The deployment of photovoltaic single-phase inverters has been rapidly increasing worldwide. However, the performance of these systems is highly influenced by atmospheric conditions and load variations, necessitating ...
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The deployment of photovoltaic single-phase inverters has been rapidly increasing worldwide. However, the performance of these systems is highly influenced by atmospheric conditions and load variations, necessitating the development of performance indices to enhance their efficiency and energy quality. In this study, four performance indices are proposed to evaluate the efficiency and energy quality of photovoltaic systems quantitatively. The entire process is analyzed, encompassing solar energy capture, DC-DC and DC-AC conversion, and filtering, to deliver maximum energy and quality to the load. Furthermore, eight system parameters are optimized using advanced techniques such as genetic algorithms, particle swarm optimization, and gray wolf optimization. These optimizations enhance the global performance of two critical stages: (1) the maximum power point tracking algorithm based on sliding mode control, which minimizes switching losses in the boost stage, and (2) the effective transfer of captured solar power to the load by optimizing the gains of a PI controller. The PI controller computes the switching triggers for the inverter stage, significantly improving the total harmonic distortion of voltage and current waveforms. Simulation results validate the proposed approach, demonstrating a marked improvement in overall system efficiency (95.8%) when compared to the incremental conductance method (-11.8%) and a baseline sliding mode control configuration (-1.14%).
The increment of autonomous systems has stimulated the research of new controller tuning techniques to face the unpredictable disturbances and parametric uncertainties inherent in any autonomous system that affect its...
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The increment of autonomous systems has stimulated the research of new controller tuning techniques to face the unpredictable disturbances and parametric uncertainties inherent in any autonomous system that affect its performance. The indirect adaptive controller tuning approach based on the general dynamic model (IACTAGDM) and bioinspiredoptimization is one of the most successful elections facing parametric uncertainties and disturbances, which are intricate to handle by other controller tuning techniques. However, this controller tuning approach is limited by the complexity of the dynamic model due to the computational burden, restricting its application to relatively small systems or systems with slow responses where the tuning is updated at large time intervals. The present work proposes a novel surrogate indirect adaptive controller tuning approach based on the response surface method (SIACTA-RSM) to address computational burden limitations. The proposal is tested on the speed regulation controller of a brushless direct current motor, with the aim of reducing the speed regulation error and the control system's power consumption. The closed -loop system performance and the required computational time obtained by the proposed SIACTA-RSM are compared to the ones of a well -established IACTA-GDM. The descriptive and inferential statistics, as well as graphical comparisons, show that the system performance obtained by the SIACTA-RSM proposal is as competitive as the IACTA-GDM approach, keeping a mean difference among the results by up to 3.18% while reducing the computational burden of IACTA-GDM by up to 90%. These outcomes show that the SIACTA-RSM proposal is a reliable alternative to overcome the computational burden limitations that affect the IACTA-GDM approach while maintaining competitive performance.
Numerical simulation of proton-exchange membrane fuel cells (PEMFCs) requires an adequate model and precise parameters for reproducing their operational performance quantified by the polarization curve. bioinspired al...
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Numerical simulation of proton-exchange membrane fuel cells (PEMFCs) requires an adequate model and precise parameters for reproducing their operational performance quantified by the polarization curve. bioinspiredalgorithms are well-suited for optimization. The simulator is stressed by inputting thousands of randomly generated parameters, and hence, a robust numerical model is required. Once the proper model and parameters reproduce the experimental data, they can be used for design improvement. This article proposes a reformulation of a macrohomogeneous mathematical model to provide higher numerical stability to the solutions. We introduce optimization problems for parameter estimation and design optimization by applying three bioinspiredalgorithms to maximize its performance and minimize the platinum mass loading m(Pt). The results are validated by comparing the experimental polarization curves with those simulated from the estimated parameters. We compare a base design's performance with the optimized design for maximum performance. We also compare a base design with the optimized design for minimum m(Pt). The results show that the particle swarm optimization requires the lowest computational cost and performs the best in most cases, fitting the experimental data with errors lesser than 10(-17). The minimization of m(Pt) reduces the amount by 42% compared to the base case.
In the past few years, bio-inspired optimizationalgorithms have shown to be an excellent way to solve a wide range of complex computing problems in science and engineering. This paper compares bio-inspired algorithms...
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ISBN:
(数字)9783031297830
ISBN:
(纸本)9783031297823;9783031297830
In the past few years, bio-inspired optimizationalgorithms have shown to be an excellent way to solve a wide range of complex computing problems in science and engineering. This paper compares bio-inspired algorithms to better understand and measure how well they find the best tuning parameters for a Dynamic Sliding Mode Control for integrating systems with an inverse response and dead time. The comparison includes four bioinspiredalgorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms. It shows how they can improve the performance of the controller by looking for the best tuning parameter solutions. The parameters of each algorithm affect the searching mechanism in different ways, and these effects were tested in two simulated systems. Ant colony optimization is much better than other algorithms at finding the best answers to our problems.
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