Partial Shading Conditions Research Articles

Implementation of modified P&O and an adaptive fuzzy logic controller based MPPT tracking system under partial shading and variable environmental conditions

Most of the conventional Maximum Power Point Tracking (MPPT) Algorithms provide the finest efficiency during uniform irradiation conditions, but under variable irradiation or partial shading condition (PSC), the performance deteriorates and the solar PV system is unable to provide the maximum electricity out of the PV Modules. This degradation in performance occurs due to the presence of numerous local maximum power points (LMPP) and a single global maximum power point (GMPP) in the power versus voltage (P-V) characteristics and the perfect tracking of these LMPP is not possible with the prevailing MPPT algorithms. In order to eradicate this shortcoming, we have proposed the implementation of an adaptive Fuzzy Logic Controller (FLC) based on Perturb and Observe (P&O) technique by employing a boost converter with variable resistive load under uniform irradiance condition (UIC), dynamic atmospheric conditions (DAC) and PSC in this article. We also demonstrated the usage of a simple and improved P&O algorithm by employing a buck boost converter for faster tracking time. These two suggested approaches of FLC and P&O incorporate a variable load scenario and a stable load scenario respectively to address the problem of output voltage oscillation in a PV-based system. The FLC is designed to adjust the dynamic change in step size based on the rate of change of the yield power of the PV panel to reduce oscillations in output of the boost converter and the PV system. According to the results of this analysis, the recommended FLC-based P&O (FLC-P&O) algorithm outperforms the proposed P&O algorithm under DAC and PSC with respect to tracking speed, steady-state error, and dynamic response. Apparently, it can be concluded that the proposed FLC-P&O technique can be applicable to real-time systems for reliable and efficient operation of the boost converter and add to the stability and simplicity of the contemporary PV-based systems.

Attenuation of shading loss using a novel solar array reconfigured topology under partial shading conditions

Partial shading conditions (PSC) degrade the maximum power production from the solar arrays. It generates shading losses which is dependent on the quantity of shade spread out on the solar array. This research proposes a novel Grecian square reconfiguration (NGR) for a 4 × 4 solar array to prevent shading issues. This novel technique is extensively compared with other benchmark solar models like total-cross-tied (TCT), kendoku (KDT), odd–even (OER), and skyscraper reconfigurations (SSR). The effectiveness of the proposed NGR is validated under four complex shadings. A thorough comparison of the proposed NGR is made based on several performance parameters like global power (GP), shading loss (SL), fill factor (FF), efficiency (Ƞ), power gain (PG), and execution ratio (ER). The experimental outcome validates the MATLAB/Simulink results, denoting the excellent performance of the proposed NGR over the existing TCT, KDT, OER, and SSR respectively. The proposed NGR magnifies the global power by 30.36%, 9.53%, 5.95%, and 21.38% under corner shading against TCT, KDT, OER, and SSR schemes. Also, under oblique shading, the shading loss is reduced by 72.04%, 91.91%, 37.04%, and 44.68% by the proposed NGR against TCT, KDT, OER, and SSR schemes. The outstanding performance of the proposed NGR against the TCT, KDT, OER, and SSR schemes under the considered shadings depicts its supremacy in handling shading issues effectively.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Development of grey wolf optimization based modified fast terminal sliding mode controller for three phase interleaved boost converter fed PV system

The conventional MPPT method has drawbacks, such as that under partial shading conditions, several peaks occur and identifying the global peak is difficult. It may converge to a local peak and lead to poor conversion efficiency and tracking efficiency. Implementation of a hybrid algorithm by integrating P&O and metaheuristic algorithms can perform better under partial shading conditions. But the tracking speed is low and the response time is longer. To mitigate the issues mentioned above, a new hybrid algorithm has been suggested that integrates GWO and a modified fast terminal sliding mode controller (MFTSMC). The suggested method with three phase ILBC is incorporated into the PV system. The MATLAB tool is employed to experiment with this study. The performance of GWO-MFTSMC is analysed through MATLAB/ SIMULINK and compared with the performance of ANN-FTSMC and PSO-FTSMC algorithm based MPPT techniques. A hardware prototype is developed and tested for 5 × 200 W solar PV modules with the GWO-MFTSMC algorithm. The proposed method conversion efficiency is 99.72% and 96.15% under simulation and hardware realisation, respectively, which is higher than the ANN-FTSMC and PSO-FTSMC methods.

