Fig 1.
Configuration diagram of power generating unit.
Fig 2.
State changeover of the power generation processing system.
Fig 3.
Flowchart of genetic algorithm.
Fig 4.
Flowchart of particle swarm optimization algorithm.
Table 1.
Effect of population size on availability of power generation processing unit by using Genetic Algorithm (Evolution = 200, Mutation = 0.55, Crossover = 0.62).
Table 2.
Effect of evolution on availability of power generation processing unit by using Genetic Algorithm (Population size = 60, Mutation = 0.55, Crossover = 0.62).
Table 3.
Effect of crossover on availability of power generation processing unit by using Genetic Algorithm (Population size = 60, Evolution = 200, Mutation = 0.55).
Table 4.
Effect of mutation on availability of power generation processing unit by using Genetic Algorithm (Population size = 60, Evolution = 200, Crossover = 0.62).
Fig 5.
No. of generation vs. availability.
Table 5.
Steady-state availability with respect to the number of generations by using Particle Swarm Optimization having a population size of 15, inertia weight 1, damping ratio 0.95, p-best 1.7 and g-best 2.3.
Table 6.
Steady-state availability with respect to the number of iterations by using Particle Swarm Optimization having population size 15, inertia weight 1, damping ratio 0.95, p-best 1.7 and g-best 2.3.
Table 7.
Steady-state availability with respect to weight damping ratio by using Particle Swarm Optimization having maximum iterations 25, population size 15, inertia weight 1, damping ratio 0.95, p-best 1.7 and g-best 2.3.
Fig 6.
No. of iterations vs. availability.
Fig 7.
Damping ratio vs. availability.
Fig 8.
Convergence of availability using GA.
Fig 9.
Convergence of availability using PSO.