Abstract
This paper describes a robust feedback technique involving novel fixed-time-convergent sliding mode technology (NFTCSMT) using improved quantum particle swarm optimization (QPSO) to obtain high-performance renewable energy inverters. Customary SMT encounters long time convergence towards the origin and the influence of the dithering. The NFTCSMT can rapidly impel system-following movement to approach the sliding manifold and effectively accelerate the convergence speed to equilibrium states. However, the NFTCSMT cannot easily select the global optimum of the controller parameters subject to large parameter changes and nonlinear interventions, leading to the dither phenomenon/steady-state error still being caused. The dither inflicts decreased control accuracy, high voltage harmonics, major harm in relation to switching components, and great thermal losses in power electronic converters. The improved QPSO including the unique property of a random compression/expansion factor is used to find optimal parameters of the NFTCSMT in practical applications, for the reason that it importantly mitigates the dither and amends convergent speed as well as guaranteeing global convergence. The presented alliance amid NFTCSMT and improved QPSO achieves faster response time and singularityless, and also yields high-accuracy tracking and dither attenuation. The robust stability using Lyapunov theorem of the suggested system has provided precise mathematical derivations. Simulations show that the suggested controller offers less than 0.1% voltage THD (total harmonic distortion) which exceeds IEEE standard 519 under heavily distorted rectifier loads, and less than 10% voltage dip which surpasses IEEE standard 1159 during step load transients. Experimental tests of an algorithmically controlled laboratory prototype (1 kW, 110 Vrms/60 Hz) of a renewable energy inverter (REI) based on digital signal processing manifest less than 0.05% voltage THD in the face of great inductor-capacitor alterations, and less than 10% voltage dip in the face of transient load scenarios.
Highlights
Renewable energy inverters have been largely used in photovoltaic (PV) array power production, wind-driven power generation, and hydrogen-based fuel-cell power production [1,2,3]
Thereby to eradicate dither/following error, ward off tedious time-consuming and inefficient computations, and gain the global best solutions, the novel fixed-time-convergent sliding mode technology (NFTCSMT) best parameter values are determined by the improved quantum particle swarm optimization (QPSO) methodology
For specified good performance and strong robustness, the numerical simulations as well as experiments are afforded for the confrontation amid the customary terminal sliding-mode variable structure control (TSMVSC) and the suggested controller
Summary
Renewable energy inverters have been largely used in photovoltaic (PV) array power production, wind-driven power generation, and hydrogen-based fuel-cell power production [1,2,3]. It was plain that in these latter years, terminal sliding-mode variable structure control (TSMVSC), which generates limited convergent response and singularityless, has provided another classification of the SMT. This TSMVSC is continually used to obtain the achieved contributions [19,20,21,22,23]. If viewed from another angle, the practical application of artificial intelligence methods provides very interesting subjects among the many areas of applied sciences and industrial technology Based on this motivation, it would be a good idea to introduce a globally-optimal methodology into NFTCSMT design to obtain the best solution. The REI employing this suggested control design is able to produce lower total harmonic distortion (THD) percentages with regard to vague interventions
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