Maximum Power Control for Photovoltaic System Using Two Strategies

Aimed at the shortcomings that traditional fuzzy logic control method for solar LED illumination system maximum power point tracking (MPPT) control system, a novel solar LED illumination control strategy based on variable univers adaptive fuzzy PID controller is proposed.The controller can automatically adjusts the variable univers with the change of photovoltaic output power deviation, to realize the solar LED illumination system photovoltaic generation system's maximum power point (MPP) fast and accurate control. A high power LED driving circuit is also designed for maintaining a constant working current.At last,a prototype system is constructed to verify the advantage of the proposed control system,and the experimental results show the control strategy can achieve effective control for solar LED illumination system MPPT and constant current output control,which improved the accuracy and stability of the whole control system,verified the system is feasible and superiority.

-The rapid integration of photovoltaic (PV) systems into modern power networks has intensified the need for efficient control strategies to ensure maximum power extraction, grid stability, and power quality. Variations in solar irradiance, temperature, and grid conditions significantly affect PV system performance, necessitating advanced Maximum Power Point Tracking (MPPT) and inverter control methods. This paper presents a comprehensive review of conventional, intelligent, and optimization-based control techniques employed in grid-connected PV and hybrid microgrid systems. Various MPPT algorithms, including classical methods, soft computing approaches, and metaheuristic optimization techniques, are critically analyzed with respect to tracking accuracy, convergence speed, computational complexity, and robustness under dynamic conditions. Furthermore, inverter control strategies, synchronization methods, and hierarchical control architectures for microgrids are discussed. A generalized control block diagram is presented to illustrate system integration. Finally, key challenges, research gaps, and future directions are highlighted to guide researchers toward next-generation intelligent and resilient PV-based energy systems. Key Words: Photovoltaic System; Maximum Power Point Tracking (MPPT); Grid-Connected Inverter; Microgrid Control; DC–DC Converter; Intelligent Control; Renewable Energy

An efficient fuzzy logic control algorithm for photovoltaic maximum power point tracking under partial shading condition

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

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.

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.

Simulation and experimental design of adaptive-based maximum power point tracking methods for photovoltaic systems

Most studies on power generation systems have focused on individual components, leaving researchers without a comprehensive understanding of integrated systems. This study addresses this gap by developing a fully self-sufficient photovoltaic (PV) system for a 12 V autonomous mobile robot. We calculated the power and energy requirements of the robot to appropriately size the PV panel and designed a DC–DC buck converter. For energy storage, we used a Li-ion battery and a bidirectional current converter to manage the charging and discharging processes. To optimize performance, we implemented Maximum Power Point Tracking (MPPT) using a fuzzy logic-based control method. Simulation results demonstrate that the proposed fuzzy MPPT controller outperforms the classical P&O algorithm by achieving up to 98% MPPT efficiency, reducing power ripple by over 50% , and improving convergence speed by approximately 30% . The system maintains the output voltage regulated within ±1% of the 12 V target across all tested irradiance and load conditions, confirming both high tracking accuracy and robust stability. During optimal irradiance, the system can simultaneously power the robot and charge the battery, while under low-irradiance conditions, the battery ensures uninterrupted operation. These findings confirm that the proposed hybrid PV system enables mobile robots to operate autonomously and reliably regardless of weather conditions, providing a practical solution for continuous power supply in robotic applications.

Application of the DTC control in the photovoltaic pumping system

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.

Recently, real-time simulation of renewable energy sources are indispensible for evaluating the performance of the maximum power point tracking (MPPT) controller, especially in the photovoltaic (PV) system in order to reduce cost in the testing phase. Nowadays, real time PV simulators are obtained by using analog and/or digital components. In this paper, a real-time simulation of a PV system with a boost converter was proposed using only the digital signal processor (DSP) processor with two DC voltage sources to emulate the temperature and irradiation in the PV system. AMATLAB/ Simulink environment was used to develop the real-time PV system with a boost converter into a C-program and build it into a DSP controller TMS320F28335. Besides, the performance of the real-time DSP-based PV was tested in different temperature and irradiation conditions to observe the P-V and V-I characteristics. Further, the performance of the PV with a boost converter was tested at different temperatures and irradiations using MPPT algorithms. This scheme was tested through simulation and the results were validated with that of standard conditions given in the PV data sheets. Implementation of this project helped to attract more researchers to study renewable energy applications without real sources. This might facilitate the study of PV systems in a real-time scenario and the evaluation of what should be expected for PV modules available in the market.

