Shading Conditions Research Articles

Multi‐Scale Simulation of Reverse‐Bias Breakdown in All‐Perovskite Tandem Photovoltaic Modules under Partial Shading Conditions

Herein, a multi‐scale simulation approach to quantify the impact of nonuniformities in cell‐level performance on the photovoltaic characteristics of monolithically interconnected large‐area all‐perovskite tandem modules under partial shading conditions is presented, addressing a crucial aspect of the up‐scaling challenge for this promising photovoltaic technology. To this end, current–voltage characteristics of small‐area all‐perovskite tandem solar cells are obtained for dark and illuminated cases from a calibrated optoelectronic device model using drift–diffusion simulation coupled to a quantum transport formalism for the band‐to‐band tunneling underlying the Zener breakdown. These current–voltage curves are computed for varying density of mobile ions and subsequently used as local 1D coupling laws connecting the 2D electrodes in a quasi‐3D large‐area finite‐element simulation approach that then provides the module characteristics under consideration of spatial variation in active area quality related to mobile ion density. The simulation reveals the appearance of localized current hot spots for the case where the shaded cell is strongly reverse biased.

A model for effect of partial shading on PV panels with experimental validation

This paper aims to develop and validate an empirical model to quantify the impact of partial shading on photovoltaic (PV) panel performance. Partial shading, a significant challenge in solar power generation, can drastically reduce energy output, yet predicting its effects remains difficult using conventional models. This study introduces a methodology that models partial shading as an equivalent reduction in solar insolation across the entire panel. The model was derived from empirical data gathered under various shading scenarios, and curve-fitting techniques were employed to establish an empirical relationship between shading patterns and power loss. The proposed model was experimentally validated on a rooftop PV system in Bengaluru, India, and demonstrated precise predictions of power loss under different partial shading conditions. The framework presented can be adapted to accommodate location-specific variables, providing a versatile tool for evaluating and optimizing PV system performance in partially shaded urban environments.

Impact of environmental conditions on the early growth of the endangered Korean fir (Abies koreana E.H.Wilson): insights for conservation and restoration strategies

Environmental factors are crucial elements that significantly influence the growth and development of plants. This study investigated the impact of environmental conditions (light, air temperature, air humidity, etc.) on the early growth of juvenile Korean fir (Abies koreana E.H.Wilson). Using random forest models and scatterplot matrix analyses, we examined the complex relationships between juvenile Korean fir early survival and various environmental factors. Specifically, a one-way ANOVA was conducted to assess the impact of light regulation in shading experiments on the growth of Korean fir seedlings. Additionally, a t-test was performed to compare the growth differences of Korean fir seedlings in two restoration sites with different canopy coverages. The results confirmed that light conditions significantly affect the growth of Korean fir seedlings, with better early growth observed in shaded conditions compared to full sunlight. Furthermore, field restoration site surveys indicated that the growth of height and root collar diameter of restored Korean fir seedlings was better in areas where the canopy coverage of the existing forest was between 60% and 75%. These findings suggest that the prerequisite for the restoration of the endangered Korean fir seedlings is the achievement of optimal light conditions during the early establishment phase.

Comparative analysis of half-cell and full-cell PV commercial modules for sustainable mobility applications: Outdoor performance evaluation under partial shading conditions

This study compares half-cell and full-cell photovoltaic (PV) modules under partial shading conditions relevant to sustainable electric vehicle charging stations. Market-available PV modules are evaluated outdoors at SolarTechLAB in Milan, Italy, with specific consideration given to partial shading caused by a chimney. Two shading scenarios were implemented: one affecting only the upper half of the PV modules and another affecting both halves. This paper comprehensively analyzes and compares the power-voltage curve, global maximum power point, shading losses, and energy yield. The results show that PV modules with Half-Cell technology perform better in partial shading conditions, with an increased energy yield ranging from 11.3% to 20.7%. This significant improvement highlights the potential of Half-Cell technology in optimizing energy production in environments prone to shading, such as electric vehicle charging stations.

