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Optimal design of proportional-integral controllers for stand-alone solid oxide fuel cell power plant using differential evolution algorithm.

Ahmed A, Ullah MS - Springerplus (2016)

Bottom Line: To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm.Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations.The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.

View Article: PubMed Central - PubMed

Affiliation: EEE Department, Islamic University of Technology, Boardbazar, Gazipur Bangladesh.

ABSTRACT
This paper proposes the application of differential evolution (DE) algorithm for the optimal tuning of proportional-integral (PI) controller designed to improve the small signal dynamic response of a stand-alone solid oxide fuel cell (SOFC) system. The small signal model of the study system is derived and considered for the controller design as the target here is to track small variations in SOFC load current. Two PI controllers are incorporated in the feedback loops of hydrogen and oxygen partial pressures with an aim to improve the small signal dynamic responses. The controller design problem is formulated as the minimization of an eigenvalue based objective function where the target is to find out the optimal gains of the PI controllers in such a way that the discrepancy of the obtained and desired eigenvalues are minimized. Eigenvalue and time domain simulations are presented for both open-loop and closed loop systems. To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm. Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations. Moreover, non-parametric statistical analyses, namely, one sample Kolmogorov-Smirnov (KS) test and paired sample t test are used to identify the statistical advantage of one optimizer over the other for the problem under study. The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.

No MeSH data available.


Related in: MedlinePlus

Response of hydrogen partial pressure for pulse disturbance
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Fig11: Response of hydrogen partial pressure for pulse disturbance

Mentions: The performance of DE and IWO for different types of load variations are compared and the corresponding time domain simulations are presented in Figs. 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18. As the optimized parameters obtained by PSO are almost identical to DE and the best fitness value reached by these two are same, the time domain responses are completely matched. So, the time domain responses of PSO are not included in this part. Figure 7 present the hydrogen partial pressure responses for step load change. The first undershoot and overshoot obtained by DE are found to be better than those obtained by IWO. Figure 8a presents the response of oxygen partial pressure for the step change in load. As the difference is not too vivid from this figure, a zoomed view of undershoot is given in Fig. 8b where the superiority of DE over IWO is clearly visible. The control inputs required for the step load change are presented in Figs. 9 and 10. The control efforts required by DE are found to have less overshoot/undershoot while the corresponding settling times are found almost identical.Fig. 7


Optimal design of proportional-integral controllers for stand-alone solid oxide fuel cell power plant using differential evolution algorithm.

Ahmed A, Ullah MS - Springerplus (2016)

Response of hydrogen partial pressure for pulse disturbance
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4814395&req=5

Fig11: Response of hydrogen partial pressure for pulse disturbance
Mentions: The performance of DE and IWO for different types of load variations are compared and the corresponding time domain simulations are presented in Figs. 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18. As the optimized parameters obtained by PSO are almost identical to DE and the best fitness value reached by these two are same, the time domain responses are completely matched. So, the time domain responses of PSO are not included in this part. Figure 7 present the hydrogen partial pressure responses for step load change. The first undershoot and overshoot obtained by DE are found to be better than those obtained by IWO. Figure 8a presents the response of oxygen partial pressure for the step change in load. As the difference is not too vivid from this figure, a zoomed view of undershoot is given in Fig. 8b where the superiority of DE over IWO is clearly visible. The control inputs required for the step load change are presented in Figs. 9 and 10. The control efforts required by DE are found to have less overshoot/undershoot while the corresponding settling times are found almost identical.Fig. 7

Bottom Line: To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm.Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations.The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.

View Article: PubMed Central - PubMed

Affiliation: EEE Department, Islamic University of Technology, Boardbazar, Gazipur Bangladesh.

ABSTRACT
This paper proposes the application of differential evolution (DE) algorithm for the optimal tuning of proportional-integral (PI) controller designed to improve the small signal dynamic response of a stand-alone solid oxide fuel cell (SOFC) system. The small signal model of the study system is derived and considered for the controller design as the target here is to track small variations in SOFC load current. Two PI controllers are incorporated in the feedback loops of hydrogen and oxygen partial pressures with an aim to improve the small signal dynamic responses. The controller design problem is formulated as the minimization of an eigenvalue based objective function where the target is to find out the optimal gains of the PI controllers in such a way that the discrepancy of the obtained and desired eigenvalues are minimized. Eigenvalue and time domain simulations are presented for both open-loop and closed loop systems. To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm. Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations. Moreover, non-parametric statistical analyses, namely, one sample Kolmogorov-Smirnov (KS) test and paired sample t test are used to identify the statistical advantage of one optimizer over the other for the problem under study. The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.

No MeSH data available.


Related in: MedlinePlus