Hybridization of CSA and PSO improves the efficacy of MPPT for solar photovoltaic array with partial shading

AbstractOne critical issue in the tracking systems based on photovoltaic (PV) is how to harvest highest power of the photovoltaic array; particularly when the system is operating in partially shaded conditions (PSCs) or varying irradiances. This study proposes particle swarm optimization (PSO) hybridization and cuckoo search algorithm (CSA) methods for maximum power point tracking (MPPT). The effectiveness of the proposed algorithm is validated and examined under various irradiance patterns. A comparison study in performance has been conducted between the proposed hybrid CSA-PSO method with the conventional P&O and PSO techniques. Several tests have been performed based on numerical simulations utilizing the programming software MATLAB/Simulink. The results demonstrated that the suggested hybrid technique yields smaller tracking time, higher power and greater efficiency than those of other traditional algorithms.

Design of novel differential power processing scheme for small-scale PV array application operating under partial shading conditions: modelling and experimental validation

In this study, an innovative approach known as Series and Parallel Differential Power Processing (SP-DPP) converters has been introduced and applied to a small-scale 2 × 2 PV array configuration. Unlike conventional methods, this approach employs Bidirectional Ćuk DC-DC Converters (BCC) in lieu of bypass diodes within serially connected PV modules, facilitating precise adjustment of module currents. Additionally, the Differential Power Processing (DPP) mechanism, utilising an Inverted-Buck Converter (IBC) topology, replaces traditional blocking diodes across parallel string connections. The paper develops mathematical models for these converters based on their transfer functions in the Continuous Conduction Mode (CCM), establishing new design criteria for SP-DPP systems. This approach ensures a well-behaved transient response and mitigates non-minimum phase response effects. Empirical testing demonstrates the efficacy of the proposed system under various static and dynamic partial shading conditions (PSCs). Furthermore, a model-based control strategy employing P + I controllers are formulated and experimentally validated to optimise the operating conditions of PV modules, enabling them to operate at their Maximum Power Points (MPPs) in varying lighting conditions. Through simulation and experimental results, the study affirms a substantial increase in the total output power of the SP-DPP system (approximately 37–41 %) compared to conventional Tied-Crossties (TCT) array configuration relying solely on bypass and blocking diodes under various static shading conditions, such as Short-Narrow (SN), Short-Wide (SW), Long-Narrow (LN) and Long-Wide (LW). Ultimately, this research highlights the novelty of the proposed SP-DPP scheme over other existing DPP schemes in the literature, in terms of maximum power extraction, cost and complexity under various lighting conditions. This research extends to explore the potential economic viability and global implications of large-scale DPP system implementation.

An improved constant current step-based grey wolf optimization algorithm for photovoltaic systems

In this paper, an improved constant current step based on the grey wolf optimization (CCS-GWO) algorithm for photovoltaic systems is investigated. The development of grey wolf optimization has been widely spread over photovoltaic applications. This method is one of the metaheuristic swarm optimization algorithm groups inspired by an optimum means of chasing prey by grey wolves. The proposed technique applies constant current steps to the pack of wolves (alpha, beta, and omega) by monitoring the average of the internal current step and external current step in order to target the leader alpha wolf position. Moreover, the proposed technique solves the convergence process issues, low convergence speed, and premature local optima problems of the traditional GWO algorithm. This CCS-GWO algorithm accurately tracks the maximum power point from the photovoltaic systems for load charging in different partial shading conditions (PSCs). A number of standard benchmark functions are presented with low average cost functions and their corresponding standard deviation values. The simulation results revealed that the proposed CCS-GWO approach outperforms the existing GWO and GA algorithms in terms of efficiency (98.55%) and tracking time (0.3 s).

Overview of Solar Photovoltaic MPPT Methods: A State of the Art on Conventional and Artificial Intelligence Control Techniques

Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel’s efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems’ classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study.

Optimizing solar panel maximum power point tracking and parasitic parameter extraction in partial shading with Enhanced Slime Mold optimization

Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find near-optimal power points by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). The proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5 S–5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABSO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to the above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.

Modelling and Analysis of High Performance for Solar Power Injection with Distribution Networks

Given the growing emphasis on environmental consciousness, there is a spike in the integration of solar photovoltaic energy into contemporary distribution networks. Interestingly, there is a non-linear relationship between the energy production of solar modules and changing external environmental conditions. In response, this study aims to increase the effectiveness of solar photovoltaic systems (SPVS) by putting out a careful analysis to ascertain the ideal performance criteria for the integration of renewable energy sources into distribution networks. The use of a Maximum Power Point Tracker is crucial to this assessment (MPPT). The model provides important insights into the possible increases in energy efficiency, making it a strong tool in this optimization process. In addition, the research conducts a comparison analysis, examining the distribution system properties in relation to different levels of solar PV system penetration. This aspect of the research illuminates the consequences of harmonic-induced distortions in current and voltage on the feeder networks in the distribution system. The simulation findings clearly show that as the penetration capacity of the PV system increases, so does the amount of harmonic dispersion that is introduced into the network. This suggests that it would be wise to incorporate the photovoltaic (PV) array only as far as the network can support it in order to prevent any possible performance reduction. Using MATLAB/SIMULINK as a computational tool, the research carefully examines the important characteristics from the technical data in order to examine the overall model. Further testing of the model's flexibility and resilience under a range of weather scenarios and partial shade conditions offers a thorough assessment of the model's performance dynamics. Positively, the investigation concluded with results that were satisfactory and confirmed the system's exceptional performance capabilities, which are supported by the MPPT. This result reflects a positive trend toward maximizing the integration of renewable energy sources into distribution networks, which will help to create a more sustainable and environmentally friendly energy landscape.