Integration of Fuzzy Logic and Neural Networks for Enhanced MPPT in PV Systems Under Partial Shading Conditions

Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At the same time, an artificial neural network (ANN) is taught to guess where the Global Maximum Power Point (GMPP) is most likely to be by looking at patterns of changes in irradiance and temperature from the past. The fuzzy controller fine-tunes the ANN's prediction, ensuring robust and precise MPPT operation. We used MATLAB/Simulink to run a lot of simulations to make sure our proposed method would work. The results showed that combining fuzzy logic with neural networks is much better than using traditional MPPT algorithms in terms of speed, stability, and response to changing shading patterns. This innovative technique proposes a dual-layered control mechanism where the robustness of fuzzy logic and the predictive power of neural networks converge to form a resilient and efficient MPPT system, marking a significant advancement in PV technology.

A Methodology to Optimize PMSM Driven Solar Water Pumps Using a Hybrid MPPT Approach in Partially Shaded Conditions

Solar water pumps are crucial for farmers, significantly reducing energy costs and providing independence from conventional fuels. Their adoption is further incentivized by government subsidies, making them a practical choice that aligns with sustainable agricultural practices. However, the cost of the required solar panels for the chosen power makes it essential to optimize solar water pumping systems (SWPS) for economic viability. This study enhances the efficiency and reliability of permanent magnet synchronous motor (PMSM)-driven SWPS in rural areas using hybrid maximum power point tracking (MPPT) algorithms and voltage-to-frequency (V/f) control strategy. It investigates the sensorless scalar control method for PMSM-based water pumps and evaluates various MPPT algorithms, including grey wolf optimization (GWO), particle swarm optimization (PSO), perturb and observe (PO), and incremental conductance (INC), along with hybrid combinations. The study, conducted using MATLAB Simulink, assesses these algorithms on convergence time, MPPT accuracy, torque ripple, and system efficiency under different partial shading conditions. Findings reveal that INC-GWO excels, providing higher accuracy, faster convergence, and reduced steady-state oscillations, thus boosting system efficiency. The V/f control strategy simplifies control mechanisms and enhances performance. Considering system non-idealities and maximum duty cycle limitations, PMSM-based SWPS achieve superior efficiency and stability, making them viable for off-grid water pumping applications.

A genetic algorithm approach for flexible power point tracking in partial shading conditions

A novel Flexible Power Point Tracking (FPPT) algorithm based on Genetic Algorithms (GAs) is presented in this study. The algorithm is designed to effectively handle fluctuating solar irradiation levels and partial shading scenarios. The main objective is to enhance power output and tracking accuracy under dynamic environmental conditions, based on extensive simulations. The results showed superior performance for power output, tracking accuracy, and convergence speed than traditional techniques. Its adaptability to changing environmental conditions renders it suitable for real-time implementation, thereby contributing to efficient power generation in PV systems. Simulations demonstrate the efficacy of the algorithm in maximizing power extraction and meeting grid-code requirements under dynamic conditions. The comparative analyses underscore the superiority of the GA-based FPPT algorithm, highlighting its potential to significantly improve power point tracking in photovoltaic systems. This shows significance of adaptive algorithms in modern PV systems, particularly for reducing the effects of partial shading on energy yield. More research is required on the refinement of the algorithm's performance for long-term reliability and scalability in larger PV installations fostering advancements in photovoltaic power point tracking toward sustainable energy systems.

