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The integrated photovoltaic‐thermoelectric cooling systems (PV‐TECS) can be used to enhance the performance and life expectancy of commercial PV power plants for sustainable power generation. The objective of the study is to assess the efficacy of PV‐TECS to address these concerns. In this study, computational fluid dynamics/finite element method analysis and experimental investigation of photovoltaic micro‐modules (PVMM‐2) with a thermoelectric cooling system and a reference system without it (PVMM‐1), is carried out under real outdoor conditions. The logged data and infrared thermal imaging analysis results show that thermoelectric cooling is very effective in maintaining a consistent PV back temperature difference of 18.24°C between PVMM‐2 and the reference system, even reaching subzero temperature when the reference module operates close to 60°C. The simulated results are found to be in close agreement with the experimental results (R2 values of 0.83 and 0.94) which allows accurate prediction of system performance under actual solar loading conditions. Further analysis shows that PV‐TECS can be effectively used in photovoltaic power plants for efficiency enhancement with a gain in the range of 1%–22% for a monocrystalline PV module depending on location and type of integration. The study is of interest for further research to develop industrial applications. [ABSTRACT FROM AUTHOR]

A major design challenge for a grid-integrated photovoltaic power plant is to generate maximum power under varying loads, irradiance, and outdoor climatic conditions using competitive algorithm-based controllers. The objective of this study is to review experimentally validated advanced maximum power point tracking algorithms for enhancing power generation. A comprehensive analysis of 14 of the most advanced metaheuristics and 17 hybrid homogeneous and heterogeneous metaheuristic techniques is carried out, along with a comparison of algorithm complexity, maximum power point tracking capability, tracking frequency, accuracy, and maximum power extracted from PV systems. The results show that maximum power point tracking controllers mostly use conventional algorithms; however, metaheuristic algorithms and their hybrid variants are found to be superior to conventional techniques under varying environmental conditions. The Grey Wolf Optimization, in combination with Perturb & Observe, and Jaya-Differential Evolution, is found to be the most competitive technique. The study shows that standard testing and evaluation procedures can be further developed for comparing metaheuristic algorithms and their hybrid variants for developing advanced maximum power point tracking controllers. The identified algorithms are found to enhance power generation by grid-integrated commercial solar power plants. The results are of importance to the solar industry and researchers worldwide. [ABSTRACT FROM AUTHOR]

In this study, an innovative hybrid machine learning‐technique is used for the early skin cancer diagnosis fusing Convolutional Neural Network and Multilayer Perceptron to analyze images and information related to the skin cancer. This information is extracted manually after applying different color space conversions on the original images for better screening of the lesions. The proposed architecture is compared with standalone architecture in addition to some other techniques by commonly used evaluation metrics. HAM10000 dataset is used for training and testing as this data contain seven different skin lesions. The novelty of the proposed hybrid model is the structure of the network which handles structured data (patients' metadata and other useful features from different color spaces related to the illumination, energy, darkness, etc.) and unstructured data (images). The results show an overall 86%, 95% top‐1 and top‐2 accuracy respectively, and 96% area under the curve for the seven classes. The study demonstrates the superiority of the proposed hybrid model with a 2% improvement in the accuracy over the standalone model and a promising behavior as compared to the ensemble techniques. The follow‐up research will include more patient data to develop a skin cancer detection device. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) plants are generally installed at optimum tilt angle for a location or using sun tracking for enhancing energy generation potential. The objective of the present study is to assess the practicability of these techniques for large commercial plants along with determining expected uncertainty in simulated energy yield estimation. In this study, performance analysis of a 19‐MWp (17‐MWac) PV plant is carried out which is installed in India with fixed‐tilt (FT), seasonal adjustable‐tilt (AT), and horizontal single‐axis solar tracking (HSAT) configurations. This study is the first‐time comparative power enhancement analysis of these three different configurations under real outdoor conditions for a single large scale power plant at one location. The operation and maintenance issues of these configurations are also analyzed. A reliability analysis of linear actuator of the industrial solar tracking system is carried out as the actuator is a critical part of the solar tracking system of the power plant. Major results of the study are annual performance ratio 79% (FT), capacity factors = 19% (FT), 20% (AT), and 22% (HSAT) respectively, final yields (Yf) = 4.58 h/day (FT), 4.85 h/day (AT), and 5.28 h/day (HSAT). The energy yield simulations are done on two widely used PV design software: PVSyst and PVSOL and results are analyzed in detail. The accuracy of predicted data is validated and found to be within acceptable limits of uncertainty. The results of this study are compared with previous studies. [ABSTRACT FROM AUTHOR]

Summary: The output power prediction by a photovoltaic (PV) system is an important research area for which different techniques have been used. Solar cell modeling is one of the most used methods for power prediction, the accuracy of which strongly depends on the selection of cell parameters. In this study, a new integrated single‐diode solar cell model based on three, four, and five solar cell parameters is developed for the prediction of PV power generation. The experimental validation of the predicted results is done under outdoor climatic conditions for an Indian location. The predicted power by three models is found close to measured values within 4.29% to 4.76% accuracy range. The comparative power estimation analysis by these models shows that the three‐parameter model gives higher accuracy for low solar irradiance values <150 W/m2, the four‐parameter model in the range of 150 to 500 W/m2, and the five‐parameter model for >500 W/m2. The present model is also compared with other models in literature and is found to be more accurate with less percentage error. The overall results also show that the power produced depends on temperature and solar radiation levels at a particular location. Thus, single solar cell model developed can be used with sufficient accuracy for power forecast of PV systems for any location worldwide. The follow‐up research areas are also identified. [ABSTRACT FROM AUTHOR]