Shading Patterns Research Articles

Global MPPT optimization for partially shaded photovoltaic systems

The global demand for electrical energy has witnessed a substantial increase, presenting a challenge for power systems worldwide. In addition to technical considerations, the escalating issue of global warming has become a paramount concern in the planning studies of various sectors. The formulation and resolution of a single-objective non-linear optimization problem are carried out, considering different operational scenarios. Recent heuristic algorithms, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimization (GWO) and Chimp Optimization algorithm (ChOA) are employed to address the complexities associated with maximizing power output under partial shading conditions in solar PV systems. The inherent challenges of achieving MPPT under such conditions make conventional analytic approaches computationally intensive. Hence, this study leverages heuristic algorithms to optimize solar PV system performance, providing efficient solutions to the associated optimization problems. The current research work was performed on a test system using a MATLAB/SIMULINK environment and the results are presented and discussed. From the simulation results, it was found that ChOA have shown higher conversion efficiency of 99.63% with maximum power output of 525.13 W when compared to other optimization algorithms for the given shading pattern condition. Further, ChOA offers easy implementation and faster convergence, outperforming established methods in GMPP search by reducing power oscillations and achieving precise MPP convergence.

Litter Dynamics and Nutrient Contributions in Arabica Coffee Agroforestry Systems Under Varied Shade Regimes in the Central Western Ghats, India

Coffee agroforestry systems (CAS) are increasingly recognized as an effective climate change mitigation strategy due to their ability to sequester carbon. However, most studies on CAS have focused on the management and productivity of coffee plants, with limited attention to litterfall dynamics and their contribution to soil nutrients, particularly in Indian coffee plantations. In this study, quantified and compared litterfall dynamics in arabica coffee (Coffea arabica L.) grown under different shade patterns. Litterfall collected from designated quadrants was analyzed using standard soil testing procedures. Results showed significant variation in litterfall across treatments, ranging from 3.43 to 13.54 MT ha-1. The highest litterfall was recorded under exotic species shade with 13.54 MT ha-1, followed by native species shade with 11.68 MT ha-1. In terms of nutrient contributions to the soil, coffee grown under native species shade recorded the highest addition of nitrogen (301.37 kg ha-1) and phosphorus (22.19 kg ha-1), significantly exceeding other treatments. Conversely, potassium addition was highest under exotic species shade (257.30 kg ha-1). The findings suggest that coffee grown under a two-tier mixed shade system, comprising both native and exotic species, benefits from enhanced litterfall dynamics and improved nutrient contributions to the soil compared to unshaded systems. To promote soil health and sustainability, policy incentives should encourage the adoption and maintenance of two-tier mixed shade systems in coffee plantations.

Top-down effects on translucency perception in relation to shape cues.

It is well established that object shape perception significantly influences the perception of translucency. However, how object shape cues such as motion and binocular disparity affect the perception of translucency in rich environments, like virtual reality or real visual environments, remains unclear. This study aims to psychophysically measure the extent to which multiple object shape cues influence the perception of translucency. Additionally, we examined whether top-down factors, such as changes in cognitive attitude caused by the sequence of experiments, affect translucency perception. The results revealed that while motion and binocular disparity enhance translucency perception, this effect is confined to situations where shape cues are poor. Moreover, the effect became particularly pronounced when the experiments began with weak specular reflection stimuli, followed by the experiments using stimuli with specular reflection. In the case of translucent objects without specular reflection, strong shape information cannot be derived solely from shading patterns. These findings thus suggest that top-down factors related to shape modulate the influence of shape cues on translucency perception.

An Approach Based on Golden Eagle Optimization Algorithm for Maximum Power Point Tracking of PV Panel Under Partial Shading Conditions

This study introduces an innovative Maximum Power Point Tracking (MPPT) technique utilizing the Golden Eagle Optimization (GEO) method, specifically designed to enhance the efficiency of photovoltaic (PV) systems under partial shading conditions. Unlike traditional MPPT approaches that struggle with local peaks in power-voltage curves caused by shading, the GEO method leverages the hunting behavior-inspired algorithm to accurately locate the global maximum power point (GMPP). The effectiveness of the GEO MPPT technique is demonstrated through extensive simulations across three diverse case scenarios, each representing different partial shading patterns. In all scenarios, the GEO method outperforms conventional MPPT techniques, showcasing its adaptability and superior performance in challenging conditions. The successful implementation of GEO MPPT leads to substantial improvements in PV panel energy extraction efficiency, even when faced with the complexities of partial shading. This research contributes significantly to the advancement of solar PV systems, enhancing their reliability and performance in real-world environments. By mitigating the impact of partial shading, this work promotes the wider adoption of solar energy as a viable and sustainable power solution.