An improved takagi - sugeno variable step size perturb and observe MPPT algorithm based lead acid battery charge controller for photovoltaic system

The main objective of photovoltaic system controller is to reduce the price of the kWh compared to traditional fuel-based electricity. Thus, the improvement of simple and low – cost controllers is a major challenge. Perturb and Observe (P&O) is the commonly used algorithm in order to ensure the power maximization of the photovoltaic system, due to its simplicity and effectiveness. However, on the main drawbacks of that algorithm is the fixed step size of perturbation, due to working environmental changes, which require dynamic tuning of the controller parameter. In order to overcome the cited disadvantage, a new Takagi - Sugeno variable step P&O is proposed. Based on the real-time measurements of the solar radiation and temperature, the proposed fuzzy system schedules the appropriate step size based on human expertise. A zero order Takagi – Sugeno with singleton output is proposed. The fuzzification were carried out using triangular and trapezoidal membership functions. The centroid defuzzification method based 25 If -Then rules are adopted. In order to verify the effectiveness of the improved P&O, three scenarios were used. The obtained results show the effectiveness of the proposed controller under shaded conditions and various temperatures.

Deep reinforcement learning using deep-Q-network for Global Maximum Power Point tracking: Design and experiments in real photovoltaic systems

This paper presents a methodology for integrating Deep Reinforcement Learning (DRL) using a Deep-Q-Network (DQN) agent into real-time experiments to achieve the Global Maximum Power Point (GMPP) of Photovoltaic (PV) systems under various environmental conditions. Conventional methods, such as the Perturb and Observe (P&O) algorithm, often become stuck at the Local Maximum Power Point (LMPP) and fail to reach the GMPP under Partial Shading Conditions (PSC). The main contribution of this work is the experimental validation of the DQN agent's implementation in a synchronous DC-DC Buck converter (step-down converter) un-der both uniform and PSC conditions. Additionally, we establish a testing pipeline for DRL models. The DQN agent's performance is evaluated alongside the P&O algorithm. Results consistently indicate the DQN agent's superiority over the P&O algorithm in all simulated scenarios. Although this trend does not entirely replicate in real-world test setups, significant results are observed. Specifically, in PSC sce-narios where the P&O algorithm becomes trapped at an LMPP, the DQN algorithm extracts up to 63.5 % more power than the P&O algorithm. An open repository is available, containing PCB schematic designs and layouts, along with the code used for model training and deployment.

PHYTOCHROME-INTERACTING FACTOR 7 and RELATIVE OF EARLY FLOWERING 6 act in shade avoidance memory in Arabidopsis

Shade avoidance helps plants maximize their access to light for growth under crowding. It is unknown, however, whether a priming shade avoidance mechanism exists that allows plants to respond more effectively to successive shade conditions. Here, we show that the shade-intolerant plant Arabidopsis can remember a first experienced shade event and respond more efficiently to the next event on hypocotyl elongation. The transcriptional regulator PHYTOCHROME-INTERACTING FACTOR 7 (PIF7) and the histone H3K27-demethylase RELATIVE OF EARLY FLOWERING 6 (REF6) are identified as being required for this shade avoidance memory. RNA-sequencing analysis reveals that shade induction of shade-memory-related genes is impaired in the pif7 and ref6 mutants. Based on the analyses of enrichments of H3K27me3, REF6 and PIF7, we find that priming shade treatment induces PIF7 accumulation, which further recruits REF6 to demethylate H3K27me3 on the chromatin of certain shade-memory-related genes, leading to a state poised for their transcription. Upon a second shade treatment, enhanced shade-mediated inductions of these genes result in stronger hypocotyl growth responses. We conclude that the transcriptional memory mediated by epigenetic modification plays a key role in the ability of primed plants to remember previously experienced shade and acquire enhanced responses to recurring shade conditions.

The Effect of Partial Shadings on the Output Power of the Photovoltaic Modules Connected with Different Current and Voltage Characteristics

A mismatch in the output power of photovoltaic (PV) modules in a PV array can occur due to partial shading or a module replacement. Substitution of a module in a PV array with a new one might lead to different current and voltage characteristics between the new and existing modules and result in power losses. The amount of power loss might be increased more if the PV array experiences partial shading. For this reason, this study aims to investigate the effect of partial shadings on the power output of photovoltaic arrays with different current and voltage characteristics. The PV array under test consists of 25 units of solar modules with a total cross-tied (TCT) configuration. There are five shading conditions applied to the test PV array, i.e., the short narrow (ShN), the short wide (ShW), the long narrow (LnN), the long wide (LnW), and the diagonal. A magic square method is applied to reduce the power loss when the PV modules experience partial shading conditions. The results show that the power loss due to partial shadings, either on all identical modules or partially identical, is the same. The most significant power loss occurs in the long comprehensive shading scenario, where 80% of the modules experience shading, which is 41.30%.

