Parameters Of Photovoltaic Module Research Articles

End-to-end learning of representative PV capacity factors from aggregated PV feed-ins

Energy system models rely on accurate weather information to capture the spatio-temporal characteristics of renewable energy generation. Whereas energy system models are often solved with high abstraction of the actual energy system, meteorological data from reanalysis or satellites provides rich gridded information of the weather. The mapping from meteorological data to renewable energy generation usually relies on major assumptions as for solar photovoltaic energy the photovoltaic module parameters. In this study, we show that these assumptions can lead to large deviations between the reported and estimated energy, as shown for the case of photovoltaic energy in Germany. We propose a novel gradient-based end-to-end framework that can learn local representative photovoltaic capacity factors from aggregated PV feed-ins. As part of the end-to-end framework, we compare physical and neural network model formulations to obtain a functional mapping from meteorological data to photovoltaic capacity factors. We show that all the methods developed have better performance than commonly used reference methods. Both physical and neural network models have much better performance than reference models whereas operational use cases may prefer the neural network due to higher accuracy while interpretable, physical models are more suited to academic settings.

Estimation and monitoring of grid connected PV system generation

This paper presents a new method to identify the operating performance parameters of a photovoltaic (PV) generator and a grid connected inverter. This method has been applied to the behavior modeling and simulation of the grid connected PV system at the Centre de Developpement des Energies Renouvelables (CDER) in Algeria. The PV module (PVM) parameters has been identified in static with outdoor measurements of I-V curves in order to model the PV array generation. Using the Linear Reoriented Coordinates Method (LRCM), the prediction of maximum power point (MPP) of the PV array has been performed without using complicate approximations or Taylor series. In order to estimate the ac output power, a PV inverter performance model based on empirical relationship was used. However, an accurate identification of the inverter performance parameters is performed using an optimization algorithm, named Levenberg-Marquardt (LM) algorithm, and a moutput current and voltage. The simulation results were achieved through MATLAB/Simulink environment, for the setting and the testing of the models. The results show a good agreement between the measured and estimated results based on the identified parameters. The integration of the proposed method and the presented model present a simple way to estimate the expected system performance with a high degree of accuracy. This solution allows a development of automatic supervision, energy generation prediction and fault detection procedure.

A Comparative Study on Accurate Parameter Estimation of Solar Photovoltaic Models Using Metaheuristic Optimization Algorithms

Fossil fuel prices are increasing day by day due to growing demand and resource limitations. This situation shifts conventional electricity generation to renewable power generation. Among renewables, solar photovoltaic (PV) energy is very promising as the third fastest growing one. However, PV system modeling requires the estimation of unknown parameters of PV cells and modules. This problem remains a compelling task due to its multi-dimensional, multi-model and non-linear characteristics. In order to contribute to the solution of this optimization problem, this work makes a detailed comparison of artificial hummingbird algorithm, artificial rabbits optimization, enhanced Jaya algorithm, flow direction algorithm and artificial gorilla troops optimizer for determining the unknown parameters of PV models. Experimental results demonstrate that artificial hummingbird algorithm in single diode modeling of PV cell, flow direction algorithm in double diode modeling of PV cell and artificial rabbits optimization in single diode modeling of PV module are found to be capable of estimating accurate and efficient design coefficients for PV systems. In addition, the mentioned metaheuristic algorithms show reasonable convergence characteristics for the corresponding PV models.

Parametric design of a residential building system through solar energy potential: the case of Guelma, Algeria

To evaluate the potential of retrofitting roofs of residential buildings in Algeria with building integrated photovoltaics (BIPV). A case study of a family residential building in the city of Guelma, was selected to receive building-integrated photovoltaic (BIPV) systems as a retrofit to their roofs using Rhinoceros software and plugins Grasshopper, Ladybug, and Honeybee, adding photovoltaic panels on the roof taking into account the study of solar radiation analysis on the building, and meteorological data from Meteonorm Software 7.3. The method used, is based on measured data for final energy consumption (yearly average of 14600 kWh), building morphology, and photovoltaic module parameters. The results of the renovation of roofs with building-integrated photovoltaic solutions are presented for the existing residential building, it is also shown that a small photovoltaic area on the roofs can meet the demand of the buildings. The results identify the aspects that need to be improved in the development of future design tools and highlight the importance of integrating parametric design software and simulation tools for BIPV systems at the preliminary design stage, to ensure good building performance, both in terms of comfort and energy performance.

