1,721,050 research outputs found
Investigating the Impact of Cracks on Solar Cells Performance: Analysis based on Nonuniform and Uniform Crack Distributions
Full text linkThe paper investigates the detrimental effect of nonuniform and uniform crack distributions over a solar cell in terms of open-circuit voltage (V_oc), short-circuit current density (J_sc), and output power, the latter under a wide range of irradiance conditions. The experimental procedure to detect the cracks relies on electroluminescence imaging, which is nondestructive and requires a relatively low amount of time. The Griddler software is adopted to translate the EL-taken image into V_oc and J_sc maps. The main findings can be summarized as follows: (i) the nonuniformly- and uniformly-cracked cells are both jeopardized in terms of output power; (ii) the loss corresponding to the cell with nonuniform distribution of cracks is increasingly higher than the uniformly-cracked counterpart as the irradiance hitting the cells grows, and (iii) all cells affected by nonuniform cracks are severely damaged in terms of fingers and rear busbar, which concur to limit the maximum output current
Photovoltaic hotspots: a mitigation technique and its thermal cycle
Full text linkIn the rapidly evolving field of solar energy, Photovoltaic (PV) manufacturers are constantly challenged by the degradation of PV modules due to localized overheating, commonly known as hotspots. This issue not only reduce the efficiency of solar panels but, in severe cases, can lead to irreversible damage, malfunctioning, and even fire hazards. Addressing this critical challenge, our research introduces an innovative electronic device designed to effectively mitigate PV hotspots. This pioneering solution consists of a novel combination of a current comparator and a current mirror circuit. These components are uniquely integrated with an automatic switching mechanism, notably eliminating the need for traditional bypass diodes. We rigorously tested and validated this device on PV modules exhibiting both adjacent and non-adjacent hotspots. Our findings are groundbreaking: the hotspot temperatures were significantly reduced from a dangerous 55°C to a safer 35°C. Moreover, this intervention remarkably enhanced the output power of the modules by up to 5.3%. This research not only contributes a practical solution to a longstanding problem in solar panel efficiency but also opens new pathways for enhancing the safety and longevity of solar PV systems
On the Optimal Orientation of Bifacial Solar Modules
Full text linkThis paper presents an accurate simulation strategy to optimize the installation of bifacial photovoltaic modules in terms of orientation and tilt. The tool relies on horizontal irradiance data and ambient temperature taken from PVGIS for assigned times during the day at the selected geographical site and provides the I-V (and P-V) curves at each time. In this analysis, Naples is chosen as a case-study. It is shown that vertical bifacial modules with the front oriented either to West or to East offer energy production comparable to that of a South-oriented monofacial panel tilted by 30°, which represents the conventional optimized configuration and is thus considered as a reference
Fault Detection and Performance Analysis of Photovoltaic Installations
Full text linkThe cumulative global photovoltaic (PV) capacity has been growing exponentially around the world, especially due to the installation of grid connected photovoltaic (GCPV) plants. Fault detection and analysis are important for the efficiency, reliability and safety of solar photovoltaic (PV) systems. Even
This thesis reports the results of the research work conducted to invent novel fault detection algorithms and evaluate their deployment in multiple existing PV installation, and empirically validate their performance.
A major contribution of this thesis is the development of PV fault detection algorithms based on two indicators named power ratio (PR) and voltage ratio (VR). Both ratios are used to identify the type of the fault that occurs in the PV modules, in PV string, and/or in maximum power point tracking (MPPT) unit.
Three AI based algorithms were also used to detect faults in PV modules. The first algorithm uses six regions of the power and voltage ratio in order to detect faults in PV systems. The average detection accuracy for the algorithm is equal to 94.74%. However, Mamdani Fuzzy Logic system has been used to enhance the occurrence of fault detection in the PV installations which resulted in an increase to 99.12%.
The second proposed PV fault detection algorithm detects defective bypass diodes in PV modules using Mamdani Fuzzy Logic. Whereas, a third PV detection algorithm is based on artificial neural networks (ANN) networks. Four different ANN models have been modelled, which can be classified as follows:
- 2 inputs, 5 outputs using 1 hidden layer
- 2 inputs, 5 outputs using 2 hidden layers
- 2 inputs, 9 outputs using 1 hidden layer
- 2 inputs, 9 outputs using 2 hidden layers
The output results for the last ANN network had the highest overall fault detection accuracy of 92.1%.
