Traditional Maximum Power Point Tracking Research Articles

Enhancing photovoltaic system maximum power point tracking with fuzzy logic-based perturb and observe method

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems.

Comparative Analysis of Hybrid Maximum Power Point Tracking Algorithms Using Voltage Scanning and Perturb and Observe Methods for Photovoltaic Systems under Partial Shading Conditions

Partial shading significantly affects the performance of photovoltaic (PV) power systems, rendering traditional maximum power point tracking (MPPT) methods ineffective. This study proposes a novel hybrid MPPT algorithm integrating voltage scanning and modified Perturb and Observe (P&O) techniques to overcome the limitations posed by partial shading. This algorithm has a simple structure and does not require panel information such as the number of panels or voltage due to its voltage scanning feature. To test the proposed algorithm, a grid-connected PV power system with a power of 252.6 kW was created in the MATLAB/Simulink environment. In this power system, six different PS conditions, containing quite challenging situations, were listed in three different scenarios and simulated. The proposed algorithm was compared with the voltage scanning and P&O and voltage scanning and variable-step P&O methods. It was observed that the proposed algorithm has lower power fluctuations compared to the other two traditional methods. Additionally, this algorithm managed to achieve higher efficiency than the other methods in some cases.

Design and implementation of microcontroller-based solar charge controller using modified incremental conductance MPPT algorithm

This paper presents the modeling, design, and implementation of a rapid prototyping low-power solar charge controller with maximum power point tracking (MPPT). The implemented circuit consists of a 60 W photovoltaic (PV) module, a buck converter with an MPPT controller, and a 13.5V-48Ah battery. The performance of the solar charge controller is increased by operating the PV module at the maximum power point (MPP) using a modified incremental conductance (IC) MPPT algorithm. While the traditional IC MPPT approach requires a substantial amount of code and algorithmic steps to keep the PV module at the MPP, the proposed IC achieves the same process with a reduced number of lines of code, owing to its optimized algorithmic approach. By adjusting the duty cycle of the generated Pulse Width Modulation (PWM) signal, maximum power transfer from the PV module is attained during the operation of the battery charging process. The simulation model is configured and tested in Matlab/Simulink environment under different solar data (1000 W/m2, 500 W/m2, 800 W/m2) with constant temperature (25 °C). To validate the simulations, experimental studies are conducted using the developed rapid prototype with the 32-bit embedded microcontroller in the laboratory. According to the simulation and laboratory results, the maximum power tracking performance of the traditional method was found to be 95.51%, while the proposed method achieved a ratio of 96.59% with less steady-state oscillation. The average tracking efficiency has increased by 1.13%. The proposed IC tracks the MPP more accurately and provides maximum available power for battery charging at different solar radiations compared to the traditional IC approach. For low-powered electric devices, the proposed system can be used to provide a charging infrastructure solution.

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

Under partial-shading conditions (PSCs), the output P-V curve of the photovoltaic array shows a multi-peak shape. This poses a challenge for traditional maximum power point tracking (MPPT) algorithms to accurately track the global maximum power point (GMPP). Single intelligent algorithms such as PSO and ABC have difficulty balancing tracking speed and tracking accuracy. Additionally, there is significant power oscillation during the tracking process. Therefore, this paper proposes a new hybrid method called the Cuckoo Search Algorithm and Artificial Bee Colony algorithm (CSA-ABC) for photovoltaic MPPT. The CSA-ABC algorithm combines the local random walk and the global levy flight mechanism of the cuckoo algorithm, by probability selection, to decide whether to group the population, and introduces adaptive weight factors and gravitational mechanisms between adjacent individuals, incorporating an algorithm restart mechanism to track new MPPs in response to changes in the external environment. The algorithm is implemented in MATLAB/Simulink using a photovoltaic power-generation system model. Simulation verification is performed under different PSC scenarios. The results show that the proposed MPPT algorithm is 6.2–78.6% faster than the PSO, CSA, and ABC algorithms and two other hybrid algorithms, with a smaller power oscillation during the tracking process and zero power oscillation during the steady process.