A Novel Improved Gradient‐Based Optimizer for Single‐Sensor Global MPPT of PV System

Gradient‐Based Optimizer (GBO) is a highly mathematics‐based metaheuristic algorithm that has garnered significant attention since its introduction. It offers several inherent advantages, such as low computational complexity, rapid convergence, and easy implementation. However, GBO has some drawbacks, including a lack of population diversity and a tendency to get trapped in local optima. To address these shortcomings, this research introduces an improved version of GBO (iGBO). In iGBO, introducing the Sobol sequence strategy ensures a higher‐quality initial population and enhances the convergence speed. Additionally, a new modified Local Escaping Operator (LEO) is proposed, which incorporates the sine‐cosine operator and the DCS/Xbest/Current‐to‐2rand strategy. This modified LEO improves the optimization efficiency of GBO and boosts its local search capability, helping to avoid local optima. The superiority of iGBO is thoroughly verified through comparisons with the original GBO and several well‐known and newly developed algorithms on the IEEE CEC’2022 benchmark suite. Furthermore, the proposed approach is applied to extract the photovoltaic system’s global maximum power point (MPP) under shading conditions. Three different shading patterns are considered to assess the reliability of iGBO. The performance of the developed iGBO is compared with several leading algorithms, including Particle Swarm Optimization (PSO), Reptile Search Algorithm (RSA), Black Widow Optimization Algorithm (BWOA), Pelican OA (POA), Chimp OA (ChOA), Osprey OA (OOA), and the original GBO. The results reveal that iGBO‐based MPPT consistently outperforms its competitors in identifying the global MPP under various shading conditions followed by PSO, while RSA performs the least effectively.

Metaheuristic Optimization‐Based Sliding Mode Control With Modified Perturb and Observe for Controlling MPPT of a PV Interfaced Grid Connected System

Energy is always needed more and more as civilization advances. Since the supply of traditional fuels is gradually depleting, renewable energy sources are essential for meeting energy needs. The goal of the research is to maximize the electricity that can be produced from renewable resources. Solar energy performed better than any resource regarding efficiency, cleanliness, and pollution‐free nature. However, the primary drawback of the resource is its erratic nature. The system must integrate the maximum power point (MPP) tracking (MPPT) method to overcome intermittency and produce continuous optimal power. The novelty of this article is the development of a sliding mode MPPT controller for photovoltaic (PV) systems working in sunny and shaded. Also, this article introduces the meta‐heuristic algorithm mountain gazelle optimization (MGO), which is incorporated to optimize the parameters of the sliding mode controller (SMC) to ensure the solar PV (SPV) MPP extraction. The stability of the mentioned control topology is assessed in terms of error parameters. The modified perturb and observe (MPb&O) method is incorporated with MGO, called (MGO‐MPb&O) for performing a better tracking ability and to overcome the inability of conventional Pb&O tracking during the shaded conditions. The suggested approach looks for every maximum point across a lengthy number of cycles to find the global maximum point after introducing (MGO‐MPb&O), and the results of the MATLAB/Simulink show that the algorithm performs well under the arbitrary changes of the physical parameters of the proposed system and ambient scenario. Also, the proposed hybrid (MGO‐MPb&O) is compared with two other hybrid control topologies that are (PSO‐MPb&O) and (cuckoo search algorithm [CSA]‐MPb&O) in terms of maximum power extracted, efficiency, and convergence time of the objective function. Results depict that the proposed algorithm outperforms in every aspect and also justify the robustness of SMC. The proposed algorithm was also tested in various shading fashion (SF) in partial shading conditions for analyzing the transient response. These two performance figures for various transition instances demonstrate that the suggested MPPT algorithm can determine the global MPP for the new shading pattern (SP) when it shifts from a uniform state to a partially shaded condition at 4s (middle of the x‐axis).

Shade watch: Mapping citywide shade dynamics through ray tracing and LiDAR data in Hong Kong's complex 3-D built environment

Shade watch: Mapping citywide shade dynamics through ray tracing and LiDAR data in Hong Kong's complex 3-D built environment

Optimizing Solar PV Array Reconfiguration for Maximum Power Extraction Using Hippopotamus Algorithm under Partial Shading

