Partial Shading Research Articles

Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains

As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua’s dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua’s solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses.

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

A comprehensive technical analysis on optimal sizing and operating strategy for large-scale direct coupled PV–electrolyser systems, considering PV system faults, degradation and partial shading conditions

The paper presents an investigation of the performance of the proton exchange membrane (PEM) electrolyser directly connected with large-scale PV systems under faulty, degradation and partial shading conditions. With the direct coupled system, the power loss and external price on auxiliary devices are avoided. However, the adaptability of the direct coupled system to fluctuate solar irradiance and ambient temperature is reduced significantly compared to systems with DC-DC converters. To maximise the system efficiency, the optimal electrolyser (ELY) cell configuration is identified using an optimal sizing approach or operating strategy. This paper applied the Particle Swarm Optimisation (PSO) algorithm to find the optimal configuration for the two approaches, namely the Sizing technique and Operating strategy under different PV systems: faults, degradation and partial shading conditions. Additionally, a large-scale system is simulated to describe the characteristics of a large-scale direct coupled PV-electrolyser system with actual operating faults and phenomena. Simulations of the large-scale direct coupling system were conducted using measured irradiance and temperature data for a typical day period. Based on the collected output, it is found that compared to the optimal sizing approach, the optimal operating strategy allows the direct coupled system to increase its coupling efficiency from 0.17% to 4.47%. In contrast, the amount of hydrogen production when applying the two methods witnessed different trends, depending on the type of faults. The differences are even more significantly presented during multiple short-circuit fault scenarios.

Effects of light and water availability on the growth of Aglaia duperreana seedlings.

Aglaia duperreana, a species with a long cultivation history, is of high ornamental value. To understand the growth and photosynthetic changes of A. duperreana seedlings under variable environmental conditions, we conducted an experiment with light intensities adjusted at 70%, 50% and 30%, crossed with three moisture treatments at 70%, 50% and 30% of field capacity, and a control group which maintained 90% light intensity and 90% field capacity. The results showed that both drought stress and shading propensity significantly inhibited the growth of A. duperreana seedlings, with stronger impacts from drought stress. The increments in stem height and ground diameter, net photosynthetic rate, transpiration rate, stomatal conductance, and chlorophyll content were decreased with the maximum declines by 71.4%, 81.2%, 93.2%, 71.5%, 70.6% and 30.4%, respectively. Under severe drought stress (30% of field capacity), partial shading (50% of translucency) appeared to lessen the detrimental effects of drought. The combination of 70% translucency and 70% field capacity was optimal, resulting in higher increments in stem height, leaf area, net photosynthetic rate, transpiration rate, and stomatal conductance. The maximum fluorescence, variable fluorescence, PSⅡ potential activity, and PSⅡ maximum light energy conversion efficiency increased and then decreased with decreasing moisture. These findings suggested that A. duperreana could adapt to drought and shading stress by modulating growth, enhancing chlorophyll content, and adjusting photosynthetic system. Maintaining translucency at 70% and field moisture capacity at 70% could promote photosynthesis, with positive consequences on growth of A. duperreana.

Binary firefly algorithm based reconfiguration for maximum power extraction under partial shading and machine learning approach for fault detection in solar PV arrays

The functioning of a photovoltaic (PV) array in the shadow presents significant issues owing to power loss, which affects the harvested power. One of the greatest promising methods for reducing the effect of shadow over the array is reconfiguration. Therefore, this work proposes a modern metaheuristic Binary Firefly Algorithm (BFA) technique for solving the reconfiguration mechanism of a partial shade PV array. The proposed BFA is tested on a 9 × 9 panel PV array with four established shadow arrangements: short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). The proposed BFA approach yields configurations superior when compared to existing reconfigurations. The best GMP boost obtained with the proposed BFA concerning TCT configuration is 36% in the SW shadow pattern, 30 in the LW arrangement, while the least is 7% in the SN and LN patterns. To evaluate the ability of the proposed system, it is compared with other reconfiguration methods tested across various performance metrics such as fill factor, mismatch power loss, percentage of power loss, and power enhancement. The energy estimates and revenue generation demonstrate that the proposed BFA approach generates 15% more power than the TCT setup and other methods. Furthermore, the Nave Bayes based Machine Learning (ML) approach is applied to detect physical degradation in PV panels. To validate the performance proposed ML method and other techniques are undergone with both faulty and non-faulty conditions. The output of the proposed ML technique-based identification of faults approach is compared to existing techniques. The acquired results supported the proposed BFA with ML's ability and superiority in optimally reconfiguring the shaded array.

Income generation through low cost partial shade poly house

Income generation through low cost partial shade poly house

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.