Nutritional Quality of Basal Resource in Stream Food Webs Increased with Light Reduction—Implications for Riparian Revegetation

Biofilms are considered a basal resource with high nutritional quality in stream food webs, as periphytic algae are abundant of polyunsaturated fatty acids (PUFAs). PUFAs are essential for growth and reproduction of consumers who cannot or have very limited capacity to biosynthesize. Yet, how the nutritional quality based on PUFA of basal food sources changes with light intensity remains unclear. We conducted a manipulative experiment in mesocosms to explore the response and mechanisms of nutritional quality to shading, simulating riparian restoration. We found a significant increase in PUFA% (including arachidonic acid, ARA) under shading conditions. The increased PUFA is caused by the algal community succession from Cyanobacteria and Chlorophyta to Bacillariophyta which is abundant of PUFA (especially eicosapentaenoic acid, EPA; docosahexaenoic acid, DHA). On the other hand, shading increased PUFA via upregulating enzymes such as Δ12 desaturase (FAD2, EC:1.14.19.6) and 3-ketoacyl-CoA synthase (KCS, EC:2.3.1.199) in the biosynthesis of unsaturated fatty acid elongation pathways. Our findings imply that riparian reforestation by decreasing light intensity increases the nutritional quality of basal resources in streams, which may enhance transfer of good quality carbon to consumers in higher trophic levels through bottom-up effects.

Design and implementation of Type-3 intuitionistic fuzzy logic MPPT controller for PV solar system: Comparative study

The performance of a photovoltaic (PV) solar system is affected by partial shading conditions (PSC) and environmental conditions, such as solar irradiance and ambient temperature, which vary throughout the day. This results in variations in the maximum power point (MPP) on the solar PV output characteristic curve. Therefore, various classical MPP tracking (MPPT) techniques have been used to track the MPP and extract maximum power from PV systems. However, these techniques have drawbacks such as lower stability, increased oscillation around the steady state, and slower convergence to the MPP. To overcome this problem, the newly proposed interval Type-3 intuitionistic fuzzy logic (T3IFL) controller has been proposed. The T3IFL MPPT controller combines the uncertainty of Type-3 fuzzy logic (T3FL) controller with intuitionistic concepts. The T3IFL controller is more accurate and offers faster convergence to the MPP under changing climatic and steady-state conditions than classical techniques and T3FL controller. The T3IFL algorithm provides better performance with excellent MPP tracking by controlling the duty cycle of the DC-DC buck converter. Four cases studied were investigated: uniform radiation conditions, a step change in solar radiation with constant temperature, replacing the battery load with the ohmic load with constant radiation and temperature, and partial shading conditions. Experimental validation of the T3IFL was performed on a DC-DC buck converter using real-time hardware-in-the-loop (HIL). Finally, the simulation and experimental results with comparative studies verified the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with better response.