An Improved Perturb and Observe MPPT for Photovoltaic Systems using Fuzzy Step Size

Photovoltaic (PV) systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The output power of these systems relies on solar cell radiation and temperature. To mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. The Maximum Power Point Tracking (MPPT) algorithm is employed to optimize power extraction from PV systems. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely Perturb and Observe (P&O) and Fuzzy Logic Controller (FLC), have been modified. This research work aims to simulate and validate the fuzzy step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using Matlab/Simulink™ for efficient power tracking in PV systems.

Comparative Study of MPPT Techniques in Solar PV Systems: A Comprehensive Review

Abstract: Maximum power point tracking(MPPT) is a very important technique which is employed in photovoltaic systems to extract maximum available power from the Photovoltaic panels under varying operating conditions. This paper presents a comparative study of different MPPT techniques, including traditional methods like Perturb and Observe (P&O), Incremental Conductance (Inc Con), Constant voltage method, as well as intelligent methods like Fibonacci algorithm-based search MPPT and Particle Swarm Optimization (PSO) Technique. The advantages and disadvantages of each technique are discussed in detail, along with their suitability for different applications. The paper concludes by highlighting the limitations of existing techniques and proposing a problem statement for developing a novel MPPT technique that addresses these limitations. Traditional MPPT techniques like Perturb and Observe , Incremental Conductance, and constant voltage method are straightforward to deploy and computationally economical. However, they can encounter issues such as being trapped in local maxima and exhibiting slower tracking rates during swift changes in irradiance. Intelligent MPPT techniques, such as Fibonacci algorithm-based search MPPT, and Particle Swarm Optimization (PSO) Technique are more adaptable to complex operating conditions and have faster tracking speeds, but they are also more computationally complex and expensive to implement. The limitations of existing MPPT techniques include sensitivity to partial shading conditions, limited efficiency under dynamic irradiance conditions, and the requirement for accurate system parameters. To address these limitations, a novel MPPT technique should be developed that is efficient under partial shading and dynamic irradiance conditions, robust to inaccurate system parameters, and has low computational complexity and implementation cost.

A Sensorless MPPT Approach For PV Pumb System Used BLDC Motor

PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

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.

A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm

The design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.

Optimized Maximum Power Point Tracking using Giza Pyramid Construction Algorithm for Photovoltaic Systems

Background: One of the key challenges in maximizing the performance of PV systems is the efficient tracking of the maximum power point (MPP) under varying operational conditions, including changes in solar irradiance and temperature. Accurate MPP tracking is essential for achieving optimal energy conversion efficiency and maximizing the electricity generation potential of the PV array, even during partial shading conditions. Traditional maximum power point tracking (MPPT) algorithms, such as the incremental conductance (INC) method, often struggle to efficiently handle partial shading conditions. As a result, there is a need for more sophisticated and robust optimization techniques that can effectively address this challenge. This study presents a novel and innovative Giza Pyramid Construction (GPC) algorithm to solve the partial shadinginduced MPP tracking problem. Objective: This study aims to apply the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in photovoltaic systems under partial shading conditions, aiming to enhance energy conversion efficiency and overall system performance. Methods: The methodology involves implementing the Giza Pyramid Construction (GPC) algorithm as the core optimization technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The GPC algorithms are utilized to iteratively adjust the duty cycle of the boost converter, enabling efficient power extraction from the PV array under varying shading conditions. The performance of the GPC algorithm is evaluated through simulations in MATLAB/SIMULINK and compared against conventional MPPT methods like INC and DGO techniques. Results: The successful application of the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in PV systems under partial shading led to significantly reduced optimization time, faster settling times, and minimized output ripples. With the proposed GPC MPPT, optimization time is reduced to 41ms, settling time is reduced to 93ms, and ripples are minimized to 0.092%. Conclusion: the Giza Pyramid Construction (GPC) algorithm demonstrates its effectiveness as a robust and efficient maximum power point tracking method in photovoltaic systems, particularly under partial shading conditions. The improved optimization speed, reduced settling times, and minimized output ripples underscore the GPC algorithm's potential to enhance the overall efficiency and reliability of PV systems, paving the way for its practical implementation in real-world renewable energy applications.

Improving Photovoltaic System Performance with Artificial Neural Network Control

Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness.