A novel concept of solar photovoltaic partial shading and thermal hybrid system for performance improvement

Large values from external causes, such as partial shade, can greatly influence output power of PV. The applications of partial shading are frequently utilized in simulation software. However, in this research work, partial shading and the integration of the photovoltaic Thermal (PV/T) Hybrid Solar Panel is implemented, and analysis is done to see how it affects the output power of solar panels under genuine climatic circumstances. Many research investigations have been conducted and researchers continue to look at PV/T systems to enhance their performance. The application is designed to provide information on solar panel output power under normal and partial shading situations. The maximum amount of power that solar panels can generate is 298.50 W. Under typical circumstances, partial shading in a solar panel can result in a maximum power value of 141.13 W, and this partial shading leads the power to increase.

Experimental Investigation of Efficiency Enhancement in Solar Photovoltaic Systems Under Partial Shading Conditions Using Discrete Time Slime Mould Optimization

Experimental Investigation of Efficiency Enhancement in Solar Photovoltaic Systems Under Partial Shading Conditions Using Discrete Time Slime Mould Optimization

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.

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.

A Kriging-Based Partial Shading Analysis in a Large Photovoltaic Field for Energy Forecast

In this paper the use of the kriging estimation method for the study of partial shading in photovoltaic fields is investigated. Different cases of shadowing spatial distribution on the PV field surface, due to different relative positions between the plant and some clouds, are studied. The adopted method gives a spatial interpolation of the shadowing so that its distribution over all the surface can be obtained on the basis of a limited number of experimental data. In all cases the kriging estimates are nearly the same as the observed data, also when a strongly reduced experimental observations are available. The accuracy of the estimates is also confirmed by Q-1 and double kriging cross validation schemes. The followed approach allows a cheaper and simpler characterization of the PV plant output power allowing energy forecast.

Maximum Power Point Extraction in PV Array Under Partial Shading Conditions Using GWO-Assisted Beta Method

This paper presents a hybrid maximum power point tracking (MPPT) algorithm, which combines the grey wolf optimization (GWO) technique and Beta method for extraction of maximum power from a photovoltaic (PV) system operating under partial shading conditions. The proposed algorithm named GWO-Beta-based MPPT uses the GWO technique to achieve the global maximum power point (GMPP), while, simultaneously, the MPPT reference using Beta method is calculated according to the PV voltage and current at GMPP. After that, the MPPT algorithm is switched to operate only with the Beta method that keeps tracked on the MPP reference, guarantying the GMPP in that specific operation point. By means of computational simulation results, the performance of the proposed MPPT algorithm is evaluated taking into account the convergence speed and power oscillations to achieve the GMPP.

Visual Re-Initialization Model Development Methodology for Solving Problems Regarding Metaheuristic Algorithm-Based MPPT Applications

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and “Embedded Coder Support Package for TI C2000 Processors” allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency.

Modeling and Power Quality Enhancement of PV Interfaced with Power Grid System

Recently, photovoltaic (PV) technology has significantly contributed to the utilization of renewable energy sources. It plays a crucial role in addressing the challenges posed by climate change, enhancing energy affordability, ensuring power supply stability, and facilitating energy accessibility. PV technology represents a significant opportunity for addressing the issue of energy access among distant areas that now lack reliable access to electricity. Using a traditional grid is limited due to cost and feasibility constraints. These difficulties are to be addressed in the appropriate timeframe to improve the dependability of the supply. The expansion of the grid into these regions diminishes the overall resilience of the grid system. This leads to a situation where PV systems are integrated into an AC grid with low power quality (PQ) or limited capacity. Nevertheless, the integration of solar power into an AC grid with low capacity. The presence of PQ problems imposes limitations on the levels of penetration. Other factors impose limitations and penetration levels encompass several factors such as non-linear loads, dynamic loads, fluctuating irradiations, and partial shade, among others. This article aims to address the PQ difficulties that arise from a weak electrical infrastructure. The purpose of this compilation is to provide engineers with a readily accessible resource, offering them a distinct advantage in their professional endeavors and scholars engaged in this field of investigation. In this context, the photovoltaic (PV) array is methodically organized into 86 parallel strings. The aforementioned strings are meticulously constructed, consisting of a sequence of seven SunPower SPR-415E solar modules that are intentionally interconnected.