In this thesis, the development of two hot spot mitigation techniques used in PV modules will be discussed. These techniques are capable of enhancing the output power of PV modules which are affected by hot spots and partial shading conditions. The detection of hot spots was captured using i5 FLIR thermal imaging camera.
Finally the thesis describes the impact of PV micro cracks on the output power of PV modules. A new statistical analysis approach using T-test and F-test was used to identify the significance impact of the cracks on the output power performance of the PV modules. This is developed using LabVIEW software
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Micro cracks distribution and power degradation of polycrystalline solar cells wafer:Observations constructed from the analysis of 4000 samples
Full text linkIn this paper, the impact of Photovoltaic (PV) micro cracks is assessed through the analysis of 4000 polycrystalline silicon solar cells. The inspection of the cracks has been carried out using an electron microscopy, which facilitate the detection of the cracks though the acquisition of both Everhart-Thornley Detector (ETD) and the Back Scatted Electron Diffraction (BSED) image, where it was found that the size micro cracks are ranging from 50 μm to a maximum of 4 mm. Micro cracks have been categorized into two main categories, including cracks in the solar cell front or rear contact. Several remarkable observations have been found, including but not limited to, (i) the output power loss due to micro cracks varies from 0.9% to 42.8%, subject to micro crack type and size, (ii) cracks in solar cells fingers reduce the finger width, resulting an increase in the output power loss by at least 1.7%, and (iii) there is a substantial correlation between PV hot-spots and the presence of micro cracks, while minimum increase in the cell temperature is observed at 7.6 °C
Assessing MPPT Techniques on Hot-Spotted and Partially Shaded Photovoltaic Modules:Comprehensive Review Based on Experimental Data
Full text linkHot-spotting is a reliability problem influencing photovoltaic (PV) modules, where a mismatched solar cell/cells heat up significantly and reduce the output power of the affected PV module. Therefore, in this paper, a succinct comparison of seven different state-of-the-art maximum power point tracking (MPPT) techniques are demonstrated, doing useful comparisons with respect to amount of power extracted, and hence calculate their tracking accuracy. The MPPT techniques have been embedded into a commercial off-the-shelf MPPT unit, accordingly running different experiments on multiple hot-spotted PV modules. Furthermore, the comparison includes real-time long-term data measurements over several days and months of validation. Evidently, it was found that both fast changing MPPT and the modified beta techniques are best to use with PV modules affected by hot-spotted solar cells as well as during partial shading conditions, on average, their tracking accuracy ranging from 92% to 94%. Ultimately, the minimum tracking accuracy is below 93% obtained for direct pulsewwidth modulation voltage controller MPPT technique
Thermal impact on the performance ratio of photovoltaic systems:A case study of 8000 photovoltaic installations
Full text linkInvestigating the thermal impact including the fluctuations of the solar irradiance and ambient temperature of photovoltaic (PV) systems is a topic of great interest by industry and policymakers, due to the limited case studies reported so far by the PV research community. Therefore, this article presents the analysis of 8000 PV systems distributed across England using the well-known metric, monthly performance ratio (PR). The PV systems were operated over five years, while the PR is calculated using the newly developed model by the US national renewable energy laboratory (NREL). Remarkably, it was found that the average monthly PR for all examined PV systems is equal to 85.74%, where the Midlands region in the UK has the highest monthly PR of 88.12%. We have also investigated the seasonal thermal impact on the performance of PV systems, where it was concluded that Spring and Summer seasons intend to have higher monthly PR compared to Autumn and Winter. Finally, a detailed experiment of three different PV modules affected by various hotspots, including cell-based and string-based, will be comprehensively discussed
Defining the best-fit machine learning classifier to early diagnose photovoltaic solar cells hot-spots
Full text linkPhotovoltaic (PV) hot-spots is a reliability problem in PV modules, where a cell or group of cells heats up significantly, dissipating rather than producing power, and resulting in a loss and further degradation for the PV modules’ performance. Therefore, in this article, we present the development of a novel machine learning-based (ML) tool to diagnose early-stage PV hot-spots. To achieve the best-fit ML structure, we compared four distinct machine learning classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbour (KNN), and the discriminant classifiers (DC). Results confirm that the DC classifiers attain the best detection accuracy of 98%, while the least detection accuracy of 84% was observed for the decision tree. Furthermore, the examined four classifiers were also compared in terms of their performance using the confusion matrix and the receiver operating characteristics (ROC)
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