A Novel Hybrid Approach Based on Analytical and Metaheuristic Algorithms for Parameters and Dynamic Resistance Estimation of a PV Array

Accurate parameters of photovoltaic (PV) arrays are essential for the modeling, analysis, and control of PV systems. Due to the lack of complete datasheets from manufacturers, different techniques have been introduced to extract the unknown parameters of PV modules. A novel approach based on one of the most recent metaheuristic (MH) optimization algorithms, the Flow Direction Algorithm, is developed in this paper to estimate the PV-modules parameters accurately. The proposed approach extracts the parameters for single-, double-, and three-diode models under different operating conditions. Comparative studies with state-of-the-art MH algorithms showed that the proposed approach is more accurate and robust and reduces the computational burden. Furthermore, a general formula is derived to obtain the dynamic resistance of different PV models. It is shown in this paper that inaccurate PV model parameters might negatively impact the steady-state and dynamic performance assessment of grid-connected PV systems under different operating conditions. Detailed time-domain simulations are presented to validate the analytical results and show the effectiveness of the proposed approach.

State-of-the-art review of MPPT techniques for hybrid PV-TEG systems: Modeling, methodologies, and perspectives

The development of alternative renewable energy technologies is crucial for alleviating climate change and promoting energy transformation. Of the currently available technologies, solar energy has promising application prospects owing to its merits of being clean, safe, and sustainable. Solar energy is converted into electricity through photovoltaic (PV) cells; however, the overall conversion efficiency of PV modules is relatively low, and most of the captured solar energy is dissipated in the form of heat. This not only reduces the power generation efficiency of solar cells but may also have a negative impact on the electrical parameters of PV modules and the service life of PV cells. To overcome the shortcomings, an efficient approach involves combining a PV cell with a thermoelectric generator (TEG) to form hybrid PV-TEG systems, which simultaneously improve the energy conversion efficiency of the PV system by reducing the operating temperature of the PV modules and increasing the power output by utilizing the waste heat generated from the PV system to generate electricity via the TEGs. Based on a thorough examination of the literature, this study comprehensively reviews 14 maximum power point tracking (MPPT) algorithms currently applied to hybrid PV-TEG systems and classifies them into five major categories for further discussion, namely conventional, mathematics-based, metaheuristic, artificial intelligence, and other algorithms. This review aims to inspire advanced ideas and research on MPPT algorithms for hybrid PV-TEG systems.

Estimation of photovoltaic module parameters based on datasheet: A review and a proposed method

Estimation of photovoltaic module parameters based on datasheet: A review and a proposed method

Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS)

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ± 0.0014, 1.3237 × 10−04± 0.0043, and 6.6016 × 10−06± 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively.

Http://journal-spqeo.org.ua/n2_2023/P180-187abstr.html

An algorithm has been developed for modeling the parameters of micro-prismatic specialized plane-focusing Fresnel lenses. Such lenses are more effective for application in photovoltaic modules for concentration of sunlight compare to the traditional point-focusing Fresnel lenses. The technical parameters of photovoltaic modules with these lenses were investigated. The method for manufacturing above lenses by diamond cutting technique and subsequent thermal pressing of silicone blanks is proposed. Some samples of specialized plane-focusing microprisms, which are made using our simulation results, have been experimentally investigated.

A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.

Modelling of Photovoltaic Modules Based on Hybrid Artificial Bee Colony Algorithm

In recent decades, the application of photovoltaic (PV) power generation and other new energy begins to attract people’s attention. The modelling of PV modules is significant for accurately estimating the generation power and electrical characteristic of the PV modules or arrays under complicated outdoor conditions. In this paper, a hybrid ABC algorithm is proposed. The model parameters of PV modules can be identified. In the proposed algorithm, three kinds of bees are hybrid, and they can complement each other and change roles dynamically, which makes the performance of the algorithm better than other basic optimization algorithms. Experimental results verify that the proposed hybrid ABC can effectively identify the model parameters and achieves better modelling accuracy of the I-V characteristics.

Meteorological-Data-Based Modeling for PV Performance Optimization

Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.

