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The purpose of this paper is to design and implement a directly cooled photovoltaic thermal (PV/T) hybrid system.

The research design subjects, instruments and methods that were used to collect data are as detailed in the paper. Two polycrystalline photovoltaic (PV) modules were used in this study.

The directly water-cooled PV module (PV/T) was found to operate better as compared to a naturally cooled module for the first three months. The PV/T initially operated at a higher electrical efficiency for 87 per cent of the day. The monthly energy-saving efficiency of the PV/T was found to be approximately 61 per cent, while the solar utilisation of the naturally cooled PV module M1 was found to be 8.79 per cent and that of M2 was 47.93 per cent.

The major limitation was the continued drop in efficiency after the first three months of the PV/T placed outdoors. The fall in the efficiency was attributed to water ingress.

Direct water cooling of PV modules is possible, only that a better sealing is needed to prevent water ingress.

PV air cooling has been researched on. Use of water as a cooling medium has been carried out using serpentine pipes or riser tube, and no direct water cooling on the back of the module has been researched on.

The purpose of this paper is to present a novel approach to the determination of the maximum power point (MPP) in the photovoltaic system using genetic algorithm (GA). The optimization is realised on two types of photovoltaic (PV) modules: monocrystalline and polycrystalline solar modules, with the same rated peak power (400 Wp) but different electrical output data.

The proposed algorithm is a nature-based algorithm that uses genetic operators such as reproduction, crossover and mutation to realise the search through the investigated area of solutions. To determine the MPP of the PV modules, a two-diode model of a PV cell is used. Based on the input electrical data for the analysed PV module, as well as the mathematical model of the PV, the algorithm can estimate the current and voltage at the MPP for given solar irradiation and cell temperature. The analysis is made for several different irradiations, but in work, the results are presented for irradiations of: 100, 500 and 1,000 W/m² and cell temperatures of 0, 25 and 40 °C.

From the presented results and performed analysis, it can be concluded that GA gives adequate results for both modules and for all working conditions. From the obtained results, it can be concluded that the optimization algorithm performs better when applied to the monocrystalline module works better especially in conditions with larger cell temperature, in comparison with the performance of the optimization algorithm applied to the polycrystalline module. On the other hand, the optimization algorithm applied to the polycrystalline module works better for the other working scenarios with smaller cell temperatures.

From the performed analysis, it can be concluded that the use GA as an optimization tool for the determination of the MPP can be successfully implemented. In addition, to improve the overall performance of the PV system, it is also necessary to forecast the weather conditions of the location where the PV system would be installed to forecast the cell temperature and the solar irradiation. This is necessary to choose the right PV module and inverter for the given location.

An optimization technique using GA as an optimization tool has been developed and successfully applied in the determination of the MPP for a PV system. The results are compared with the analytically determined values as well as with the values given by the producer, and they show good agreement.

The reliability prediction is among the most important objectives for achieving overall system performance, and this prediction carried out by anticipating system performance degradation. In this context, the purpose of this research paper is to development of methodology for the photovoltaic (PV) modules' reliability prediction taking into account their future operating context.

The proposed methodology is framed by dependability methods, in this regard, two methods of dysfunctional analysis were used, the Failure Mode and Effects Criticality Analysis (FMECA) method is carried out for identification of the degradation modes, and the Fault Tree Analysis (FTA) method is used for identification the causes of PV modules degradation and the parameters influencing its degradation. Then, based on these parameters, accelerated tests have been used to predict the reliability of PV modules.

The application of the proposed methodology on PWX 500 PV modules' in different regions of Algeria makes it possible to predict its reliability, taking into account the future constraints on its operation. In this case, the temperature and relative humidity vary from one region to another was chosen as constraints. The results obtained from the different regions confirms the reliability provided by the designer of the Saharan cities Biskra, In Salah, Tamanraset, and affirms this value for the two Mediterranean cities of Oran and Algiers.

The proposed methodology is developed for the reliability prediction of the PV modules taking into account their future operating context and, the choice of different regions confirms or disproves the reliability provided by the designer of the PV modules studied. This application confirms their performance within the framework of the reliability prediction.

This paper aims to present a detailed investigation on the parameter estimation of a photovoltaic (PV) module by using a simplified two-diode model.

The studied PV module in this paper resembles an ideal two-diode model, and to reduce the computational time, the proposed model has a photocurrent source and two ideal-diodes and neglects the series and shunt resistances. Hence, for calculating the unknown parameters, only four parameters are required from the datasheet. Moreover, the studied model is simple and uses an easy modeling approach which is free from complexities.

The performance of the PV module is analyzed under non-standard test conditions by considering partial shading at different shaded levels, and it is found that the model has less computational time and gives accurate results.

The usefulness of this PV model is demonstrated with the help of several illustrative figures, and the performance of the PV module is evaluated.

Parameter identification of photovoltaic (PV) modules plays a vital role in modeling PV systems. This study aims to propose a novel hybrid approach to identify the seven parameters of the two-diode model of PV modules with high accuracy.