A new metaheuristic-based MPPT controller for photovoltaic systems under partial shading conditions and complex partial shading conditions

A new metaheuristic-based MPPT controller for photovoltaic systems under partial shading conditions and complex partial shading conditions

A Coronavirus Optimization (CVO) algorithm to harvest maximum power from PV systems under partial and complex partial shading conditions

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

The peculiar alterations of the optical and mechanical characteristics of the quartz glass plates with a reinforcing coating under the collision with a hyper-velocity flux of microparticles

This article experimentally investigates the collision of a high-velocity (5–8 km/s) flux of iron microparticles with quartz-glass plates and the effect of such collisions on the optical and mechanical properties of the plates protected by In–Sn–O-based coatings. Such coatings are used to protect the optical systems of spacecraft from the impacts of micrometeoroids and fragments of space debris. To ensure the safety of the optical systems of manned vehicles is crucial for the future of space exploration. This requires special protective materials that can resist external factors.In the present article, thin and thick protective coatings were formed by pulsed reactive magnetron sputtering of a tin-doped (7.5 wt%) indium target.The collision of the particles with the target's surface forms craters. The surface density of the craters ρ depends on the thickness of the coating and its physical and mechanical properties. The increased thickness up to ∼6 μm decreases the crater density 3.3 times, which is due to increased elastic modulus and crack resistance and decreased microhardness of the coated glass. For the quartz-glass specimens without the protective coating, the bombardment by the high-speed flux of iron microparticles decreases the visible-region light transmittance by ~ 6 %. The bombardment of quartz-glass specimens with thin protective In–Sn–O coating reduces the light transmittance to a lesser extent, while there is no reduction in the case of a thick coating. The transmittance linearly depends on the crater density and is inversely proportional to the total area occupied by the craters on the surface under study. This effect is due to the partial shading of the glass working area when rays of light pass through the zones occupied by the craters.

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions

At present, fossil fuel-based power generation systems are reducing drastically because of their less availability in nature. In addition, it produces hazardous gasses and high environmental pollution. So, in this work, the solar natural source is selected for generating the electricity. Due to the nonlinear behavior of PV, achieving maximum voltage from the Photovoltaic (PV) system is a more tough job. In this work, various hybrid optimization controllers are studied for tracing the working power point of the PV under different Partial Shading Conditions. The studied hybrid optimization MPPT methods are equated in terms of oscillations across MPP, output power extraction, settling time of the MPP, dependency on the PV modeling, operating duty value of the converter, error finding accuracy of MPPT, algorithm complexity, tracking speed, periodic tuning required, and the number of sensing parameters utilized. Based on the simulative comparison results, it has been observed that the modified Grey Wolf Optimization based ANFIS hybrid MPPT method provides good results when equated with the other power point tracking techniques. Here, the conventional converter helps increase the PV source voltage from one level to another level. The proposed system is investigated by using the MATLAB/Simulink tool.

Comprehensive analysis of line losses and valuation of energy savings in optimized photovoltaic array subjected to partial shadings

In this manuscript, an attempt is made to reconfigure the conventional total cross-tied (TCT) array through a novel optimized SuDoKu (OPSDK) static reconfiguration technique. This approach helps to reduce the line losses occurring due to increased wire length and simultaneously diminishes the adverse effects of accumulated shadings in close vicinity by diffusing it across the photovoltaic array (PVA). The MATLAB/Simulink platform is used to study the variations in the performance of various existing topologies and the proposed OPSDK PVA model under various shading scenarios such as bottom-right corner, bottom-left corner, top-right corner, top-left corner and drifted-locus centre. The performance is monitored with regard to the specific characteristics viz., % fill-factor, mismatch power losses, % power loss, % efficiency, global maximum power point, maximum voltage, maximum current, local maximum power points (LMPPs), number of LMPPs, misleading power loss, current loss, voltage loss, % execution ratio, % power improvement, % thermal voltage, % power enhancement ratio, line loss and annual energy saving. Theresults show that the proposed OPSDKmethod generates 28.12852 kWh energy units per day, which is maximum when compared to other five topologies viz. SuDoKu (SDK), futoshiki SuDoKu (FSDK), skyscraper SuDoKu (SKYSDK), modified SuDoKu (MSDK), ancient chinese magic square (ACMS). Consequently, the annual income generatedby [9×9] SDK, FSDK, SKYSDK, MSDK, ACMS and OPSDK PVAs is ₹ 150,839.54, ₹ 141,514.52, ₹150,108.08, ₹ 150,751.94, ₹ 150,249.33 and ₹154,003.72, respectively, signifying that OPSDK PVA produces highest income. The extensive analysis signifies that the proposed OPSDK topology succeeds in reducing line losses by 25.62 %, 0.00 %, 37.98 %, 33.43 % and 35.49 % in comparison to SDK, FSDK, SKYSDK, MSDK and ACMS, respectively. The mismatch power losses in power output are also reduced by 643.1 W as compared to the other configurations. The OPSDK technique, thus, proves to be one of the most efficient, effective, optimised and sustainable PVA reconfiguration technique with good power savings and greater economic returns/viability.