This research paper presents an innovative approach to maximizing power extraction from solar photovoltaic (PV) arrays under partial shading conditions by employing the Hippopotamus Optimization Algorithm (HOA). Partial shading is a common issue that significantly reduces the efficiency of PV systems by creating multiple local maxima on the P-V curve, thereby challenging conventional Maximum Power Point Tracking (MPPT) methods. To address this, we propose an adaptive reconfiguration strategy for the PV array, optimized using HOA, which successfully moderates the impacts of shading and enhances overall energy yield. The Hippopotamus Optimization Algorithm, inspired by the foraging behavior of hippopotamuses, is utilized for its robust global search capabilities and fast convergence. The algorithm dynamically adjusts the arrangement of the PV module to locate and maintain operation at the global maximum power point. Our methodology involves simulating various shading scenarios and evaluating the performance of HOA-based reconfiguration against traditional MPPT techniques. Simulation results demonstrate a significant improvement in power extraction efficiency, with the HOA-based reconfiguration strategy consistently achieving higher power output compared to conventional methods. Additionally, the proposed system exhibits enhanced adaptability to changing shading patterns, ensuring reliable performance in diverse environmental conditions. The findings highlight the potential of the Hippopotamus Optimization Algorithm as a powerful tool for optimizing PV schemes, particularly in scenarios where shading is inevitable. This study contributes to the advancement of renewable energy technologies by offering a novel solution for improving the efficiency and reliability of solar PV arrays.

A new hybrid swarm intelligence-based maximum power point tracking technique for solar photovoltaic systems under varying irradiations

A new hybrid swarm intelligence-based maximum power point tracking technique for solar photovoltaic systems under varying irradiations

Performance evaluation of different photovoltaic array configurations under partial shading

Performance evaluation of different photovoltaic array configurations under partial shading

Improving solar panel performance using MPPT optimization algorithms

One of the primary challenges faced by solar energy systems is the issue of inconsistent lighting and uneven distribution of sunlight across the surface of solar panels. These conditions can result in a substantial reduction in the power output of the panels and, in some cases, the complete failure of certain solar cells. This paper examines the effects of shading on solar panel performance, focusing on how partial shading impacts energy production and efficiency through both experimental analysis and simulation. The findings emphasize the critical influence of shading patterns and configurations on the overall efficiency of photovoltaic systems. The study highlights the necessity of implementing shading mitigation strategies and optimization algorithms to enhance energy production and ensure the reliability of solar systems operating in environments with varying light conditions. This study showed that the use of optimization algorithms in photovoltaic systems exposed to shading increases their efficiency in terms of energy saving as well as performance stability. The simulation results shows that the GWO algorithm gives a higher performance than the other algorithms in the same partial shading conditions.

Pixel-level Terrain Processing of the Martian Surface from Moderate Resolution Images of Tianwen-1

The moderate-resolution camera (MoRIC) of the Tianwen-1 orbiter, China’s first Mars exploration mission, is a frame-imaging scheme color camera. However, generating a precise digital elevation model (DEM) from these images faces challenges due to the presence of featureless and repetitive regions on the Martian surface. Traditional photogrammetric methods often struggle to achieve pixel-level resolution in these areas. To address this, we employ a shape from shading (SFS) algorithm that leverages shading patterns to reconstruct fine-scale terrain details, optimizing photogrammetric initial DEM. This paper presents a novel global bisection search algorithm, based on the Pearson correlation coefficient to determine the smoothing parameter, a crucial element in the SFS process. Comparisons with higher-resolution data from the Mars Express high-resolution stereo camera and Mars Reconnaissance Orbiter CTX images validate our approach, highlighting the potential of SFS to significantly enhance the scientific value of Tianwen-1 MoRIC data by generating high-resolution, three-dimensional maps of Mars.

Characterizing energy-related occupant behavior towards urban residential buildings in China with statistical database establishment

Characterizing energy-related occupant behavior towards urban residential buildings in China with statistical database establishment

A novel hybrid GMPPT method for PV systems to mitigate power oscillations and partial shading detection

The Maximum Power Point Tracking (MPPT) algorithm plays a crucial role in maximizing the power output of a PV system. While uniform shading conditions (USCs) present challenges related to dynamic changes in irradiance, partial shading conditions (PSCs) introduce the complexity of multiple local maxima and rapidly changing shading patterns. Conventional algorithms often struggle to effectively track the global maximum power point (GMPP) during PSCs leading to power losses and oscillations around the MPP. In contrast, advanced algorithms are more complex and require additional computational time causing the algorithm to unnecessarily scan the entire PV curve during USCs, thereby extending the tracking time. This paper proposes a hybrid approach that combines the Modified Perturb and Observe (MP&O) and Modified Coot Optimization Algorithm (MCOA) methods termed as MP&O-MCOA as a robust real time MPPT algorithm. The MP&O method with trapezoidal rule and adaptive step size is employed under USCs, while the MCOA with only one tuning parameter and fewer random numbers is utilized during PSCs to rapidly identify varying conditions. Experimental validation using a boost converter has demonstrated the effectiveness of the proposed method, achieving the fastest average tracking times of 0.24 s under USCs and 0.73 s under PSCs and highest average tracking accuracy above 99 % for all operating conditions tested. The proposed method has proven to outperform other existing MPPT methods under various weather conditions due to its ability to distinguish between USCs and PSCs at exceptional speed and accuracy.