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.

Solar Photo-voltaic (PV) arrays have non-linear characteristics with distinctive maximum power point (MPP) which relies on ecological conditions such as solar radiation and ambient temperature. In order to obtain continuous maximum power (MP) from PV arrays under varying ecological conditions, maximum power point tracking (MPPT) control methods are employed. MPPT is utilized to extract MP from the solar-PV array; high-performance soft computing techniques can be used. In this paper, the proposed hybrid MPPT algorithm is used in the solar-PV system with variable climatic conditions. The performance of the proposed hybrid MPPT algorithm with different membership functions is analyzed to optimize the MPP. Simulation results establish that with the application of MPPT controller such as Perturb and Observe, Fuzzy Logic and a proposed hybrid MPPT for the solar-PV system, the proposed hybrid MPPT controller provides more accurate performance and also reduces the fluctuation about the MPP as compared to other MPPT techniques.

Purpose Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP) is used by implying the nonlinear behavior of I-V characteristics. Different techniques are used regarding maximum power point tracking (MPPT). The paper aims to review the techniques of MPPT used in PV systems and review the comparison between Perturb and Observe (P&O) method and incremental conductance (IC) method that are used to track the maximum power and gives a comparative review of all those techniques. Design/methodology/approach A study of MPPT techniques for photovoltaic (PV) systems is presented. Matlab Simulink is used to find the MPP using P&O simulation along with IC simulation at a steady temperature and irradiance. Findings MATLAB simulations are used to implement the P&O method and IC method, which includes a PV cell connected to an MPPT-controlled boost converter. The simulation results demonstrate the accuracy of the PV model as well as the functional value of the algorithms, which has improved tracking efficiency and dynamic characteristics. P&O solution gave 94% performance when configured. P&O controller has a better time response process. As compared to the P&O method of tracking, the incremental conductance response rate was significantly slower. Originality/value In PV systems, MPPT techniques are used to optimize the PV array output power by continuously tracking the MPP under a variety of operating conditions, including cell temperature and irradiation level.

Photovoltaic (PV) systems are broadly utilized, especially for remote electrification. It is easier for installation and is free of greenhouse gases, so the impacts on the environment are reduced. The temperature and irradiance change during the day, and the circumstances are more dynamic on cloudy days. Hence, attaining maximum power under some environmental conditions is complex and also requires individual techniques for tracking the maximum available power. These approaches are named maximum power point tracking (MPPT) methods. These are designed through power electronic converters, which supply the PV power to the load. In recent years, meta-heuristic-based algorithms have emerged because of their local optima avoidance ability and flexibility. However, the maximum power point (MPP) will not be tracked efficiently due to the constraint factors between some circuit parameters. This limitation has to be solved in this study. Here, MPPT constraint conditions of the PV system with an inverter are found by analyzing its integrated mathematical model. The MPP of a PV system is tracked by diverse techniques. Though these approaches differ in complexity, effectiveness, and convergence speed, cost and sensors are required. With the effect of these challenges, the main intention of this paper is to design and develop a new modified MPPT algorithm for enhancing the efficiency of the power grid connected with a DC-AC single-phase full-bridge inverter and a proportional integral derivative (PID) controller. Here, the parameters of the PID controller are tuned or optimized by the newly improved meta-heuristic algorithm termed modified tunicate swarm algorithm with new condition (MTSA-NC) thus maximizing the energy extraction of the PV system. The search process for newly improved MTSA-NC works well to adapt the MPPT to cope with a "grid-connected" inverter by attaining a faster convergence rate. Here, MTSA-NC is adopted by introducing the fitness-based solutions for updating the swarm behavior of tunicates to maximize the energy efficiency of "grid-connected" inverter systems for PV arrays. In MTSA-NC, the optimal fitness value is known as a food source, which is used for determining the solution updating. Finally, the experimental tests validate the success of the designed scheme in a PV system during uniform irradiance and partial shading conditions.