TY - JOUR
T1 - Steady state analysis of modern industrial variable speed drive systems using controllers adjusted via grey wolf algorithm & particle swarm optimization
AU - Nadweh, Safwan
AU - Khaddam, Ola
AU - Hayeh, Ghassan
AU - Atieh, Bassan
AU - Haes Alhelou, Hassan
N1 - Publisher Copyright:
© 2020 The Author(s)
PY - 2020/11
Y1 - 2020/11
N2 - This paper presents the reconfiguration of control circuit designed to control four-quadrant chopper placed in the variable speed drive system (VSDS)'s DC-link. The purpose of this design is to reduce the overall total harmonic distortion THD% of input current, and the ripple factor (RF) of the DC-link current in this system. Both of Grey Wolf Algorithm (GWO) & Particle Swarm Optimization (PSO) have been used to get the optimal parameters of proportional integral PI and proportional integral differential with filter PIDN controllers. The variable speed drive system and the proposed filter have been modeled in integration with the suggested algorithms to determine the optimal values of the controllers' parameters. The grey wolf algorithm GWO outperformed the PSO algorithm in term of reaching the optimum parameters in less number of iterations in both dynamic and static work conditions. Also, the time response of the system with GWO is better than with PSO.
AB - This paper presents the reconfiguration of control circuit designed to control four-quadrant chopper placed in the variable speed drive system (VSDS)'s DC-link. The purpose of this design is to reduce the overall total harmonic distortion THD% of input current, and the ripple factor (RF) of the DC-link current in this system. Both of Grey Wolf Algorithm (GWO) & Particle Swarm Optimization (PSO) have been used to get the optimal parameters of proportional integral PI and proportional integral differential with filter PIDN controllers. The variable speed drive system and the proposed filter have been modeled in integration with the suggested algorithms to determine the optimal values of the controllers' parameters. The grey wolf algorithm GWO outperformed the PSO algorithm in term of reaching the optimum parameters in less number of iterations in both dynamic and static work conditions. Also, the time response of the system with GWO is better than with PSO.
KW - Electrical engineering
KW - Electrical system
KW - Energy
KW - Four quadrant chopper
KW - Grey wolf optimization
KW - Industrial engineering
KW - Particle swarm optimization
KW - Power quality
KW - Variable speed drive system
UR - https://www.scopus.com/pages/publications/85095426999
U2 - 10.1016/j.heliyon.2020.e05438
DO - 10.1016/j.heliyon.2020.e05438
M3 - Article
C2 - 33204887
AN - SCOPUS:85095426999
SN - 2405-8440
VL - 6
JO - Heliyon
JF - Heliyon
IS - 11
M1 - e05438
ER -