Due to their simplicity, cheapness, and ease of maintenance, induction motors (IMs) are the most widely used motors in the industry. However, if they are not properly controlled, the load torque and motor speed will fluctuate in an unsatisfactory fashion. To effectively control the load torque and speed of these IMs, it is necessary to use specialized drives. The entire system (IMs + Drives) will experience uncertainty, nonlinearities, and disruptions, which calls for an outstanding performance control structure. The sensorless sliding mode predictive torque control (SSM-PTC) for both AC-DC converter and DC-AC inverter, which are utilized for feeding the IM, is investigated in this work. The AC-DC converter is controlled using the SSM-PTC method in order to follow the DC-link reference voltage throughout any changes in the operating point of the IM. While the DC-AC inverter is controlled using a sensorless predictive power control (SPPC). Within a unity power factor, this SPPC regulates the reactive power flow between the motor and the supply to account for the undesirable harmonic components of the grid current. In addition, an experimental performance improvement of SSM-PTC of IM supplied by a 5-leg AC-DC-AC power converter using extended Kalman filter (EKF) without weighting factor (WF) is also studied in this work. Design and implantation of the suggested control systems are performed using a dSPACE 1104 card. The experimental results of the proposed converter control demonstrate that the suggested approach effectively regulated the DC link, reducing load torque and speed fluctuations. In the context of inverter control, a prompt active power response yields a motor current waveform that resembles a sinusoidal pattern, exhibiting minimal levels of harmonic distortion.

Purpose: Direct Torque Control (DTC) of induction motor drives is a well-established technique owing to features such as fast dynamic and insensibility to motor parameters. However, conventional DTC scheme, based on comparators and the switching table, suffers from large torque and flux ripples. To improve DTC performance, this study aims to propose and implement a sensorless finite-state predictive torque control using extended Kalman Filter in dSPACE environment. Design/methodology/approach: This paper deals with the design of an extended Kalman filter for estimating the state of an induction motor model and for sensorless control of systems using this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. Findings: Simulation and experimental results reveal that the drive system, associated with this technique, can effectively reduce flux and torque ripples with better dynamic and steady state performance. Further, the proposed approach maintains a constant switching frequency. Originality/value: The proposed speed observer have been developed and implemented experimentally under different operating conditions such as parameter variation, no-load/load disturbances and speed variations in different speed operation regions.

Wind energy is a fast-growing renewable energy source that helps reduce greenhouse gas emissions and supports sustainable power solutions. However, managing wind turbines efficiently and reliably is challenging, especially when wind conditions are unpredictable. Traditional control methods often rely on mechanical sensors such as anemometers, which increase complexity, maintenance costs, and reduce reliability. They can also suffer from problems like torque fluctuations, high computational demands, and reduced power quality. To make wind turbines work efficiently and reliably, especially under changing wind conditions, advanced control strategies are needed. This paper addresses these challenges by proposing an innovative control method designed to improve system performance and simplify turbine management. This paper presents a new control strategy that combines two intelligent estimation techniques—the Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO)—with Three-Vector Finite Set Predictive Torque Control (3V FS-PTC). This method is designed specifically for wind turbines that use Permanent Magnet Synchronous Generators (PMSGs) connected directly to the power grid (Variable Speed Wind Turbine (VSWT)). This approach does not require an anemometer, mechanical parameters, or rotor position sensors, making the system simpler, more reliable, and cost-effective. The 3V FS-PTC algorithm enhances control performance by selecting the three most optimal voltage vectors, two active voltage vectors and one zero voltage vector. Experimental results demonstrate a 19.75% reduction in computational time during wind speed profile, and the method significantly reduces torque and flux ripples compared to Conventional Model Predictive Torque Control (CMPTC), which uses just one voltage vector per control period and often leads to significant torque fluctuations. Additionally, the Tip Speed Ratio Maximum Power Point Tracking (TSR-MPPT) approach is used to maximize efficiency, achieving the power coefficient value at 0.48. Then the energy produced has been successfully integrated into the grid with low THD value of 3.5% and high power factor value of 0.99. The proposed FS-PTC strategy was implemented experimentally, and its performance was evaluated under varying wind speeds using MATLAB/SIMULINK, ControlDesk, dSPACE DS1104, and the DSP LAUNCHXL-F28379D real-time platform.

This paper introduces a novel fractional-order fuzzy logic controller (FOFLC) designed for stator voltage control in standalone doubly fed induction generator (DFIG) systems used in wind energy applications. Unlike traditional fuzzy logic controllers (FLCs), which are limited by integer-order dynamics, the FOFLC leverages the advanced principles of fractional-order (FO) calculus. By integrating fuzzy logic with fractional-order operators, the FOFLC enhances system precision, adaptability, and interpretability while addressing the inherent limitations of conventional proportional-integral (PI) controllers and integer-order FLCs. A key innovation of the FOFLC is its dual-mode architecture, enabling it to operate seamlessly as either a traditional FLC or a fractional-order FOFLC controller. This versatility allows for independent tuning of fractional parameters, optimizing the system’s response to transients, steady-state errors, and disturbances. The controller’s flexibility makes it particularly well-suited for nonlinear and dynamically complex stand-alone renewable energy systems. The FOFLC is experimentally validated on a 3-kW DFIG test bench using the dSPACE-1104 platform under various operating conditions. Compared to a conventional PI controller, the FOFLC demonstrated superior performance, achieving 80% reduction in response time, eliminating voltage overshoot and undershoot, reducing stator power and torque ripples by over 46%, and decreasing total harmonic distortion (THD) of both stator voltage and current by more than 50%. These results confirm the FOFLC’s potential as a robust and adaptive control solution for stand-alone renewable energy systems, ensuring high-quality power output and reliable operation.

This article presents direct flux-vector control (DFC), a new control strategy based on direct torque control (DTC), for a doubly fed induction generator (DFIG) driven by wind, feeding AC loads without grid access