The proposed hybrid approach combines an improved particle swarm optimization (IPSO) algorithm with an analytical approach. Three parameters are optimized using IPSO, whereas the other four are analytically determined. To improve the performance of IPSO, three improvements are adopted, that is, evaluating the particles with two evaluation functions, adaptive evolutionary learning and adaptive mutation.

The performance of proposed approach is first verified by comparing with several well-established algorithms for two case studies. Then, the proposed method is applied to extract the seven parameters of CSUN340-72M under different operating conditions. The comprehensively experimental results and comparison with other methods verify the effectiveness and precision of the proposed method. Furthermore, the performance of IPSO is evaluated against that of several popular intelligent algorithms. The results indicate that IPSO obtains the best performance in terms of the accuracy and robustness.

An improved hybrid approach for parameter identification of the two-diode model of PV modules is proposed. The proposed approach considers the recombination saturation current of the p–n junction in the depletion region and makes no assumptions or ignores certain parameters, which results in higher precision. The proposed method can be applied to the modeling and simulation for research and development of PV systems.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

This paper aims to propose a simple, derivative-free novel method named as Nelder–Mead optimization algorithm to estimate the unknown parameters of the photovoltaic (PV) module considering the environmental conditions.

At a particular temperature and irradiation, experimental current-voltage (I-V) and power-voltage (P-V) characteristics are drawn and considered as a reference model. The PV system model with unknown model parameters is considered as the adaptive model whose unknown model parameters are to be adapted so that the simulated characteristics closely matches with the experimental characteristics. A single diode (Rsh) model with five unknown model parameters is considered here for the parameter estimation.

The key advantages of this method are that parameters are estimated considering environmental conditions. Experimental characteristics are considered for parameter estimation which gives accurate results. Parameters are estimated considering both I-V and P-V curves as most of the applications demand extraction of the actual power from the PV module.

The proposed model is compared with other three well-known models available in the literature considering various statistical errors. The results show the superiority of the proposed model with a minimum error for both I-V and P-V characteristics.

The purpose of this paper is to investigate the dark properties as a function of reverse current induced defects. Dark characteristics of solar modules are very essential in the understanding the functioning of these devices.

Reverse currents were applied on the photovoltaic (PV) modules to create defects. At several time intervals, dark characteristics along with surface temperature were measured.

Current-voltage (I-V) and capacitance-voltage (C-V) characteristics furnished valuable data and threshold values for reverse currents. Maximum module surface temperatures were directly related to each of the induced reverse currents and to the amount of leakage current. Microstructural damages, in the form of hot spots and overheating, are linked to reverse current effects. Experimental evidence showed that different levels of reverse currents are a major degrading factor of the performance of solar cells and modules.

These results give a reliable method to predict most of the essential characteristics of a silicon solar cell or a module. Similar test could help predict the amount of degradation or even the failure of PV modules.

Partial shading causes significant power decreases in the PV systems. The purpose of this paper is to address this problem, connectivity regulation is designed to reduce partial shading problems.

In this approach, the partial shading was estimated and dispersed evenly on the whole array by global shade dispersion technique (GSD). The grey wolf algorithm was implemented for the interconnection of arrays by an efficient switching matrix.

After the implementation of the GSD technique using a grey wolf algorithm, the performance under different shading conditions was analyzed using the MatLab simulation tool. The results were compared with total cross-tied (TCT), Su Do Ku and the proposed method of reconfiguration, where the proposed method improves the maximum power of the PV system appropriately.

This methodology uses any size of PV systems.

Replacement of conventional energy systems with renewable energy systems such as solar helps the environment clean and green.

The GSD interconnection scheme using the grey wolf optimization algorithm has proved an improved output performance compared with the existing TCT and Sudoku based reconfiguration techniques. By comparing with existing techniques in literature, the proposed method is more advantageous for reducing mismatch losses between the modules of any size of the PV array with less operating time.

The purpose of this study is to monitor the surface cooling of the photovoltaic (PV) panel and the effect of the dust accumulated on the panel surface on the electrical efficiency remotely and instantaneously.

An autonomous system has been designed that can measure and record the PV surface temperature, the amount of dust on the surface, current, voltage and power values at certain intervals. It can also perform surface cooling and cleaning with water cycle when the temperature and dust amount reach certain threshold values and transmit these values to the user via global system for mobile communications module, Bluetooth module and graphically with a touchscreen liquid crystal display panel. Thus, it is aimed to benefit from PV at the maximum level, and it was installed in Kahramanmaras Sütçü Imam University Faculty of Engineering and Architecture.

An increase in power was observed for PV surface cooling and surface dust removal by 3.78% and 45.99%, respectively.

This system is of vital importance in terms of time and energy-saving, especially for solar plants far from the city center, which are difficult to access because of climatic conditions. In other hand for future studies, it is foreseen that more efficiency gains can be achieved by using artificial intelligence and image processing techniques.