Conventional direct torque control is one of the most effective strategies for managing the torque of an induction machine (DTC), which is one of the most common types of machines. Instead, the DTC's inadequate control ability is made worse at low speeds by the audible flux and torque waves that are generated by its extremely low switching frequency. This is because the DTC's switching frequency is exceedingly low. In an effort to address these concerns, a variety of direct torque control strategies focused solely on flux and torque as their primary concerns. An improvement in DTC control and the elimination of ripples in induction motors are the goals of this research, which introduces a sliding mode fuzzy logic controller technique. The complexity of the algorithm, sensitivity of the parameters, the tracking speed, the switching loss, and the ripple reduction properties of the technique will all be thoroughly investigated. Simulation of the control mechanism is performed with MATLAB/Simulink in order to verify that it functions as intended.
This paper presents and evaluates advanced control strategies to enhance power tracking and robustness in doubly fed induction generator systems operating under realistic and perturbed wind conditions. In addition to the conventional field-oriented control, we develop a model predictive control approach that determines the optimal rotor voltage vectors by minimizing a quadratic cost function, as well as a fuzzy-weighted model predictive control in which the cost weight is adjusted online based on the tracking error and its derivative. The dynamic models used accurately represent the key behaviors of the doubly fed induction generator and its rotor-side and grid-side converters. MATLAB/Simulink simulations are carried out using two wind scenarios: a smooth sinusoidal profile and a filtered stochastic profile, while a robustness test introduces variations in rotor parameters during operation. The results demonstrate that the fuzzy-weighted model predictive control achieves faster convergence, lower steady-state error, and improved robustness, all while maintaining reasonable converter effort and acceptable power quality.
Need for off-grid electric vehicle (EV) charging solutions, using intelligent control systems, such as machine learning (ML)-based maximum power point tracking (MPPT), to harness solar energy, offers a means of optimizing efficiency even in the face of fluctuations. This innovative strategy combines clean energy, cutting-edge power electronics, and practical application, which makes it perfect for fostering innovation in areas with inadequate infrastructure. For rural areas without grid infrastructure, this paper presents a novel design and performance assessment of a portable solar-powered EV charging system. To maximize solar energy harvesting and charging efficiency, the system combines an interleaved buck-boost converter with an ML-driven MPPT algorithm. It is appropriate for small electric vehicles (EVs) like auto rickshaws because it uses a 48 V lithium iron phosphate (LiFePO₄) battery. A supervised regression model trained on real-time electrical (voltage, current, and power) and environmental (temperature, irradiance) parameters is used to implement the MPPT algorithm. The system was created using MATLAB/Simulink, and the key performance parameters were evaluated using real-time information. Analyses of the key performance metrics like charging efficiency, converter stability, and tracking accuracy show a superior energy harvesting efficiency of 97%.
This paper offers a structured methodology for regulating optimal control switching angles in multilevel inverters to achieve selective harmonic elimination (SHE) using embedded optimization algorithms on the PYNQ‑Z2 FPGA operating base. The proposed approach employs metaheuristic techniques including particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimization (GWO), slime mould algorithm (SMA), and whale optimization algorithm (WOA) to generate candidate switching angles across a wide range of modulation indices. For each modulation index, the most effective solution towards harmonic reduction and waveform quality is selected and implemented on the FPGA controller, enabling reliable real‑time operation with very low delay. Experimental validation of a 31‑level single‑phase inverter confirms the effectiveness of the method in eliminating selected harmonics and refining the fundamental output. Combining comparative algorithmic selection with FPGA‑based control demonstrates a systematic and efficient strategy for advanced inverter systems. A MATLAB/Simulink model is constructed to represent the 31-level inverter to enhance the practical aspect and study the frequency spectrum, where many harmonics that contribute to obtaining THD within the IEEE standards are eliminated. Also, the different types of losses are calculated based on the governing mathematical rates.
With the increasing global interest in renewable energy, advanced control strategies for wind energy conversion systems (WECSs) are critical for achieving not only maximum efficiency but also grid compatibility. This paper introduces a new adaptive, forecast-based predictive control algorithm to maximize the real-time energy efficiency of wind turbines integrated with smart grid architectures. The method couples a short-term wind-speed prediction module with a model-predictive control algorithm that uses the predictions to anticipate and adjust turbine operating conditions, such as pitch angle and rotor speed, to counteract the mechanical load from wind shear while maximizing power production. The analysis employs a dataset of 487 high-resolution cases of wind speed and wind turbine performance metrics to train and validate the adaptive model. The simulations were conducted in MATLAB/Simulink, using a model that captures the turbine’s nonlinear behavior and the electrical grid connection. The results indicate that the proposed controller increases the cycle-averaged power coefficient by approximately 8.2% and reduces tip-speed-ratio tracking error by roughly 70% relative to a conventional proportional-integral-derivative (PID) controller, while also reducing pitch and torque actuation, thereby lowering structural fatigue loads. This study is expected to provide a reliable solution to wind energy intermittency and facilitate the more stable and efficient integration of wind farms into modern smart grid systems.