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

This paper presents a comparative analysis of bioinspired algorithms employed on a PV system subject to standard conditions, under step-change of irradiance conditions, and a partial shading condition for tracking the global maximum power point (GMPP). Four performance analysis and comparison techniques are artificial bee colony, particle swarm optimization, genetic algorithm, and a new metaheuristic technique called jellyfish optimization, respectively. These existing algorithms are well-known for tracking the GMPP with high efficiency. This paper compares these algorithms based on extracting GMPP in terms of maximum power from a PV module running at a uniform (STC), nonuniform solar irradiation (under step-change of irradiance), and partial shading conditions (PSCs). For analysis and comparison, two modules are taken: 1Soltech-1STH-215P and SolarWorld Industries GmbH Sunmodule plus SW 245 poly module, which are considered to form a panel by connecting four series modules. Comparison is based on maximum power tracking, total execution time, and minimum number of iterations to achieve the GMPP with high tracking efficiency and minimum error. Minitab software finds the regression equation (objective function) for STC, step-changing irradiation, and PSC. The reliability of the data (P-V curves) was measured in terms of p value, R, R2, and VIF. The R2 value comes out to be near 1, which shows the accuracy of the data. The simulation results prove that the new evolutionary jellyfish optimization technique gives better results in terms of higher tracking efficiency with very less time to obtain GMPP in all environmental conditions, with a higher efficiency of 98 to 99.9% with less time of 0.0386 to 0.1219 sec in comparison to ABC, GA, and PSO. The RMSE value for the proposed method JFO (0.59) is much lower than that of ABC, GA, and PSO.

A novel on high voltage gain boost converter with cuckoo search optimization based MPPTController for solar PV system

Traditionally, isolated and non-isolated boost converters are used for solar photovoltaic systems (SPV). These converters have limitations such as low voltage gain, less voltage ripples, temperature dependence, high voltage stress across the switches, and being bulky in size. Besides, the solar PV system also has non-linear characteristics between I–V and P–V, and the energy yield potential is affected by partial shading phenomena. Therefore, maximum power point tracking (MPPT) is being added to the SPV system to get the maximum output power under steady and dynamic climate conditions. Although the conventional MPPT has drawbacks such as less accuracy in predicting the MPP under partial shading conditions, low tracking speed, and more ripples, Hence, the research proposes a stackable single switch boost converter (SSBC) with a Cuckoo search MPPT controller for the SPV system. The efficiency of the proposed circuit topology has been compared with conventional boost converters with various MPPTs. Subsequently, the accuracy of tracking true MPPT by CSO is compared with that of PSO and FPNA. The results show, that the CMPPT with CBC has produced more ripples, whereas the BMPPT with SSBC produces ripple-free power under steady conditions. It is also observed that SSBC with BMPPT produces more power than SSBC with TMPPT. The efficiency of SSBC with BMPPT is better than other combinations. Finally, a prototype model has been developed and verified.

A novel algorithm for maximum power point tracking using computer vision (CVMPPT).

The behavior of an illuminated solar module can be characterized by its power-voltage curve. Tracking the peak of this curve is essential to harvest the maximum power by the module. The position of the peak varies with temperature and irradiance and needs to be traced. Under partial shading conditions, the number of peaks increases and makes it more difficult to find the global maximum power point (MPP). Various methods are used for maximum power point tracking (MPPT) that are based on iterations. These methods are time-consuming and fail to work satisfactorily under rapidly changing environmental conditions. In this paper, a novel algorithm is proposed that for the first time, utilizes computer vision to find the global maximum power point. This algorithm, which is implemented in Matlab/Simulink, is free of voltage iterations and gives the real-time data for the maximum power point. The proposed algorithm increases the speed and the reliability of the MPP tracking via replacing analogue electronics calculations by digital means. The validity of the algorithm is experimentally verified.

Maximum power point tracking in PV arrays exposed to partial shading condition irrespective of the array configuration

Maximum power point tracking in PV arrays exposed to partial shading condition irrespective of the array configuration

Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm Optimization.

