Wireless power transfer (WPT) based on inductive coupling has gained increasing interest in scientific research, particularly in the field of implantable medical devices (IMDs). The efficiency of a WPT system primarily depends on the operating frequency, the quality factors (Q1 and Q2) of the coils, and significantly on the coupling coefficient (k) between the two coils forming the link. This efficiency can be enhanced by optimizing the geometric parameters of the coils or adjusting the operating frequency. In this paper, we characterize the coil parameters constituting the inductive link to demonstrate the impact of their variation on the energy transfer efficiency. Furthermore, we address the design considerations of the coils for wireless power transfer applications in IMDs.

A detailed comparative analysis of sawtooth and triangular carrier pulse-width modulation (PWM) techniques for buck chopper systems is presented in this study. The research addresses the need for empirical performance evaluation in motor control applications by systematically examining critical operational parameters, including speed regulation, torque stability, and current dynamics. MATLAB/Simulink is employed for system modeling and simulation, while real-time validation is conducted using RTLAB to ensure practical applicability. The results demonstrate that both techniques achieve fast dynamic response, with overshoot limited to less than $\mathbf{1 6} \boldsymbol{\%}$ and settling time maintained below 0.2 seconds during speed transitions. However, distinct performance characteristics are observed. Superior steady-state performance is exhibited by the sawtooth carrier method, with current oscillations reduced by approximately 30% and torque ripple lowered by 25% compared to the triangular approach, making it particularly suitable for precision applications. In contrast, marginally faster transient response is provided by the triangular carrier, though higher electromagnetic interference is generated due to more pronounced current fluctuations. These findings offer clear selection criteria for engineers based on application priorities, whether for high-precision systems requiring smooth operation or scenarios where rapid response is prioritized. The study contributes to power electronics optimization by establishing validated performance benchmarks and practical implementation guidelines for buck chopper control strategies.

This study investigates the performance of bipolar and unipolar control methods for single-phase asynchronous motors. Using MATLAB's SimPower System for modeling and RTLAB for validation, the study highlights significant findings. Unipolar control demonstrated a 20.23% reduction in total harmonic distortion (THD) compared to bipolar control, resulting in improved current waveform quality and enhanced torque response. Bipolar control, while less complex to implement, exhibited higher THD and marginally slower dynamic response. These findings provide actionable insights for optimizing motor control systems in various industrial and domestic applications.

Renewable energy sources are becoming increasingly critical for combating global electricity shortages and mitigating environmental pollution. Among these, solar energy stands out for it s abundance, minimal environmental impact, and widespread availability across diverse regions. However, optimizing solar energy production is essential to maximize its potential by reducing costs, improving efficiency, and fully utilizing this ever -present resource. This research introduces two novel, AI -powered nonlinear control methodologies designed for precise Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. These methodologies are specifically crafted to handle dynamic changes in solar irradiance and temperature with exceptional adaptability. The proposed techniques' performance is rigorously evaluated through simulations conducted within the MATLAB/Simulink environment. The evaluation focuses on their effectiveness under various system conditions, employing two distinct DC -DC converter configurations for a comprehensive analysis.

The power-current relationship of a photovoltaic generator (GPV) is non-linear and contingent upon environmental factors. Nonetheless, achieving the highest possible power output from a GPV can only occur at a specific point along the characteristic curve. The development of Maximum Power Point Tracking (MPPT) techniques is fundamental to designing solar systems that optimize power generation. The Adaptive Fuzzy Neural Inference System (ANFIS) is one of the most effective ways to attain the maximum power point (MPP) in PV systems due to its prompt response time and minimal oscillations. Furthermore, sliding mode control (SMC) is a popular method for managing linear and nonlinear systems because of its robustness. The primary objective of this research is to introduce a novel approach that utilizes a combination of ANFIS and Sliding Mode Control (ANFIS-SMC) to safeguard the PV system against uncertain conditions and achieve the optimum power point. The simulation outcomes indicate that the ANFIS-SMC controller delivers a precise, swift, and resilient response, compared to other algorithms like perturb and observe (P&O).

This work compares three MPPT (maximum power point tracking) algorithms for solar panels: Perturb and Observe (P&O), Incremental Conductance (INC), and Particle Swarm Optimization (PSO). Despite the fact that each algorithm seeks to optimize output power by adjusting tension and charge current, they differ in method and complexity. The P&O algorithm is simple to implement, but it can be unstable. The INC algorithm is more effective in managing several sites of operation, but the PSO method is more difficult yet effective. As a result, the choice of algorithm is determined by the ultimate application and the required complexity.

