Explore the vast range of titles available.

Accurately simulating photovoltaic (PV) modules requires precise parameter extraction, a complex task due to the nonlinear nature of these systems. This study introduces the Mother Tree Optimization with Climate Change (MTO-CL) algorithm to address this challenge by enhancing parameter estimation for a solar PV three-diode model. MTO-CL improves optimization performance by incorporating climate change-inspired adaptations, which affect two key phases: elimination (refreshing 20% of suboptimal solutions) and distortion (slight adjustments to 80% of remaining solutions). This balance between exploration and exploitation allows the algorithm to dynamically and effectively identify optimal parameters. Compared to seven alternative methods, MTO-CL shows superior performance in parameter estimation for various solar modules, including ST40 and SM55, across different irradiances and temperatures. It achieves exceptionally low Root Mean Square Error (RMSE) values from 0.0025A to 0.0165A and Mean Squared Error (MSE) values between 6.2 × 10^−6 and 2.7 × 10^−4, while also significantly minimizing power errors, ranging from 22.86 mW to 239.40 mW. These results demonstrate MTO-CL’s effectiveness in improving the accuracy and reliability of PV system modeling, offering a robust tool for enhanced solar energy applications.

This work is aimed at achieving a simple and reduced-cost configuration of photovoltaic (PV) water pumping system (PVWPS) using an induction motor with high efficiency. The proposed PV system is composed of two stages of converters which the first one ensures the maximum power point by controlling the duty ratio of boost converter using variable step size incremental conductance (VSS INC) technique. Fuzzy logic control based on direct torque control is proposed to serve the purpose of operating an induction motor. Moreover, the combining of these proposed control strategies has been never discussed. The proposed control scheme is modeled and simulated in detail under MATLAB/Simulink software to evaluate its performance under fast variations of irradiance and daily climatic profile. The obtained simulation results using the suggested control strategies are compared to those using the most used method in the literature (variable step size perturb and observe (VSS P&O) algorithm). The simulation results indicate that the proposed PVWPS performed best in terms of the time of response; pumped water, flux ripples, and the stator currents are reduced.

The article presents an innovative method leveraging statistical sound analysis to diagnose malfunctions in variable-speed drive-controlled (VSD) induction machines (IM). This early anomaly detection approach strengthens preventive maintenance by preventing major failures. The bell-shaped curve of the Gaussian distribution, integrated into this analysis, reveals a significant concentration of values around the mean, thus facilitating the early detection of abnormal sounds emitted by the machine. Experiments highlight that driving IMs controlled by frequency inverters at low speeds can induce the emergence of resonance frequencies, altering the machine's sound in an unusual and bothersome manner. To address this issue, a statistical characterization of the sound data from IMs is adopted. In this experimental study, the machine is first powered by a zero-frequency inverter, then by frequencies of 16.66Hz, 33.33Hz, and finally at the nominal frequency of 50Hz. The recorded sound signals are then transformed in to data matrices, and a statistical analysis is conducted, calculating different variables and coefficients of high-order statistics (HOS). This approach leads to precise characterization of normal and abnormal sounds, thus improving the machine's reliability and overall operational efficiency.

This article presents a modeling and control of the Doubly Fed Induction Motor (DFIM), associated with two inverters controlled through the Pulse Width Modulation technique (PWM), the control of the DFIM is carried out by the approach of Rotor Flux Oriented Control (RFOC) according to the direct axis. In this approach, regulation is done by classic PI regulators, the latter having undesirable overruns and static errors in non-linear systems, for that the introduction of the control by sliding mode in place of the classic PI speed regulator, that is in the form of a control law based on this type of controller since it is invariant to the non-linearity of the system and precise, stable, simple and has a good response time, in order to validate the objectives of improving the DFIM behavior in front of the reference parameters, such as the speed and the torque imposed on the machine. The results of the proposed approach are validated by its implementation on the Matlab/Simulink environment.

Conventional direct torque control (DTC) is one of the excellent control strategies available to control the torque of the induction machine (IM). However, the low switching frequency of the DTC causes high ripples in the flux and torque that leads to an acoustic noise which degrades the control performances, especially at low speeds. Many direct torque control techniques were appeared to remedy these problems by focusing specifically on the torque and flux. In this paper, a state of the art review of various modern techniques for improving the performance of DTC control is presented. The objective is to make a critical analysis of these methods in terms of ripples reduction, tracking speed, switching loss, algorithm complexity and parameter sensitivity. Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for academic and industrial researchers.

