African vulture optimizer algorithm based vector control induction motor drive system

This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response

Development and implementation of two-stage boost converter for single-phase inverter without transformer for PV systems

This paper offers a two-stage boost converter for a single-phase inverter without transformer for PV systems. Each stage of the converter is separately controlled by a pulse width modulated signal. A Simulink model of the converter using efficient voltage control topology is developed. The proposed circuit performance characteristics are explained and the obtained simulation results are confirmed through the applied experiments. Moreover, this paper has examined the control circuit of a single-phase inverter that delivers a pure sine wave with an output voltage that has the identical value and frequency as a grid voltage. A microcontroller supported an innovative technology is utilized to come up with a sine wave with fewer harmonics, much less price and an easier outline. A sinusoidal pulse width modulation (SPWM) technique is used by a microcontroller. The developed inverter integrated with the two-stage boost converter has improved the output waveform quality and controlled the dead time as it decreased to 63 µs compared to 180 µs in conventional methods. The system design is reproduced in Proteus and PSIM Software to analyze its operation principle that is confirmed practically

Solar Hydrogen Variable Speed Control of Induction Motor Based on Chaotic Billiards Optimization Technique

This paper introduces a brand-new, inspired optimization algorithm (the chaotic billiards optimization (C-BO) approach) to effectively develop the optimal parameters for fuzzy PID techniques to enhance the dynamic response of the solar–hydrogen drive of an induction motor. This study compares fuzzy-PID-based C-BO regulators to fuzzy PID regulators based on particle swarm optimization (PSO) and PI-based PSO regulators to provide speed control in solar–hydrogen, induction-motor drive systems. The model is implemented to simulate the production and storage of hydrogen while powering an induction-motor drive which provides a great solution for the renewable energy storage problem in the case of solar pumping systems. MATLAB/Simulink 2021a is used to simulate and analyze the entire operation. The laboratory prototype is implemented in real time using a DSP-DS1104 board. Based on the simulation and experimental results, the proposed fuzzy-PID-based C-BO has reduced speed peak overshoot by 45.3% when compared to a fuzzy PID based PSO speed regulator and by 68.13% when compared to a PI-based PSO speed controller in the case of a large-scale motor. Additionally, the proposed speed regulator has a 6.1% faster speed rising time than a fuzzy-PID-based PSO and a 9.5% faster speed rising time than a PI–PSO speed controller. It has an excellent dynamic responsiveness value when compared to the other speed regulators

Experimental Validation of Second-Order Adaptive Fuzzy Logic Controller for Grid-Connected DFIG Wind Power Plant

This paper introduces a second-order adaptive fuzzy logic controller (SO-AFLC) to enhance the characteristics of a doubly fed induction generator (DFIG) inside a grid-tied wind power plant (WPP). SO-AFLCs were utilized to maximize the output of the DFIG wind power plant (WPP) and improve dynamic responsiveness with extremely low mean square error. When comparing the mean square error of SO-AFLC with proportional-integral controllers (PI) and adaptive fuzzy logic controllers (AFLC), the reductions are 87.38% and 40.39%, respectively. This controller prevents overshoots and oscillations. DFIG wind power plant is modeled and simulated using Matlab/Simulink package. Under the unit step wind speed profile, SO-AFLC improved the steady-state error in the Cp waveform by 63.25% compared with the PI controller and 13.12% compared with AFLC. DSpace1104 is used to conduct an experimental investigation to validate the simulation results. In addition, realistic data from the wind farm at RAS Ghareb in the Gulf of Suez, Egypt, are used in this study to achieve more realistic results. Compared to those obtained with PI and AFLC, the results obtained using SO-AFLCs showed fast time response, high convergence rate, reduced peak overshoot, less undershoot, and low steady-state error in terms of power coefficient of the turbine, DC link voltage control, and rotor speed tracking. In addition, a wind turbine performance index based on gross system integral absolute error (IAE) is provided. This index is used to illustrate the SO-AFLC methodology’s viability compared to AFLC and PI under the same wind turbine conditions