Simulation and Analysis of Wind-Solar Hybrid Energy Conversion System

Energy crises and the growth of the energy demand have increased the interest in utilizing unconventional power sources. Thus, renewable energy sources have become a topic of interest to mitigate rising energy concerns and cope with increased electricity demand. With remarkable merits including cleanness and abundance, photovoltaic (PV) solar energy systems are a key to solving these issues. The employed inverters should effectively utilize the maximum available power from the PV solar system and transfer this power to the utility grid without posing any further limitations. However, the unequal power generation of different PV systems caused by partial shading (PS) and other PV panel degradation factors leads to a reduction in generation capacity. One of the relatively new solutions to mitigate the mismatch concerns between the PV modules and sub-modules is to extract the maximum power of each sub-module individually. The main objective of this paper is to present a comprehensive review of such PV grid-connected inverters topologies associated with sub-module connection and control. It will classify the PV grid-tied inverters in accordance with the level where the maximum power point tracking (MPPT) system is implemented. A special focus has been placed on sub-module microinverters (MI) in terms of circuit topologies, conversion efficiency, and controller design. This paper provides a comprehensive analysis of employing the distributed MPPT (DMPPT) approach to maximize the power generation of PV systems by mitigating the mismatch issues inside the PV module. The circuit topology, PV system configuration, and MPPT algorithms used for applying DMPPT solutions in PV SMs are discussed in detail in this study.

In renewable system control engineering, Maximum Power Point Tracking (MPPT) techniques are considered as one of the key steps in photovoltaic (PV) system development. Indeed, some MPPT techniques provide high performance under uniform irradiation and temperature conditions. However, their performance significantly degrades when the two preceding climatic conditions suddenly change. Therefore, this paper proposes a new voltage regulation based on Fuzzy Logic Control (FLC) strategy and small-signal model of DC–DC booster converter. The proposed control scheme is mainly based on the indirect incremental conductance (Inc) technique, equipped with an optimal integrator controller (OIC). It is implemented in a PV system, consisting of a DC–DC boost converter connected to a resistive load, with the main objective of ensuring a good MPP tracking behavior for variable operating conditions. To achieve this objective, the proposed MPPT strategy is designed around two stages. First, the smooth reference voltage is ensured by a Single-Input Single-Output (SISO) fuzzy logic controller using the Inc technique based on the MPPT approach. Then, the optimal integrator controller stabilizing the closed-loop system is integrated with optimization of its parameter according to Routh’s criterion for resistive load variations. Simulation results confirm the effectiveness of the proposed MPPT technique compared to some commonly used MPPT approaches. The superiority of the proposal is proven by using efficiency ratio, which rises more than 98% in all case studies. These objectives are ensured for both uniform and critical sudden change in weather conditions, regardless of resistive load variations.

The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed. The PV system consisting of PV array and boost converter with MPPT controller feeds the DC load. The power conversion and panel efficiency are the significant factors to determine the effectiveness of tracking maximum power point. The simulation results show the performance of these controllers on the PV panel output power and the load side output power under changing loads and irradiance. In addition, the comparison of PV panel efficiency of ANN and PSO based MPPT techniques w.r.t changing loads is carried out. Based on the above analysis, PSO based MPPT algorithm marginally outperforms the ANN based MPPT algorithm. Further, the implementation of hybrid MPPT (ANN &PSO) for higher accuracy and tracking capability can be carried out as future work.

Maximum power point tracking (MPPT) strategy is one of the major parameters affecting efficiency in photovoltaic (PV) systems. In this paper, distributed MPPT approaches are compared with central mode MPPT. Advantages and disadvantages of submodule level MPPT technique and module level MPPT approach are shown with simulation studies. Comparisons are made with incremental conductance (IC) algorithm. In this context, non-isolated zeta converter is used as a power processing unit. Effect of the MPPT strategy on the collected energy performance is observed by simulation studies performed in MATLAB/Simulink. It is clear by these studies that energy capture is bigger in submodule level MPPT strategy and module level MPPT with respect to central mode which is seen by simulations. However, central mode MPPT offers cost effective solution because of the low hardware requirements.

In the last decade, there has been a substantial surge in the advancement of research into the maximum power point tracking (MPPT) controller. The MPPT approaches, on the other hand, continue to be in high demand due to the ease and simplicity with which tracking techniques can be implemented on the maximum power point (MPP). Diverse MPPT approaches and their modifications from various literature are categorized and thoroughly explored in this work, which is divided into two sections. The discussions are centered on the primary goal of attaining the most extraordinary feasible MPPT technique that produces the best results at the lowest possible expense. In order to determine which MPPT approaches to use, evaluations from earlier literature are used to guide the decision. In this section, we will examine the evaluation of the MPPT technique in two sections. Previously, in Part I, we explored the MPPT techniques based on constant parameters and trial-and- error. Part II of this article will examine the MPPT technique, which is based on mathematical computation, measurement, and comparison, and the algorithm development that has occurred in recent years. Furthermore, this section’s assessment for selecting MPPT approaches is based on previous literature reviews. To aid with this selection, the following criteria for the MPPT approach are proposed: sensors and analog/digital requirements, costeffectiveness, simplicity, stability, efficiency, and tracking speed. This enables the reader to select the MPPT technique that is most appropriate for their application.

