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

Abstract

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.

Sensorless Tracking Control Based on Sliding Mode for the “Full-Bridge Buck Inverter–DC Motor” System Fed by PV Panel

“…Furthermore, in the literature, studies have been presented where the primary power source is generated from renewable energy that feeds power electronic converters or other systems. Within these contributions, the following applications are found: power converters [72][73][74], AC motors [75,76], pumping systems [77][78][79], and electric vehicle motors [80][81][82].…”

The Validation and Implementation of the Second-Order Adaptive Fuzzy Logic Controller of a Double-Fed Induction Generator in an Oscillating Water Column

“…The memberships are formulated as positive medium (PM), positive big (PB), negative big (NB), negative medium (NM), positive small (PS), zero (Z), and negative small (NS), as illustrated in Figure 5 [31]. The triangle membership functions (MFs) with overlapping on (NB) and (PB) have demonstrated the ability to introduce output/input fuzzy sets with the best accuracy and a tolerable computational cost [32,33]. This restricts the number of firing rules in a two-input FLC to a maximum of four, independent of the quantity of fuzzy sets applied to each input variable.…”

“…This restricts the number of firing rules in a two-input FLC to a maximum of four, independent of the quantity of fuzzy sets applied to each input variable. In an unconstrained situation, increasing the number of fuzzy sets per input variable increases the number of rules firing at once, since each FLC input is fuzzified into a growing number of fuzzy sets, each of which depends on the number of fuzzy sets overlapping each other [33]. Table 1 shows the rules of the AFLC.…”

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

“…6. One idea states that the input/output fuzzy sets are represented by the triangular membership functions (MFs) with overlap (PB) [32], [33]. The fuzzy inference employs 49 control rules to generate the proper signal, as illustrated in Table I.…”