Particle swarm optimization applied to reactive power dispatch considering renewable generation

Integrated neuro-swarm heuristic with interior-point for nonlinear SITR model for dynamics of novel COVID-19

“…The execution procedure of PSO is simple as well as easy to implement due to lessor requirements of the memory [26] . Recently, PSO is applied in nonlinear electric circuits [27] , pitch control system of wind turbine [28] , parameter approximation [29] , reactive power dispatch generation [30] , benchmark optimization models [31] , tune an adaptive PID controller [32] and approximation of undrained shear soil strength [33] .…”

Improvement of voltage profile for large scale power system using soft computing approach

“…The second optimization approach employed in this paper is particle swarm optimization (PSO) proposed by Kennedy and Eberhart in 1995 [10] where this algorithm mimics the behavior of bird flocking. This algorithm is a population-based search approach where each individual in a population is presented as a particle.…”

“…This algorithm is a population-based search approach where each individual in a population is presented as a particle. Each particle in a swarm flies around in a multi-dimensional search space by memorizing its own experience and the experience of neighboring particles [10]. Furthermore, the flowchart of PSO is depicted in Figure 1.…”

“…Second categories is physically inspired such as simulated annealing algorithm, and lastly social inspired such as imperialist competitive algorithm, and tabu search algorithm [5]. There are a number of researches that utilizing soft computing based on biological inspired approaches to solve optimization problems such as genetic algorithm (GA) to determine the optimal generator output [6], ant colony optimization (ACO) for reactive power management [7], differential evolution (DE) algorithm to solve non-convex and high non-linear problems [8,9], particle swarm optimization (PSO) for reactive power dispatch [10], biogeography based optimization (BBO) for optimal VAR control [11], harmony search algorithm (HSA) for reactive dispatch [12], hybrid tabu search algorithm (TS) and simulated annealing algorithm (SA) for optimal reactive power problem [13], teaching learning based optimization algorithm (TLBO) for reactive power planning [14], group search optimization (GSO) for power and emission dispatch [15], honey bee mating optimization (HBMO) for power loss minimization [16], gravitational search algorithm (GSA) to determine the optimal FACTS for reactive power planning [17], artificial bee colony algorithm (ABC) for reactive power flow [18], cuckoo optimization algorithm (COA) [19] and artificial immune system (AIS) [20] for distribution network reconfiguration problem.…”