How Cropping System Influences Robusta Coffee Leaf Gas Exchange Efficiency via Stomatal Regulation

Background: Robusta coffee leaves regulate gas exchange in response to climatic factors like full sun and shaded conditions. To fully understand the reasons behind the variations in photosynthetic rates and water-use efficiency, exploring the underlying mechanisms of stomatal regulation in the maximum photosynthetic efficiency of Robusta coffee trees, especially in a shade condition is necessary. Methods: This study aimed to evaluate how stomatal regulation in Robusta coffee leaves affects gas exchange under different cropping systems. The Robusta coffee trees were grown in four different cropping systems: monoculture (full-sun condition) (S1), intercropped (single row) with rubber trees (S2), intercropped (double spacing) with rubber trees (S3), and intercropped in a mixed orchard (S4). Result: The results revealed that Robusta coffee trees grown between rubber tree rows with closer spacing (S2) exhibited the largest stomata sizes. These stomata sizes had a statistically significant increase in width, length, and area (ranging from 12.04 to 14.74 µm, 19.22 to 25.55 µm, and 202.37 to 296.05 µm², respectively) compared to Robusta coffee grown in S1, S3, and S4 systems. On the other hand, Robusta coffee grown in S1 system displayed the highest stomatal density, ranging from 310 to 540 stomata/mm². Interestingly, coffee trees grown between rubber rows (S3) and in the mixed orchard (S4) exhibited both high rates of photosynthesis (9.18 and 9.35 µmol/m²/s) and photosynthetic water-use efficiency (3.78 and 5.15 mmol/mol). Notably, these values were statistically different from those observed in other planting conditions. Additionally, Robusta coffee grown in S1 system exhibited the highest canopy temperature (ranging from 30.8 to 47.4°C). It also had the lowest Fv/Fm value (ranging from 0.706 to 0.759) compared to other planting conditions. This study indicates that Robusta coffee leaves exhibited varying stomatal responses to changes in light and temperature depending on the cropping system. By understanding how these leaf traits influence adaptation, we could potentially optimize Robusta coffee trees to thrive in full sun and shaded conditions, particularly within intercropping systems.

High-resolution genetic map and SNP chip for molecular breeding in Panax ginseng, a tetraploid medicinal plant

Abstract Ginseng (Panax ginseng) renowned as the king of medicinal plants. Ginseng grows slowly under shade conditions, requiring at least four years to produce a limited number of seeds. Molecular breeding of ginseng faces challenges due to its the tetraploid genome and the absence of an efficient molecular marker system. To overcome these obstacles, we adopted genotyping-by-sequencing to delve into genetic mapping and survey genetic diversity. We constructed a comprehensive genetic map comprising 24 linkage groups, each corresponding to one of the 24 chromosomes in the ginseng genome, based on 1216 non-redundant SNPs obtained from an F2 mapping population. Additionally, 431 103 SNPs were identified from 119 diverse ginseng genotypes. From these, 192 informative subgenome-specific single copy SNPs were selected to develop a SNP chip. The SNP chip was used to genotype a large ginseng collection, encompassing registered cultivars, breeding lines, wild-simulated ginseng, and wild ginseng from various countries and regions. We evaluated the utility of the assay for molecular breeding with 919 ginseng genotypes. This breeder-friendly SNP chip promises versatility, enabling purity assessments of seeds and products, the authentication of species and cultivars, and the determination of homozygosity and homogeneity rates for breeding lines. Genotype data for 1200 ginseng genotypes are now stored in our database. This SNP chip lays the foundation for a molecular breeding in ginseng and will facilitate the breeding process in this medicinal crop.

