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Wind energy conversion systems (WECSs) are considered green generators, environmentally friendly, and fully suitable energy sources to replace fossil energy sources. WECS’s output power is hugely dependent on the random nature of the wind. There are many solutions to improve the output power for WECSs, such as adjusting the profile of turbine blades, locating installation places, improving generators, etc. Nevertheless, maximum power point tracking (MPPT) algorithms for WECSs are optimal and the most effective because they are flexible in controlling different variable wind speeds and match all types of WECS. The parameters on the generator side control or the grid side control will be adjusted when MPPT algorithms are used, allowing the output power of WECSs to be maximized while maintaining stability in variable-speed wind. There are various MPPT algorithms, but the current problem is their efficiency and whether it requires deep knowledge to select the best MPPT solutions because each method has different advantages and disadvantages. This study has implemented an overview of modern maximum power tracking algorithms applied to permanent magnet synchronous generators in WECS with MPP methods based on speed convergence, efficiency, self-training, complexity, and measurement of wind parameters.

Renewable energy-based solar photovoltaic (PV) generation is the best alternative for conventional energy sources because of its natural abundance and environment friendly characteristics. Maximum power extraction from the PV system plays a critical role in increasing the efficiency of the solar power generation during partial shading conditions (PSCs). Therefore, a suitable maximum power point tracking (MPPT) technique to track the maximum power point (MPP) is of high need, even under PSCs. This paper presents an organized and concise review of MPPT techniques implemented for the PV systems in literature along with recent publications on various hardware design methodologies. Their classification is done into four categories, i.e. classical, intelligent, optimal, and hybrid depending on the tracking algorithm utilized to track MPP under PSCs. During uniform insolation, classical methods are highly preferred as there is only one peak in the P-V curve. However, under PSCs, the P-V curve exhibits multiple peaks, one global maximum power point (GMPP) and remaining are local maximum power points (LMPP's). Under the PSCs, classical methods fail to operate at GMPP and hence there is a need for more advanced MPPT techniques. Every MPPT technique has its advantages and limits, but a streamlined MPPT is drafted in numerous parameters like sensors required, hardware implementation, cost viability, tracking speed and tracking efficiency. This study provides the advancement in this area since some parameter comparison is made at the end of every classification, which might be a prominent base-rule for picking the most gainful sort of MPPT for further research.

In this paper a comparison of different types of maximum power point search methods for the photovoltaic panels is presented. The methods that represent each group of maximum power point techniques will be implemented in the software that allows to test the behaviour of photovoltaic panels in different environment conditions including partial shading. In this paper each implemented method was compared including time of convergence with the maximum power point, tracking error and differences in the energy obtained from photovoltaics during the simulation time. The algorithms were compared under both uniform lighting and partial shade conditions.

For ensuring a greener and low carbon future; renewable energies sources such as solar energy stands out as a prominent solution for generating sustainable and clean electricity due to its accessibility, abundance and numerous benefits. The use of solar panel also called as photovoltaic systems has more importance in the world for their ability to convert sun irradiation into electricity while they have significant drawbacks like the nonlinearity of Pv panel. The efficiency and performance of Photovoltaic (PV) systems can be influnced by various factors, like climate fluctuations during the day. Therefore, it is so important to optimize the power capturing from PV panels. To optimize the energy created by photovoltaic modules, it is necessary to carefully select a DC-DC converter with MPPT control. This guarantees that the maximum power is extracted from the solar power plant and sent to the demand side in less time and with greater effectiveness. This paper introduces a super twisting sliding mode control technique for achieving maximum power point tracking (MPPT) in a photovoltaic (PV) system. The Single Ended Primary Inductor Converter (SEPIC) is proposed as a superior alternative to the conventional boost dc-dc converter, as it enables the extraction of the highest possible power from the photovoltaic panels array. Upon doing a thorough comparison of the suggested control with the P&O algorithm in various scenarios using the MATLAB/SIMULINK tool, it was found that the provided STC (Synchronous Tracking Control) for the SEPIC converter demonstrates greater efficiency and reduced oscillation around the Maximum Power Point (MPP).

The nonlinear characteristics and low efficiency of photovoltaic (PV) systems remain critical challenges that necessitate advanced solutions. This study proposes two innovative Maximum Power Point Tracking (MPPT) algorithms based on the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). The primary advantage of these methods lies in their adaptive step-size optimization, leveraging multiple criteria to determine the optimal step size. A novel fitness function was developed to improve tracking accuracy, minimize ripple, and reduce overshoot. Simulation results demonstrated remarkable improvements, including up to 98% reduction in ripple, 67% reduction in overshoot, and significant improvements in tracking accuracy compared to fixed-step methods. Field validation was conducted using real-world data from the Ain El Melh PV station in Algeria on June 21, 2023. Experimental results confirmed the effectiveness of the proposed methods, with the WOA-based MPPT achieving up to 99% ripple reduction and 40% overshoot reduction under dynamic environmental conditions. A comparative analysis of MPPT algorithms revealed superior performance metrics for the bio-inspired methods. The PO-WOA algorithm achieved the highest efficiency of 98.87% in simulation and 98.94% in real data, surpassing both PO and PO-GWO. It also minimized power loss to 0.56 W in simulation and 0.39 W in real data, demonstrating its optimization capabilities under fluctuating conditions. Although its response time was slightly longer than other methods, at 0.65 s in simulation and 0.48 s in real data, it prioritized stability and precision. These findings underscore the potential of WOA and GWO algorithms to enhance PV system performance, offering robust and efficient solutions for optimizing energy output in both simulation and real-world scenarios.

