This paper presents the optimization of fuel cost, emission of NO_X, CO_X, and SO_X gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.

This paper presents a 2-D analytical method to calculate the back EMF of the axial flux permanent magnet synchronous generator (AFPMSG) with coreless stator and dual rotor having magnets mounted on both sides of rotor yoke. Furthermore, in order to reduce the no load voltage total harmonics distortion (VTHD), the initial model of the coreless AFPMSG is optimized by using a developed analytical method. Optimization using the 2-D analytical method reduces the optimization time to less than a minute. The back EMF obtained by using the 2-D analytical method is verified by a time stepped 3-D finite element analysis (FEA) for both the initial and optimized model. Finally, the VTHD, output torque and torque ripples of both the initial and optimized models are compared with 3D-FEA. The result shows that the optimized model reduces the VTHD and torque ripples as compared to the initial model. Furthermore, the result also shows that output torque increases as the result of the optimization.