This study offers a thorough examination of a photovoltaic (PV) system using a variety of maximum power point tracking (MPPT) methods, including fuzzy logic control (FLC), adaptive neuro-fuzzy inference systems (ANFIS), perturb and observe (P&O), and artificial neural networks (ANN). Optimizing power extraction from PV systems under various environmental circumstances, including temperature variations and irradiance, is the main goal of these MPPT algorithms. Despite its widespread use and affordability, the P&O algorithm may have performance issues in dynamic circumstances. By using fuzzy logic to adjust to non-linear changes in environmental conditions, FLC improves P&O and offers more dependable and seamless operation. Although they demand a large amount of data and processing power, ANN-based MPPT approaches provide sophisticated capabilities by predicting optimal operating points by learning from historical system actions. By fusing fuzzy logic and neural networks, ANFIS offers a reliable solution that can more accurately adjust in real time to changing circumstances. These algorithms' incorporation into a PV system allows for more flexible and effective power management, guaranteeing peak performance in a range of climatic conditions. By combining many MPPT techniques, hybrid approaches can further reduce the drawbacks of individual approaches and improve the overall dependability and efficiency of PV systems.

Photovoltaic (PV) systems are increasingly used as a sustainable energy source, but their efficiency is significantly affected by environmental factors such as shading and temperature variations. This study proposes a novel Maximum Power Point Tracking (MPPT) algorithm based on the fusion of Support Vector Regression (SVR) and Incremental Conductance (INC) techniques, designed to enhance the performance of PV systems under partial shading conditions. The SVR model is employed to predict the power output of the PV system based on voltage and current measurements, leveraging its capability to handle non-linear relationships with high accuracy. The INC method is then integrated with the SVR predictions to dynamically adjust the operating point of the PV array, ensuring rapid and precise tracking of the maximum power point (MPP). The algorithm was implemented in MATLAB, and comprehensive simulations were conducted to evaluate its performance. Simulation results demonstrate that the SVR-INC fusion algorithm significantly improves the accuracy and stability of MPPT under varying shading conditions. The algorithm effectively reduces the tracking time to the MPP and minimizes power losses caused by partial shading. Compared to traditional MPPT methods, the proposed algorithm shows enhanced robustness and faster convergence, making it highly suitable for practical applications in real-world PV systems. Furthermore, the integration of SVR with INC provides a robust framework for dealing with the complexities of $P V$ power generation. The algorithm’s ability to adapt to changing environmental conditions ensures that the PV system operates at its maximum efficiency. The study also discusses the optimization of SVR parameters and the importance of datadriven approaches in enhancing MPPT performance. In conclusion, the proposed SVR-INC fusion MPPT algorithm offers a promising solution for improving the efficiency and reliability of PV systems under partial shading. The findings highlight the potential of this approach for future advancements in PV technology, contributing to the development of more efficient and resilient solar energy systems..

Los controladores de carga son implementados en varios sistemas electrónicos con el objetivo de proteger y controlar la carga y descarga de una batería; en el caso de los controladores utilizados en sistemas fotovoltaicos autónomos se implementan dos tipos de tecnologías, Pulse Width Modulation (PWM) y Maximum Power Point Tracking (MPPT). En este artículo se compararon dos controladores de carga con diseños originales en sistemas fotovoltaicos con las mismas especificaciones técnicas para determinar el comportamiento de cada uno bajo condiciones ambientales similares. La implementación de ambos controladores de carga se basó en software y hardware con diseños originales, utilizando tecnología PWM y MPPT. Ambos sistemas están compuestos por el controlador de carga, un panel solar de 30 W y una batería de 12 V a 18 Ah; se realizaron las pruebas experimentales de ambos controladores midiendo voltaje y corriente en el panel y en la batería en procesos de carga y descarga, observando que el controlador MPPT tiene una eficiencia promedio mayor que el controlador PWM debido a que el tipo de tecnología implementada influye directamente en la eficiencia, incluso ante valores menos favorables de radiación solar y temperatura ambiente. El controlador PWM es una opción de eficiencia aceptable y además de bajo costo respecto al controlador MPPT. En la implementación de ambos controladores se calcularon tiempos de autonomía similares.