MPPT Strategy of Waterborne Bifacial Photovoltaic Power Generation System Based on Economic Model Predictive Control

At present, the new energy industry represented by photovoltaics has become the main force to realize the optimization of China’s energy structure and the goal of “double carbon”; with the absence of land resources, the waterborne bifacial photovoltaic has ushered in a new opportunity. Therefore, in order to address the problem that the maximum power point tracking (MPPT) of photovoltaics (PV) could not take into account, the dynamic economic performance in the control process, an economic model predictive control (EMPC), is proposed in this work to realize the MPPT of the waterborne bifacial PV power generation system. Firstly, the model of the bifacial PV module is constructed by combining the ray-tracing irradiance model and considering the effect of water surface albedo on the irradiance absorbed by the module. Secondly, the EMPC controller is designed based on the state-space model of the system to maximize the power generation as the economic performance index, and to solve the optimal input variables time by time to achieve a rolling optimization with the operational requirements of the system itself as the constraints. Thirdly, the MATLAB/Simulink (R2022a) simulation experimental results verify that the EMPC strategy could be utilized to achieve MPPT of the waterborne bifacial PV power generation system, according to the changes of environment. Finally, it is also demonstrated that the bifacial PV power generation system that employed the EMPC strategy outperformed the traditional MPPT algorithm, with respect to both output power tracking velocity and accuracy, and the power generation could be improved by about 6% to 14.5%, which significantly enhances the system’s dynamic process economics.

A stochastic variance reduction gradient-based GSO-ANFIS optimized method for maximum power extraction of proton exchange membrane fuel cell

Proton Exchange Membrane Fuel Cells (PEMFCs) play a pivotal role in the clean energy landscape, yet their efficiency is contingent upon effective power optimization. This paper presents Maximum PowerPoint Tracking (MPPT) control schemes for PEMFCs, focusing on a ground-breaking methodology. Traditional MPPT controllers are instrumental in maintaining optimal performance; however, they often struggle with dynamic operating conditions. In response to this challenge, this research work presents a pioneering MPPT control scheme employing a stochastic variance reduction gradient system. The novelty of this approach lies in its fusion with the Glow Swarm Optimization (GSO) and the Adaptive Neuro Fuzzy Inference System (ANFIS), resulting in a robust hybrid controller. In the pursuit of optimizing the PEMFC system, the proposed GSO-ANFIS controller is subjected to rigorous testing under dynamic variations in both PEMFC temperature and load. Notably, PEMFCs, due to fluctuations in pressure and temperature, exhibit stochastic behaviour, forming a Gaussian surface. In this research, the popular Perturb and Observe (P&O) and Incremental Conductance methods are evaluated alongside the newly introduced GSO-ANFIS model. The proposed GSO-ANFIS controller outperforms its counterparts, showcasing an impressive accuracy level of 89.97%. In contrast, the Artificial Neural Network (ANN) achieves 80.33% accuracy, and the standalone ANFIS controller attains 86.5% accuracy. This disparity underscores the efficacy and potential of the novel hybrid approach, which not only adeptly handles the stochastic nature of PEMFCs but also significantly enhances accuracy in power optimization. This research not only contributes a valuable addition to the field of MPPT control but also offers a promising trajectory for the future development of efficient and reliable PEMFC systems.

Assessment of hybrid MPPT for SPV based high gain cubic boost converter interfaced with reduced switch multilevel inverter for power quality enhancement

A high-gain cubic boost converter (HG-CBC) with hybrid-based maximum power point tracking (MPPT) through a neural network (NN) aided by the P&O technique (HNN-PO MPPT) has been suggested to acquire optimum power from a solar photovoltaic (SPV) model under varying climatic conditions. The SPV’s output is enhanced using the suggested HG-CBC as per the requirement. A detailed comparison of different conventional boost converters (BC) with the suggested HG-CBC is presented, mainly highlighting part count and boost factor (B). Using the MATLAB tool, the functionality of the developed HNN-PO MPPT technique has been examined for constant and different irradiation (G) levels. The hybrid-based MPPT helps quickly attain maximum power point (MPP) with minimum oscillations at the output. The convergence period is very short with high precision in comparison with P&O and NN MPPT. The results are examined between the suggested and traditional MPPT methods in relation to the percentage of oscillations and rise time. The Reduced Switch Multilevel Inverter (RSMLI) is proposed to integrate the SPV with the RL load. The RSMLI is compared with the conventional standard five-level MLI in relation to the quantity of DC sources, diodes, switches, capacitors, and other parts utilized. The suggested MLI involves a reduced switch count, which mitigates the overall losses during switching and hence improves the efficiency of an inverter. MLI switches are controlled using a sine Pulse Width Modulation (PWM) technique. The THD of the output current of a five-level RSMLI is 4.47%, and it falls within the IEEE 519 norm. Hence, output power quality is enhanced.