The problem of partial shading has serious effects on the performance of photovoltaic (PV) systems. Adding a bypass diode in shunt to each PV module avoids hot-spot phenomena, but causes multi-peaks in the power–voltage (P–V) characteristics of the PV array, which cause traditional maximum power point tracking (MPPT) techniques to become trapped in local peaks. This problem has forced researchers to search for smart techniques to track global peaks and prevent the possibility of convergence at local peaks. Swarm optimization techniques have been used to fill this shortcoming; unfortunately, however, these techniques suffer from unacceptably long convergence time. Cuckoo search (CS) is one of the fastest and most reliable optimization techniques, making it an ideal option to be used as an MPPT of PV systems under dynamic partial shading conditions. The standard CS algorithm has a long conversion time, high failure rate, and high oscillations at steady state; this paper aims to overcome these problems and to fill this research gap by improving the performance of the CS. The results obtained from this technique are compared to five swarm optimization techniques. The comparison study shows the superiority of the improved CS strategy introduced in this paper over the other swarm optimization techniques.

The presence and evolution of static power converters in electric grids are growing on a daily basis. Starting from the most used voltage source converter (VSC), passing through the use of multilevel converters, the most recent configuration is the so-called modular multilevel converter (MMC). Because of its intrinsic advantages, it is used not only in high-voltage systems but also in low- and medium-voltage ones to interface renewable energy sources such as photovoltaic (PV) panels. Several configurations and maximum power point tracker (MPPT) algorithms have been proposed and analyzed for MMC-PV-based systems. However, when using distributed MPPTs, partial shading conditions cause a problem. The PV panel can be directly connected to the MMC using its dc link or submodule. Based on this configuration, this paper proposes a novel control strategy that tracks both the ac grid current and ac circulating current for a single-phase low-voltage system to obtain the maximum power under any irradiance condition. The effectiveness of the proposed control strategy is demonstrated through time-domain simulation results.

To operate photovoltaic (PV) systems efficiently, the maximum available power should always be extracted. However, due to rapidly varying environmental conditions such as irradiation, temperature, and shading, determining the maximum available power is a time-varying problem. To extract the maximum available power and track the optimal power point under these varying environmental conditions, maximum power point tracking (MPPT) techniques are proposed. The application of MPPT for extracting maximum power plays a crucial role in developing efficient PV systems. These MPPT techniques face several issues and limitations, particularly during partial shading conditions caused by non-uniform environmental conditions. Researchers have been focusing more on mitigating the partial shading condition in PV systems for the last few years due to the need to improve power output and efficiency. This paper provides an overview of MPPTs proposed in the literature for uniform and non-uniform environmental conditions broadly categorized as MPPT-based and circuit-based methods. The MPPT-based methods are classified as conventional, soft computing, and hybrid techniques. A critical analysis of each approach regarding tracking speed, algorithm complexity, and dynamic tracking under partial shading is discussed. The literature shows hybrid strategies provide fast-tracking speed and are efficient with a tracking efficiency of around 99% compared to conventional methods; however, their design and practical implementation are complex. This comprehensive review of MPPT methods aims to provide power utilities and researchers with a reference and guideline to select the best MPPT method for normal operation and partially shaded PV systems based on their effectiveness and economic feasibility.

In this paper, a comparative review for maximum power point tracking (MPPT) techniques based on model predictive control (MPC) is presented in the first part. Generally, the implementation methods of MPPT-based MPC can be categorized into the fixed switching technique and the variable switching one. On one side, the fixed switching method uses a digital observer for the photovoltaic (PV) model to predict the optimal control parameter (voltage or current). Later, this parameter is compared with the measured value, and a proportional–integral (PI) controller is employed to get the duty cycle command. On the other side, the variable switching algorithm relies on the discrete-time model of the utilized converter to generate the switching signal without the need for modulators. In this regard, new perspectives are inspired by the MPC technique to implement both methods (fixed and variable switching), where a simple procedure is used to eliminate the PI controller in the fixed switching method. Furthermore, a direct realization technique for the variable switching method is suggested, in which the discretization of the converter’s model is not required. This, in turn, simplifies the application of MPPT-based MPC to other converters. Furthermore, a reduced sensor count is accomplished. All conventional and proposed methods are compared using experimental results under different static and dynamic operating conditions.

The performance of a photovoltaic system is subject to varying environmental conditions, and it becomes more challenging to track the maximum power point (MPP) and maintain the optimal performance when partial shading occurs. In this study, an enhanced MPP tracking (MPPT) method is proposed utilising the state estimation by the sequential Monte–Carlo (SMC) filtering, which is assisted by the prediction of MPP via an artificial neural network (ANN). A state-space model for the sequential estimation of MPP is proposed in the framework of incremental conductance MPPT approach, and the ANN model based on the observed voltage and current or irradiance data predicts the global MPP to refine the estimation by SMC. Moreover, a quick irradiance change detection method is applied, such that the SMC-based MPPT method resorts to the assistance from ANN only when partial shading is detected. Simulation results show that the proposed enhanced MPPT method achieves high efficiency and is robust to rapid irradiance change.