Solar PV system with maximum power tracking

The paper investigates the state-of-the-art architecture of photovoltaic systems (PVS) by evaluating the performance of the developed maximum power point tracking (MPPT) algorithm of fuzzy particle swarm optimization (FPSO) for temperate continental latitudes. The material of the paper gives an evaluation of traditional MPPT algorithms in relation to the state-of-the-art FPSO algorithm. The study summarizes the problems of On-Grid PV systems and analyzes methods of solving them by combining them with hydrogen technologies. The paper summaries the experience of observations of climatic factors and solar resources on the example of the Russian Federation. Thus, in 2023, the average winter temperature exceeded the norm by 4.7 °C, and the average summer temperature exceeded the norm by 4.9 °C compared to 2022. The paper analyses the level of insolation, which increased by 0.03 kWh/m2 by regions of Russia for the period 2022–2023. The experimental part of the work was carried out at the solar power plant (SPP) «Kalmykskaya» with coordinates 53.422832 north latitude and 55.266895 east longitude. The watt-volt field characteristic of photovoltaic modules (PVM) connected to the inverter was obtained experimentally. When compared, the correlation coefficient between the experimental power (P) and voltage (U) of the panels was found to be higher than that of the ideal panels, 0.933 versus 0.914. The correlation coefficient between the ideal P(U) function and the experimental one is (−0.475). The data for calculating the coefficient of performance (COP) of the implemented MPPT algorithm was also obtained experimentally, which was about 98.7%. The reliability of the data used in the calculations was confirmed by two independent means of measurement, the difference of the obtained results was less than 1%. In the last part of the experiment of this study, the dependence of insolation in a given geographical point on the generated PV field power was evaluated. The correlation coefficient was 0.47, while the inverter output voltage was maintained in the nominal range of (600 ± 20%)V. Thus, the authors of the study experimentally proved the efficiency of using MPPT on FPSO in temperate continental climate with duration of sunshine T = 1850 h per year. The effectiveness of the FPSO algorithm under the conditions of inverter distance from the common point of the DC switching cabinet (DСCC) has been confirmed. The effectiveness of the MPPT algorithm based on FPSO under conditions of partial shading, increased cloud cover and increased air temperature is concluded. Using the description of the current architecture of On-Grid SPP, the authors draw attention to the impossibility of operation of such systems without the presence of voltage in the reference network. In addition, the impossibility of the system operation at the value of PVM power over 1500 kW and at the voltage of panels less than 900 V is noted. To modernize the existing PVS architecture, for the first time, the use of a DCCC and an inverter with implemented MPPT on the FPSO in combination with a hydrogen (H2) production unit and nickel-hydrogen (Ni–H2) batteries is proposed. The researchers propose that when the PVM field voltage is below 900 V and when the PVM field power exceeds 1500 kW, energy can be diverted to H2 generation or to charge Ni–H2 batteries via a DCCC controller. Such an architecture will improve the continuity and efficiency of the PVS, reduce the carbon footprint, and allow the PVS to be used as an industrial uninterruptible power supply (UPS). The authors see the lack of standardization of implemented projects based on the ESG principle as the main problem of alternative energy development.

Sand cat swarm optimization based maximum power point tracking technique for photovoltaic system under partial shading conditions

Maximum power point tracking (MPPT) plays a crucial role in photovoltaic systems (PVS). In partial shading conditions (PSCs), the P-V characteristic curves of PVS exhibit multiple peaks. Traditional MPPT algorithms, like perturbation and observation (P&O), may fall into local maximum power points (LMPP) and fail to identify the global maximum power point (GMPP). To address the drawbacks of conventional optimal search algorithms, this paper introduces a bio-inspired approach named Sand Cat Swarm Optimization (SCSO) for maximizing the power output of individual PVS. The SCSO can mitigate the adverse effects of partial shadows on PVS performance by precisely identifying the GMPP. In comparison to other bio-inspired algorithms, SCSO exhibits lower complexity and higher efficiency by utilizing only one optimization parameter. SCSO’s performance is evaluated in four scenarios: uniform irradiance, complex partial shading conditions, step-varying stochastic irradiance, and gradual irradiance. A comparative analysis is conducted with Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Crow Search Algorithm (CSA), Slap-Swarm Optimization (SSA) and P&O, focusing on factors such as high efficiency, accuracy, convergence time, and implementation simplicity. Simulation results demonstrate that, on average, the tracking time improves by 19.91%, achieving an efficiency of over 98% while maximizing energy yield. Simultaneously, experimental results indicate that the SCSO is capable of tracking to a larger power output in a shorter time, with an average tracking efficiency improvement of 3.16%.

