Fig 1.
The figure represents curve of fuel-cost for non-convex machine.
Table 1.
Parameter setting/values using GA and SQP.
Fig 2.
Soft computing optimizer framework.
It represents three sections of flowcharts. The first flow chart describes the representation of dynamic EDP, its modeling and optimization which provides optimal generation of units. The second flow chart of Fig 2. elaborates GA based global optimizer, toll initialization, the evaluation of fitness value and finally getting the best chromosomes by GA. The third flow chart of Fig 2. represents the program initialization, toll initialization of assigning optimum parameters, SQP algorithm and then saving final weights and the execution time.
Table 2.
Three unit system for Case Study-I.
Table 3.
Optimal generations, cost, and time for Case Study-I.
Fig 3.
Optimizer response against three non-convex machine.
A. Power output B. Fitness level.
Fig 4.
Optimizer response against three convex machines.
A. Power Output B. Fitness Level.
Table 4.
Three unit system for Case Study-II.
Table 5.
Optimal generations, cost, and time for Case Study-II.
Table 6.
Six unit system for Case Study-III.
Table 7.
Optimal generations, cost, and time for Case Study-III.
Fig 5.
Optimizer power output response against six convex machines.
Fig 6.
Optimizer fitness level response against six convex machines.
Table 8.
Six units system for Case Study-IV.
Table 9.
Optimal generations, cost and time for Case Study-IV.
Fig 7.
Optimizer power output response against six non-convex machines.
Fig 8.
Optimizer fitness level response against six non-convex machines.
Table 10.
Eleven unit system for Case Study-V.
Table 11.
Optimal generations, cost, and time for Case Study-V.
Fig 9.
Optimizer power output response against eleven convex machines.
Fig 10.
Optimizer fitness level response against eleven convex machines.
Fig 11.
Optimizer power output response against twelve convex machines.
Fig 12.
Optimizer fitness level response against twelve convex machine.
Table 12.
Twelve unit for Case Study-VI.
Table 13.
Optimal generations, cost, and time for Case Study-VI.
Fig 13.
Optimizer power output response against thirteen non-convex machines.
Fig 14.
Optimizer fitness level response against thirteen non-convex machines.
Table 14.
Optimal generations, cost, and time for Case Study-VII.
Table 15.
Optimal generations, cost, and time for Case Study-VIII.
Fig 15.
Optimizer power output response against fifteen convex machines.
Fig 16.
Optimizer fitness level response against fifteen convex machines.
Fig 17.
Optimizer power output response against twenty convex machines.
Fig 18.
Optimize fitness level response against twenty convex machines.
Table 16.
Optimal generations, cost, and time for Case Study-IX.
Table 17.
Optimal generations, cost, and time for Case Study-X.
Fig 19.
Optimizer power output response against forty non-convex machines.
Fig 20.
Optimizer fitness level response against forty non-convex machines.
Table 18.
Statistical analysis of different approaches.