Enhancing the power quality of dual rotor wind turbines using improved fuzzy space vector modulation and super twisting sliding techniques.

In the field of control, many approaches have been used to control generators, where indirect vector control (IVC) is considered one of the most prominent of these approaches due to its many advantages. This approach has a fast response time (RT) and is quite easy to realize. However, its reliance on traditional controllers makes this approach less efficient and less effective if the system parameters change.

Genetic algorithm type 2 fuzzy logic controller of microgrid system with a fractional-order technique.

This paper presents a hybrid approach that combines a genetic algorithm (GA)-optimized type-2 fuzzy logic controller (T2FLC) with a fractional-order technique for enhanced control of a microgrid system. The T2FLC approach is employed to handle the inherent uncertainties in the microgrid due to fluctuating renewable energy inputs and varying loads. The GA optimizes the parameters of the designed FO-T2FLC approach, ensuring optimal performance under different operational conditions.

Control of multi-level quadratic DC-DC boost converter for photovoltaic systems using type-2 fuzzy logic technique-based MPPT approaches.

This paper introduces a type 2 fuzzy logic (T2FL)-based controller for maximum power point tracking (MPPT) in a high-gain three-level quadratic DC-DC boost converter (TLQDC-DCBC) designed for photovoltaic (PV) systems. High-gain DC-DC converters like the TLQDC-DCBC are beneficial in PV applications as they boost low PV voltages to higher levels, thereby reducing power losses and improving overall efficiency. The proposed MPPT-T2FLC addresses key limitations of traditional MPPT methods, including the oscillations near the maximum power point (MPP) in incremental conductance (IC) algorithms and the reduced precision of type-1 fuzzy logic (T1FL) controllers under uncertain environmental conditions.