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.

This paperpresents a direct power control (DPC) method using an adaptive reference model, known as Model Reference Adaptive Control (MRAC), for the Doubly Fed Induction Generator (DFIG). This approach addresses to get over the drawbacks of the classic DPC, which relies solely on PID controllers. These limitations are often addressed with the compromise speed/efficiency trade-off and divergence from peak power in the event of rapid variation in wind speed. The Doubly Fed Induction Generator (DFIG) mathematical equations in the d-q reference frame are provided. Subsequently, a direct power control (DPC) algorithm is developed for controlling of the DFIG, employing PID controllers, along with space vector modulation (SVM) to maintain a steady switching frequency. The Maximum Power Point Tracking (MPPT) technique keeps the stator side power factor at unity level. The conventional PID controllers are replaced with Model Reference Adaptive Control (MRAC) in the DPC framework. Furthermore, the efficacy of DPC-based MRAC is assessed and contrasted with that of the PID controller. Results from robustness tests conducted in the MATLAB/Simulink environment demonstrate that MRAC exhibits efficiency, superior dynamic performance, and enhanced robustness against parameter variations.

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 use of MPPT control is essential for the optimization of a photovoltaic system. It consists in controlling the static converter to reach the maximum power of the photovoltaic generator. Indeed, the MPP research is based on the variation of the duty cycle according to the evolution of the input parameters. the current and the voltage and consequently the power of the photovoltaic generator until the MPP is reached. The MPPT algorithm can be more or less complicated to find the MPP. In our paper we have presented three MPPT commands: perturb and observe, fuzzy logic, particle swarm optimization MATLAB/SIMIULINK is used. The results of the simulation illustrate the high tracking performance of the proposed technique under different climatic conditions.

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.

Photovoltaic energy is a very important renewable energy source. However, modern energy strategies are increasingly integrating the PV system into the grid. In the PV grid connected, energy improvement is a continuous process that includes maximizing PV power, regulating DC link voltage, compensating reactive power, and minimizing total harmonic distortion. In this paper, the two-stage configuration of the transformerless system is adopted for PV grid connections. In this configuration, it is aimed at improving the DC link voltage controller. Three controls can be compared, such as the conventional DC Link voltage controller, the back-stepping voltage controller, and the proposed DC Link voltage controller. These controls are tested in MATLAB/simulink in order to evaluate the performance of each one. The results show some advantage for the proposed control.

The performance of a photovoltaic system is strongly affected by the environmental conditions which it is subjected such as random atmospheric variations. In order to improve the performance of a photovoltaic system, the work of this paper is devoted to the comparative study between the following MPPT algorithms: the perturbation and observation algorithm (P&O) and the particle swarm optimization algorithm PSO. These two algorithms are tested under various atmospheric conditions and evaluated in terms of efficiency, stability, speed, and robustness. The obtained simulation results show the effectiveness of the PSO than the P&O algorithm.

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.

As PV energy is clean and renewable, more and more PVG is connected to the grid. In this paper, PV grid connected is presented in a two-stage configuration. The first stage is a DC bus. It is composed by a BOOST converter with its MPPT control. This control is based on Perturb and observe method. This method is used under three scenarios, conventional method, optimized method by two PI controllers and modified method. The second stage is the AC bus. It is composed of a VSI inverter and LCL filter with its control, where there are three loops are figured such as outer current, voltage and inner current. The reactive power compensation and THD compensation are considered in order to improve energy flow between PVG and the three phase grid. Parameters PI controller are optimized by an easy analytical method. The theoretical study is tested and verified by simulation under MATLAB.Simulink.

This issue of Interdisciplinary Methods and Approaches for Cybernetics and Systems Modeling includes a collection of extended versions of best presented papers in CoDIT 2020 conference. The aim con...

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.

This paper presents a control design for the irrigation station by sprinkling. The proposed method is applied in order to solve the problem of managing water sources and distributions systems. This paper presents the synthesis of a Fuzzy Logic control applied to the station of irrigation by sprinkling, this method has the advantage of stability conditions of the proposed controller. After presentation of mathematical model of our station, simulation results illustrate the performance of the control strategy.

In this paper, is proposed a robust sensorless speed observer-controller scheme for a Permanent Magnet Synchronous Motor (PMSM) based on the use of an Extended Kalman Filter (EKF) to estimate both position and speed, without any mechanical sensor. Then, a state-feedback optimal control algorithm is developed for uncertain of PMSM, based on the use of an Algebraic Riccati Equation (ARE) and a convex optimization approach. The robustness of this method, with respect to the parameters uncertainties, is tested, with success, for fourth order model of the studied process.