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Changes in climatic circumstances, as well as intermittency, which has a significant impact on the overall energy system output from renewable energy sources (RESs), require the development of control strategies for extracting the maximum power available from RESs. To accomplish this task, several techniques have been developed. An efficient maximum power point tracking (MPPT) technique should be utilized to guarantee that both wind-generation and PV-generation systems provide their full advantages. In this paper, a new MPPT approach with jellyfish search optimization (JSO) is developed; in addition, a unified power-quality conditioner (UPQC) is utilized to enhance the performance of the microgrid (MG) and to solve the power-quality issues for the sensitive load. The MG, which includes a photovoltaic (PV), a wind turbine, and a fuel cell battery, is examined and modeled for uniform and nonuniform wind speed and solar irradiance. A comparison between the developed algorithm and different maximum power tracking algorithms is presented. Additionally, four case studies are carried out to verify the effectiveness of the introduced UPQC in enhancing power-quality problems. The research outcome shows high performance from the developed algorithm when assessed with additional algorithms. MATLAB/Simulink software is utilized for the simulation of the wind, PV, and FC control systems. However, experiment validation tests are given under the same condition of PV irradiation to validate the simulation results. The experimental validation is executed by utilizing the PV module simulation model, threefold, 23 V/2A CO3208-1A with solar altitude emulator CO3208-1B board, and the results are compared to the simulation results.

Due to the Randomness and fluctuation of the photovoltaic (PV) system, Maximum Power Point Tracking (MPPT) algorithm is employed to ensure the maximum power extraction from PV arrays, when it is connected to the grid. The previous studies have indicated that the MPPT algorithm is one of the main causes of interharmonics emission in PV systems. The MPPT algorithm parameters such as perturbation step size inevitably have a strong impact on the interharmonics in the current injected to the grid. In general, a smaller MPPT perturbation step size will reduce the interharmonics emission level, but it will result in poor tracking performance of the MPPT algorithm. Thus, there is a trade-off between the MPPT efficiency and the interharmonics emission in PV system. In this paper, an interharmonics suppression scheme with variable step size MPPT algorithm was proposed. In the suppression scheme, two voltage values near by the MPPT voltage and two tracking step size are preset. When the dc-link voltage of PV is between the two preset voltage values, it means that the PV power near its MPP and the smaller perturbation step size implemented in the MPPT algorithm. Otherwise, the bigger perturbation step size is employed. It can greatly reduce interharmonics emission level and maintain the MPPT efficiency. Simulations have been carried out to verify the effectiveness of the suppression scheme.

This study introduces a novel methodology aimed at minimising total harmonic distortion (THD) in grid-connected photovoltaic (PV) systems (GCPVs) through the implementation of a maximum power point tracking (MPPT) approach based on artificial neural networks (ANN). High THD levels in PV systems can lead to inefficiencies, power quality issues, and potential damage to the grid infrastructure. Although traditional MPPT methods effectively optimise the power output, they often fail to address harmonics. The proposed ANN-based MPPT algorithm improves PV power harvesting while actively minimising the harmonic distortions. The ANN was trained using a comprehensive dataset that included various environmental conditions, ensuring robust performance in diverse operational scenarios. Simulation results demonstrate that the ANN-based MPPT approach significantly reduces THD to below 1% across various irradiance levels, in contrast to the 1.18% to 2.72% observed with conventional methods such as perturb and observe (PO), while simultaneously preserving optimal power output. Reducing harmonic distortion improves the power quality, system efficiency, and lifespan of grid-connected components. This study highlights ANN-based control strategies for addressing the challenge of maximising energy harvesting and maintaining power quality in modern PV systems, offering a solution for the sustainable integration of solar energy into the grid.

The current publication is directed to achieve a high-performance stand-alone PV system having the capability of tracking maximum output power, providing fixed output DC voltage, and attaining efficient system utilization, under different irradiation levels. A new maximum power point tracking (MPPT) approach integrating the incremental conductance algorithm and fuzzy logic control, and enhanced with PI-controller, was proposed to track maximum power. To provide fixed output DC voltage and approaching full system utilization, the PV system is equipped with a battery bank, electrolyzer; as a dump load, and buck-boost converter, with two controllers. The results of the proposed MPPT technique; modified incremental conductance (MINC), are compared with the corresponding results of three prevalently implemented MPPT algorithms: perturbed and observed (P&O), modified variable step-size P&O (VSZ-PO) and the ordinarily incremental conductance (INC). The highest output power, best tracking efficiency and best output power response are achieved by utilizing the proposed MPPT method. The results of the output voltage response and electrolizer on/off states confirm the ability of the PV scheme to provide fixed DC voltage and attain efficient system utilization, under varying irradiances.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study.