This article discusses the significance of parameter identification in solar cell model formulations for simulation and design of photovoltaic systems. The most often used designs are those based on diodes, with designs with one or two diodes being the most significant. In order to minimize the difference between calculated and measured data, an objective function is used to optimize the extraction of parameters from these models. To handle parameter extraction in the photovoltaic field, a number of traditional and hybrid digital analytic models have been developed. Recently, meta-heuristic optimization algorithms have been used to overcome the challenges of finding highly credible results quickly and with the appropriate level of precision. The study suggests using the meta-heuristic Mountaineering Team-Based Optimization (MTBO) algorithm with iterative Newton-Raphson technique to estimate the model parameters for one and two diodes. The results of several algorithms that have been described in the literature are used to compare how well the MTBO method performs.

Examination of performance degradation of on-grid PV power plants shows many failures in PV modules with C-Si technologies, such as hot spots, degradation of interconnect fingers, discoloration, cracking of cells of delamination, the weld defect in the connection between the cells. The degradation rate is based on system size, age, deployment climate, and mounting configuration. Potential-induced degradation and mismatch effect are two main cascading mechanisms responsible for high degradation rate and reduction in conversion rate. In this article, we will have analyzed the photovoltaic system affected by the PID and simulated it, classified the various breakdowns generated by the PID and then we will have proposed some actions to reduce or attenuate these effects. Finally, we will describe the lag effect created by PID in PV modules and its effect on MPPT control.

Nowadays, managers are looking for adequate strategies and advanced logistics management models in order to achieve their competitiveness and improve their overall performance. The current literature on supply network planning has not adequately incorporated sustainability factors, uncertainties, and the integration of various levels of planning (operational, tactical and strategic). In this paper, we address the integrated planning of logistics networks in an uncertain and sustainable environment. The problem is formulated as a two-stage stochastic programming which minimizes the logistics costs and evaluates a posteriori the CO2 emissions and the transport accident risk. This model is solved using the Sample Average Approximation (SAA) approach. Numerical experiments are performed using CPLEX to study the impact of different parameters on sustainability aspects.

Several variants of particle swarm optimization (PSO) have been created to identify various solutions to compli-cated optimization problems. Only a few PSO algorithms exist that can locate and monitor multiple optima in dynamically shifting search landscapes when dealing with dynamic optimization situations. These methods have yet to be thoroughly tested on a large number of dynamic optimization problems. In fact, because there are so many PSO algorithm modifications, it's simple to get stuck in a local optima. To address the aforementioned flaws, this work proposes and evaluates an enhanced version of the multiswarm particle swarm optimization technique (MsPSO) with numerous variations particle swarm optimization published in the literature. Standard tests and indicators provided in the specialized literature are used to verify the effectiveness of the suggested algorithm. Furthermore, on the CEC’ 13 test suite, comparison results between the extended heterogeneous multi swarm PSO algorithm (XMsPSO) and other nine popular PSO show that XMsPSO achieves a very optimistic performance for solving various kinds of problems, contributing to both higher solution accuracy.

In this paper, we present a comparison of three types of algorithms: Perturb and Observe (P&O), Incremental Conductance (InCnd), and Fuzzy Logic Controller (FLC) that track the maximum power point (MPP) of a photovoltaic system (PV) over varying conditions of solar irradiation and temperature. The PV system is composed of solar panels, a resistive load, and an MPPT controller with pulse width modulation (PWM) technique for driving the DC-DC Quadratic Boost converter (QBC). This comparison, based on three criteria, namely stability, time response and, power efficiency, demonstrates the benefit of employing an MPPT with variable step monitoring. The energy obtained using those three algorithms is practically similar, with a considerable improvement for the Fuzzy Logic Controller.

In this paper, an identification problem for nonlinear models is explored and an improved fuzzy identification method based on the heterogeneous Multi-swarm PSO (MsPSO) algorithm is proposed in order to obtain an optimal T-S fuzzy model. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To improve the performance of MsPSO, the particles should be able to adaptively changing their original trajectories to explore new search space. In fact, a new multiswarm particle swarm optimization algorithm using an adaptive inertia weight, denoted AIMsPSO, has been presented in order to improve the performance of constructing the T-S fuzzy system.