This study introduces an improved quantum-behavior particle swarm optimization (IQPSO), tailored for the task of maximum power point tracking (MPPT) within photovoltaic generation systems (PGSs). The power stage of the MPPT system comprises a series of buck-boost converters, while the control stage contains a microprocessor executing the biomimetic algorithm. Leveraging the series buck-boost converter, the MPPT system achieves optimal operation at the maximum power point under both ideal ambient conditions and partial shade conditions (PSCs). The proposed IQPSO addresses the premature convergence issue of QPSO, enhancing tracking accuracy and reducing tracking time by estimating the maximum power point and adjusting the probability distribution. Employing exponential decay, IQPSO facilitates a reduction in tracking time, consequently enhancing convergence efficiency and search capability. Through single-peak experiments, multi-peak experiments, irradiance-changing experiments, and full-day experiments, it is demonstrated that the tracking accuracy and tracking time of IQPSO outperform existing biomimetic algorithms, such as the QPSO, firefly algorithm (FA), and PSO.

Modelling and optimisation of PV-PEMEC hydrogen generation system considering partial shading conditions

This study addresses the adverse effects of PSC on both PV output and PEMEC performance. It establishes a PV-PEMEC hydrogen production simulation model considering complex PSC scenarios. The analysis delves into the impacts of PSC on electrolyzer performance, including internal overpotential, internal pressure variation, and electrolysis efficiency. A hybrid GMPPT algorithm based on IGWO and AP&Q is proposed to mitigate PSC effects on the electrolyzer. The algorithm enhances population initialization with chaotic mapping and antagonistic learning mechanisms within the GWO algorithm. Additionally, it incorporates Levy flight and random walk strategies to improve local search capability, coupled with AP&Q to enhance convergence speed. Results indicate significant impacts of PV array PSC on PEM electrolyzer internal electrochemical mechanisms, particularly affecting Ohmic and diffusion overvoltages. Different local shading scenarios cause continuous fluctuations in electrolysis efficiency, more pronounced at lower current densities. PSC induces substantial fluctuations in hydrogen partial pressure and fewer fluctuations in oxygen partial pressure. The IGWO-AP&Q hybrid algorithm exhibits high responsiveness and tracking efficiency, yielding the highest PV-PEMEC electrolysis efficiency. This study effectively integrates complex multi-scenario PSC with PEMEC for solar-powered hydrogen production, laying a solid theoretical foundation for regions with high solar indices.

Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications

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.

Power quality improvement in grid-connected photovoltaic system under partial shading condition

ABSTRACT Solar systems are becoming widely used renewable energy source since photovoltaic cell generates high power output. However, during partial shading conditions (PSC), the existing MPPT techniques failed to determine the actual maximum point of the photovoltaic (PV) cell, and the photovoltaic system is stuck at a local maximum thus output is gradually reduced. The Optimized Grid-Connected Photovoltaic system aims to extract the most power from the solar array under partial shading conditions. It uses an optimisation algorithm named Sovereign Butterfly Optimization Algorithm and a sequence controller to optimize power value selection. The voltage harmonics and phase delay cause the system to be in an unbalanced state, so the proposed model uses an anomaly obliteration filter fusion technique in which the Butterworth filter is utilised for reducing voltage harmonics and removing phase delay completely by avoiding negative sequence by converting three-phase current to dq0 frames. To increase the overall power quality in the photovoltaic system without synchronization problems, a Subsystem Synchronization Control Strategy is applied which effectively synchronizes the active and non-active currents injected into the grid. Simulated results using the proposed model showed high tracking accuracy of 99.23% and minimum power oscillation of 2.53%, thus enhancing the overall power quality.

Tie failure detection technique for total cross tied solar photovoltaic system

Partial Shading in the solar photovoltaic (PV) system leads to more power loss due to mismatch among cells or module’s power generation. The Total Cross Tied (TCT) array design approach is employed for Partial Shading Conditions (PSC) to reduce this issue and boost output power. A tie is used to connect the strings and modules in a solar-powered photovoltaicarray. However, external environmental conditions like strong winds and storms may influence this tie to break. The conventional protection and fault identification technique in the PV system failed to detect this fault. The Solar PV system's entire power generation is drastically impacted by this TCT tie connector problem. Furthermore, during PSC, there is a notable fall in power generation too. To address this, a new technique has been proposed in this work which is capable of precisely identifying the tie interconnection fault in the PV array. A line inspector circuit is used to identify and classify the partial shading and tie interconnection fault. This proposed technique is experimentally verified in the 4 × 4 PV array of 160 W. In all the cases, the proposed algorithm exactly finds the tie interconnection failure. When the diagnosed fault is fixed, the PV array's power output is found to rise by up to 4.5 times.

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.