Photovoltaic Water Pumping Systems (PVWPS) have become increasingly important as a renewable energy solution in rural areas, providing energy independence, cost savings, and environmental friendliness. This system has two main controllers. The first controller is employed to maximize power extraction from the PV array by controlling the duty ratio of the DC-DC boost converter. The second controller is responsible for regulating the operation of the induction motor through the switching pulses of the Voltage Source Inverter (VSI). These two controllers play an essential role in the system, which increases efficiency and performance. Therefore, the innovative aspect of this work consists of introducing Artificial Neural Networks (ANNs) based on each PVWPS controller. On the one hand, ANN-based MPPT is implemented to ensure optimal performance of the PV array under varying irradiation levels. On the other hand, to overcome the defects and problems caused by Direct Torque Control (DTC), such as flux and torque ripples, high switching frequency, and challenges at low speeds, an ANN-based DTC is proposed in which each of the hysteresis comparators, switching table, and speed controller in the DTC are replaced by ANN controllers. The PVWPS based on the proposed controls is thoroughly modeled and simulated using MATLAB/Simulink software and validated using dSPACE DS1104 Board. The results demonstrate significant improvements, including a 75.51% reduction in flux ripples, a 77.5% reduction in torque ripples, a 44.79% improvement in response time, and an increase in the water quantity. Furthermore, the Real-Time simulation and visualization obtained are consistent with the simulation outcomes.

This paper investigates enhancing the efficiency of solar water pumping systems (SWPS) by implementing a Maximum Power Point Tracking technique based on the Bat Metaheuristic Optimizer (MPPT-bat) for the photovoltaic generator (PVG) side, coupled with Direct Torque Control (DTC) for the induction motor powering the pump. Unlike traditional techniques, which make no compromise between tracking speed, oscillation and robustness. The integration of the MPPT-bat represents a significant advance, making it possible to improve PVG performance whatever the weather conditions. The main objective remains to improve the energy efficiency of this type of application by maximizing the electrical power allocated to the SWPS. At the same time, a DTC controller applied to the pump motor aims to optimize the use of the energy generated by the MPPT-bat. This intelligent approach adjusts the motor power according to the power extracted from the PVG, thus avoiding inappropriate profiles for the pumping system. The study confirms that optimizing SWPS using this approach based on MPPT-bat and DTC, significantly improves overall performances in terms of tracking error, oscillations, tracking speed and robustness, promotes more efficient pump rotation and, consequently, increases the flow rate of pumped water, and that these improvements persist under different climate conditions.

Numerous recent innovations have been achieved with the goal of enhancing electric vehicles and the parts that go into them, particularly in the areas of managing energy, battery design and optimization, and autonomous driving. This promotes a more effective and sustainable eco-system and helps to build the next generation of electric car technology. This study offers insights into the most recent research and advancements in electric vehicles (EVs), as well as new, innovative, and promising technologies based on scientific data and facts associated with e-mobility from a technological standpoint, which may be achievable by 2030. Appropriate modeling and design strategies, including digital twins with connected Internet of Things (IoT), are discussed in this study. Vehicles with autonomous features have the potential to increase safety on roads, increase driving economy, and provide drivers more time to focus on other duties thanks to the Internet of Things idea. The enabling technology that entails a car moving out of a parking spot, traveling along a long highway, and then parking at the destination is also covered in this article. The development of autonomous vehicles depends on the data obtained for deployment in actual road conditions. There are also research gaps and proposals for autonomous, intelligent vehicles. One of the many social concerns that are described is the cause of an accident with an autonomous car. A smart device that can spot strange driving behavior and prevent accidents is briefly discussed. In addition, all EV-related fields are covered, including the likely technical challenges and knowledge gaps in each one, from in-depth battery material sciences through power electronics and powertrain engineering to market assessments and environmental assessments.

Renewable energy sources are nowadays a viable choice to satisfy the rising energy consumption and promote the advancement of sustainable development. These systems are integrated into microgrids using a variety of technological solutions to ensure customer communication and distributed generation facilities in an optimal way. Energy management in microgrids refers to the information and control system that provides the necessary functionality to guarantee that the generating and distribution systems produce energy at the lowest expenses. This study analyzes the various optimization objectives, constraints, problem-solving techniques, and simulation tools used for connected and freestanding microgrids. It reviews the literature on energy control in microgrids powered by sustainable energy. Energy storage technology is also viewed as an intriguing alternative to managing the intermittent nature of renewable energy because of its advanced techniques, increased energy efficiency, and capacity to perform tasks such as frequency response. The final phase suggests future suggestions, particularly for the model-based prediction of energy storage systems.

Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application.