Weak overcomes strong in sensory integration: shading warps the disparity field

Human perception is often based on the integration of multiple sensory cues, where more statistically reliable cues are considered to have stronger influence on the percept. For example, in three-dimensional shape perception, binocular disparity is considered the most reliable cue at close distances, while pictorial cues like shading are considered less reliable. In three within-subject experiments, we instead show that shading can override disparity, a result that cannot be explained by its measured reliability, cue vetoing or cue promotion. Observers judged the shape of a disparity surface that was combined with different shading patterns. In all tests, shading altered the perceived shape specified by a strong disparity signal in a way that could not be predicted by standard accounts of three-dimensional perception. We find an example of unaccounted for cue cooperation, where shading becomes strong when it interacts with disparity.

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

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

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.

Performance enhancement of PV array using successive ring adder algorithm based reconfiguration techniques

The photovoltaic array’s output is decreased due to increase of mismatch losses (ML) under partial shading condition. Different row currents begin to flow from the PV modules as a result. Therefore, the panels must be reconfigured to minimize the row current differential in order to get the most power possible from the PV panel. This paper suggests Successive Ring Adder Algorithms (SRAA) to minimize the difference of row current. Under various shading patterns, the proposed scheme’s superiority is evaluated and contrasted with series-parallel (SP) and Total-Cross-Tied (TCT) configurations. The numerical outcomes demonstrate the superiority of the suggested algorithm. Additionally, unlike the recently reported reconfiguration techniques, it may be used with both square (9 × 9) and non-square (9 × 6) PV arrays. In comparison to SP and TCT configuration system, the maximum power generation has improved by 4.04% and 9.25%, respectively. In comparison to TCT (30.96%) and SP (37.52%), the ML was obtained with the lowest value i.e. 25.87%. The efficiency for a 9 × 9 PV array is measured at 13.18%, the highest among TCT (12.67%) and SP (12.07%) configurations. For non-squared (9 × 6) PV array arrangement, similar types of enhanced outcomes are produced.

A tutorial on the physics of light and image shading.

This paper provides an overview of the many different ways that light interacts with surfaces in the natural environment to provide useful information for visual perception. It begins with a discussion of how the concept of light has evolved over the course of human history. It then considers a wide variety of optical phenomena including Lambert's laws of illumination, the effects of microscopic surface structure on patterns of reflection, the bidirectional reflectance distribution function, the refraction of transmitted light, chromatic dispersion, thin film interference, sub-surface scattering, the Fresnel effects, indirect illumination from multiple reflections, caustics, and the structure of the light field. The primary goal of this discussion is to provide the necessary background information to help students and young researchers more easily understand the scientific literature on the perception of 3D shape and material properties from patterns of image shading.

A novel Adaptive Jellyfish Search algorithm (AJFS) for maximum power optimization of photovoltaic systems: performance evaluation and analysis

ABSTRACT In solar photovoltaic (SPV) systems, optimizing output under various meteorological conditions relies on the Maximum Power Point (MPP) tracking controller. However, the presence of multiple peaks due to partial shading complicates this tracking process. While traditional and soft computing methods are commonly employed for MPP tracking, they face limitations such as the fixed step size in conventional methods and a lack of randomness in soft computing approaches once they reach a certain MPP. To address these challenges, a unique optimization technique known as the Adaptive Jellyfish Search (AJFS) method has been proposed. This method conducts both global and local searches simultaneously in a single step, enhancing the effectiveness of MPP tracking. To evaluate its performance and resilience, zero, weak, moderate, and strong shading patterns are employed in simulations, with comparisons made against traditional Jellyfish Search (JFS) and Particle Swarm Optimization (PSO) techniques. The newly suggested AJFS approach demonstrates significant improvements over traditional methods. It reduces convergence time by 49% and offers additional motivating features, including zero risk of failure, minimized oscillation in power parameters, and enhanced energy production by 1.5%. Moreover, it enables smooth tracking of the MPP, particularly in dynamically changing shading patterns. Overall, the AJFS method presents a promising solution for efficient MPP tracking in SPV systems, overcoming limitations of conventional approaches and demonstrating superior performance under various shading conditions. Its ability to simultaneously conduct global and local searches in a single step makes it well suited for optimizing energy production in real-world scenarios.