A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems

For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive optimization algorithms for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems in order to meet the energy demands of grid systems. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s

Design and implementation of 2 KW OFF-GRID photovoltaic systems with maximum power point tracking using PSO algorithm operating under partial shading conditions

This paper presents the design and implementation of a metaheuristic control method for maximum power point tracking (MPPT) methodology in photovoltaic systems under partial shading conditions. To attain maximum efficiency, photovoltaic (PV) panels should operate at their maximum power point (MPP). Therefore, to maximize the power from the PV system, an MPP tracker is inserted between the PV arrays and the load to ensure that the operating point of the system is adjusted to be placed on the MPP. When the radiation distribution over PV panels is uniform, many traditional MPPT techniques can effectively monitor MPP. However, when the PV arrays are partially shaded, more MPPs are displayed, which usually results in the inability to find the overall MPP. To overcome this difficulty, this proposed work presents a particle swarm optimization algorithm-based MPPT scheme for PV systems. This PSO heuristic algorithm-based technique not only ensures the ability to find the global MPP but also gives a simpler control scheme. The feasibility of this proposed method is verified by various partial shading conditions using Matlab. The proposed method is implemented with a 2KW PV system and the power output is compared with the existing system.

Research on Q-Table Design for Maximum Power Point Tracking-Based Reinforcement Learning in PV Systems

Photovoltaic (PV) power generation is considered to be a clean energy source. Solar modules suffer from nonlinear behavior, which makes the maximum power point tracking (MPPT) technique for efficient PV systems particularly important. Conventional MPPT techniques are easy to implement but require fine tuning of their fixed step size. Unlike conventional MPPT, the MPPT based on reinforcement learning (RL-MPPT) has the potential to self-learn to tune step size, which is more adaptable to changing environments. As one of the typical RL algorithms, the Q-learning algorithm can find the optimal control strategy through the learned experiences stored in a Q-table. Thus, as the cornerstone of this algorithm, the Q-table has a significant impact on control ability. In this paper, a novel Q-table of reinforcement learning is proposed to maximize tracking efficiency with improved Q-table update technology. The proposed method discards the traditional MPPT idea and makes full use of the inherent characteristics of the Q-learning algorithm such as its fast dynamic response and simple algorithm principle. By establishing six kinds of Q-tables based on the RL-MPPT method, the optimal discretized state of a photovoltaic system is found to make full use of the energy of the photovoltaic system and reduce power loss. Therefore, under the En50530 dynamic test standard, this work compares the simulation and experimental results and their tracking efficiency using six kinds of Q-table, individually.

Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach

Photovoltaic (PV) systems suffer from partial shade and nonuniform irradiance conditions. Meanwhile, each PV module has a bypass shunt diode (BSD) to prevent hotspots. BSD also causes a series of a peak in the power-voltage characteristics of the PV array, trapping traditional maximum Power Point Tracking (MPPT) methods in local peaks. This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. The results compared the yield power by Tracking the MPP using only GWO, CS, or PSO MPPT techniques and combining them. Results show that in four cases: in case 1) Uniform Irradiation in three patterns (High, Medium, and Low), In case 2) Fixed Nonuniform Irradiation, While In case 3) Slow Dynamic Nonuniform Irradiation and case 4) ) Fast Dynamic nonuniform irradiation. The efficiency (PSO + CS) 97.86%, (PSO + GWO) 97.74%, and (GWO + CS) 98.55% were the highest performers in the case 1 results in (high, medium, and low), respectively. In Case 2, the efficiency (GWO + CS) is 98.62%, and it operates more effectively under fixed nonuniform irradiance. It has the highest efficiency in both Cases 3 and 4, even though its respective PSO + GWO efficiencies are 97.45% and 97.26%. Based on these results, a hybrid mode of merging algorithms based on weather radiation conditions is proposed.