibility principles to best individual solutions of particles in the region. APSO [52] An accelerated PSO was introduced to accelerate algorithm convergence to transverse into global best position by velocity vector.…”

Section: -Pso [51]mentioning

confidence: 99%

“…The literature highlights PSO algorithm premature convergence and other factors contributing to unsatisfactory behaviour of the algorithm [42] - [45], other authors [48] - [60] introduce variants to optimise the performance of PSO. However, setting particle swarm optimizer parameters to yield optimal overcurrent relay settings is a long-standing problem.…”

Section: -Pso [51]mentioning

confidence: 99%

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

Langazane,

Saha

2024

IEEE Access

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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.

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Section: A Particle Swarm Optimizationmentioning

confidence: 99%

Section: -Pso [51]mentioning

confidence: 99%