Micro cracks distribution and power degradation of polycrystalline solar cells wafer: Observations constructed from the analysis of 4000 samples

In 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

70% decrease of hot-spotted photovoltaic modules output power loss using novel MPPT algorithm

The phenomenon of 'hot-spotting' within photovoltaic (PV) panels, where a mismatched cell/cells heats up, leads to reliability and efficiency issues. In this brief, a novel maximum power point tracking (MPPT) algorithm is developed to compensate for hot-spotted PV module effects, thus increasing the output power and improving reliability. The MPPT algorithm implements two mitigation processes; the first to identify the optimum power-voltage curve to track the global maximum power point (GMPP). The second process is to manipulate the output power toward the GMPP through the control of the perturbation step size. In order to verify the appropriateness of the proposed algorithm, multiple hot-spotted PV modules were tested under various environmental conditions. Significantly, the algorithm reduces the hot-spotted PV modules output power loss by at least 70% under all irradiance transition scenarios, slow, medium, and fast

Defining the best-fit machine learning classifier to early diagnose photovoltaic solar cells hot-spots

Photovoltaic (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)

Performance Ratio and Degradation Rate Analysis of 10-Year Field Exposed Residential Photovoltaic Installations in the UK and Ireland

As photovoltaic (PV) penetration of the power grid increases, accurate predictions of return on investment require accurate analysis of decreased operational power output over time. The degradation rate in PV module performance must be known in order to predict power delivery. This article presents the degradation rates over 10 years for seven different PV systems located in England, Scotland, and Ireland. The lowest PV degradation rates of −0.4% to −0.6%/year were obtained at the Irish PV sites. Higher PV degradation rates of −0.7% to −0.9%/year were found in England, whereas the highest degradation rate of −1.0%/year was observed in relatively cold areas including Aberdeen and Glasgow, located in Scotland. The main reason that the PV systems affected by cold climate conditions had the highest degradation rates was the frequent hoarfrost and heavy snow affecting these PV systems, which considerably affected the reliability and durability of the PV modules and their performance. Additionally, in this article, we analyse the monthly mean performance ratio (PR) for all examined PV systems. It was found that PV systems located in Ireland and England were more reliable compared to those located in Scotland

Assessing MPPT Techniques on Hot-Spotted and Partially Shaded Photovoltaic Modules: Comprehensive Review Based on Experimental Data

Hot-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

Fault Detection and Performance Analysis of Photovoltaic Installations

The 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

Potential Induced Degradation in Photovoltaic Modules : A Review of the Latest Research and Developments

Photovoltaic (PV) technology plays a crucial role in the transition towards a low-carbon energy system, but the potential-induced degradation (PID) phenomenon can significantly impact the performance and lifespan of PV modules. PID occurs when a high voltage potential difference exists between the module and ground, leading to ion migration and the formation of conductive paths. This results in reduced power output and poses a challenge for PV systems. Research and development efforts have focused on the use of new materials, designs, and mitigation strategies to prevent or mitigate PID. Materials such as conductive polymers, anti-reflective coatings, and specialized coatings have been developed, along with mitigation strategies such as bypass diodes and DC-DC converters. Understanding the various factors that contribute to PID, such as temperature and humidity, is critical for the development of effective approaches to prevent and mitigate this issue. This review aims to provide an overview of the latest research and developments in the field of PID in PV modules, highlighting the materials, designs, and strategies that have been developed to address this issue. We emphasize the importance of PID research and development in the context of the global effort to combat climate change. By improving the performance and reliability of PV systems, we can increase their contribution to the transition towards a low-carbon energy system

Simultaneous fault detection algorithm for grid-connected photovoltaic plants

In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK

Solar cells micro crack detection technique using state-of-the-art electroluminescence imaging

In this article, we present the development of a novel technique that is used to enhance the detection of micro cracks in solar cells. Initially, the output image of a conventional electroluminescence (EL) system is determined and reprocessed using the binary and discreet Fourier transform (DFT) image processing models. The binary image is used to enhance the detection of the cracks size, position and orientation, principally using the geometric properties of the EL image. On the other hand, the DFT has been used to analyse the EL image in a two-dimensional spectrum. The output image of the DFT consists of structures of all required frequencies, thus improving the detection of possible cracks present in the solar cell. As a result, the developed technique improves the detection of micro cracks in solar cells compared to conventional EL output images

Field Study of Photovoltaic Systems with Anti-Potential-Induced-Degradation Mechanism: UVF, EL, and Performance Ratio Investigations

The potential-induced degradation (PID) of photovoltaic (PV) modules is one of the most extreme types of degradation in PV modules, where PID-affected modules can result in an almost 25% power reduction. Understanding how module defects impact PID is key to reducing the issue. Therefore, this work investigates the impact of an anti-PID inverter on PV modules throughout three years of field operating conditions. We used electroluminescence (EL), ultraviolet fluorescence (UVF), and thermography imaging to explore the varieties of an anti-PID inverter connected to a PV string. It was discovered that a PV string with an anti-PID inverter could improve the output power of the modules by 5.8%. In addition, the performance ratio (PR) was equal to 91.2% and 87.8%, respectively, for PV strings with and without an anti-PID inverter