A new solution for tackling mismatching losses in solar PV array via image encryption

Solar photovoltaic systems may undergo changes in power generation due to environmental factors such as partial shadowing, resulting in losses due to mismatch. This can ultimately lead to a decrease in the efficiency of the system's power conversion. While current solutions are both cost-effective and efficient, there is potential for novel approaches to further enhance system performance by improving consistency, power production, and minimizing mismatch losses. To address these limitations, this paper proposes the utilization of a highly efficient generalized Arnold's cat Map technique, commonly used in the image encryption process, to reconfigure the PV array. The effectiveness of these proposed approaches is evaluated in a MATLAB Simulink environment, specifically for a symmetrical (8x8) PV array, under various benchmark conditions such as Long wide; Short wide, Long Narrow, and Short Narrow shading conditions. The results obtained from these evaluations are then compared to prominent strategies such as Total-cross-tied and the recently reported Chaotic Baker's Map reconfiguration techniques. Furthermore, extensive analysis is conducted using different performance indices to gain a comprehensive understanding of the proposed approaches.

A Novel EPSO Algorithm Based on Shifted Sigmoid Function Parameters for Maximizing the Energy Yield from Photovoltaic Arrays: An Experimental Investigation

This paper presents a new method for maximizing the energy production of photovoltaic (PV) arrays, utilizing the Enhanced Particle Swarm Optimization (EPSO) algorithm. In contrast with the classical PSO, the proposed EPSO algorithm employs shifted sigmoid functions in order to adapt the acceleration coefficients and the inertia weight instead of using constant coefficients. The EPSO algorithm proves its effectiveness even in challenging either condition, such as abrupt variations in solar irradiance or instances of partial shading conditions (PSCs). The proposed EPSO-based Maximum Power Point Tracking (MPPT) controller has undergone testing and a comparative analysis alongside well-known metaheuristic algorithms, which include the PSO, Bat Algorithm (BAT), Grasshopper Optimization Algorithm (GOA), and Grey Wolf Optimizer (GWO). The results obtained through simulations consistently demonstrate that the EPSO-based MPPT method surpasses other metaheuristic approaches in terms of energy yield and the speed at which it tracks the Maximum Power Point (MPP) under PSCs. In addition, the proposed EPSO algorithm demonstrates robustness, maintaining consistent tracking of the MPP during sudden solar radiation changes. Experimental validation on a real PV system affirms these findings, demonstrating that the EPSO algorithm surpasses its counterparts by achieving a high MPPT efficiency of approximately 99.19% in a fast-tracking time of 2.4 s while maintaining minimal power oscillations of around 2.04 %.

Enhance Power Generation of PV Array under Partial Shading Conditions by a Lo Shu and Optimal SuDoKu Puzzle Fusion Topology

A novel Lo Shu and Optimal SuDoKu Puzzle Fusion Topology (LS-OSDKP) is presented to disperse shading effects and enhance photovoltaic (PV) array output power under partial shading conditions (PSCs). Incorporating the ancient Chinese Lo Shu magic square puzzle and the Optimal SDKP topologies, the proposed LS-OSDKP is capable of alleviating current mismatch losses, decreasing wiring loss, simplifying the P-V curves of the array, and significantly improving shading dispersion. Under six PSCs, the performance of the proposed topology was evaluated using a 9 × 9 PV array and compared to the total-cross-tied (TCT), Twisted Two-Step, SDKP, Optimal SDKP, and Lo Shu topologies. Simulation results confirmed that the proposed method could extract the highest averaged power of the PV array among the six topologies, with a 10.47% power improvement when compared to the TCT. Additionally, the LS-OSDKP is capable of effectively reducing the wiring length by 29.09% compared to the Lo Shu. Moreover, the proposed method has the highest potential for energy savings and revenue generation. Compared to the conventional TCT configuration, it could improve extracted energy by approximately 9.58% and shorten payback time by approximately 9.06%. Consequently, the advantages of the proposed LS-OSDKP topology include an increase in extracted power, a reduction in wiring loss, and a shorter energy payback period.