Distribution‐based PV module degradation model

AbstractThe degradation of photovoltaic modules has an impact on various parameters of photovoltaic modules. Ignoring the degradation of photovoltaic modules or inaccurate estimation of the degradation will lead to wrong power dispatching strategies and lead to economic losses. For PV module life estimation or reliability estimation, it is necessary to first establish an accurate statistical degradation model of PV module. The main goal of this paper is to analyze a selection of explicit PV module degradation model based on distribution. Since the degradation is related to time, the study assumed that those parameters in Gamma or Gaussian distributions are related to time. Five models are calculated based on maximum likelihood estimation and particle swarm optimization. Through verification and comparison on the measured PV module degradation data, the performance of these models in four cases: long‐term data fitting, long‐term data prediction, single‐module short‐term data fitting, and multimodule short‐term data fitting are evaluated. The results show that the model proposed in this paper has a great improvement over the original model, and the constant‐σ Gaussian distribution degradation model achieves the best performance.

Photovoltaics literature survey (No. 181)

Photovoltaics literature survey (No. 181)

SIMULATION OF THE WORK OF PHOTOELECTRIC PLANTS

The article draws attention to the fact that nowadays the demand for energy is not decreasing, but its cost is increasing. The need to research the parameters of photovoltaic modules and the capabilities of microprocessor technology for this is substantiated. It is noted that the scheme of electric distribution networks has become more difficult to operate, and the algorithms and methods of reliable and optimal management of modes are changing due to the modernization of the infrastructure of electricity supply and electricity consumption of the district. Nowadays, renewable energy sources are installed in the electrical distribution system at various levels of power supply and power transmission between the substation and consumers. Due to damage to the electrical equipment, there may be interruptions in the power supply, which unintentionally cause "islanding". During operation, situations sometimes arise when the power supply system of the "islands" is promptly de-energized starting from the points of connection of the "islands" to the unified electric power system. This happens based on the results of measuring the parameters of the mode of local electric power systems or based on the results of mathematical modeling of processes in local electric systems when, for example, the current levels of nodal voltages will not allow the use of an "island" electric network. For the purpose of researching processes in local electrical systems with photovoltaic plants, the article proposes their physical model in the form of a research bench, which can be used during the practical training of future employees who will manage the modes of distribution electrical networks with photovoltaic plants and maintain the equipment of such networks, systems and electrical stations Algorithms for optimal management of generated power, consumption and accumulation of electric energy tested using the proposed stand are designed to show the operating personnel ways of high-quality operation of distribution electric networks under the conditions of implementation of the Smart Grids concept.

Estimating parameters of photovoltaic modules based on current–voltage characteristics at operating conditions

Output characteristics of photovoltaic (PV) systems are greatly affected by both external and internal aspects. The external features, such as irradiance and temperature, are measured by the weather station. However, the irradiance and the temperature measured from measurement instruments are not representative of the energy and the heat that the PV modules receive. The internal factors, such as resistance, are difficult to identify since they are not physically present in the PV modules. One of the trending research directions is to utilize the output characteristics of the PV module to determine the operating conditions of the PV module. This paper presents a method to identify external features (solar irradiance and temperature) and internal resistances by employing the I–V characteristics of the PV module. The proposed method is based on the regression model, which uses the experimental I–V values from the I–V​ tracer. In addition, a method to obtain irradiance from the maximum power point current in the recent literature is employed for the comparison. The simulation results show that the estimation accuracy of the proposed method is higher than that of the referenced method.

Effects of Damp-heat on Shunt, Series Resistances and Fill Factor into Crystalline Silicon Photovoltaic Solar Modules in Tropical Zone

Most solar module manufacturers guarantee the minimum performance of their modules for 20 to 25 years. But some time after their installation, one observes faults which appear on the various components of these modules. During long-term exposure to severe climatic conditions, these faults, which accumulate over time, lead to performance losses of the module. This performance degradation is due to several factors such as humidity, temperature, heat, irradiation etc. These factors cause various degradation processes which can be electrical, chemical, mechanical, thermal, etc. The tropical zone, the middle of our study being characterized by high heat and humidity, has definite impacts on the electrical parameters of photovoltaic modules. The electrical parameters degradation is among others cause of photovoltaic modules performance loss. This study purpose is to study the degradation of the shunt resistance, the series resistance and the form factor of photovoltaic modules installed in tropical areas in order to appreciate their impact on the performance loss of the latter. For that, we used Peck performance degradation analytical model and Braisaz degradation analytical models of shunt resistance and the series resistance to calculate over a period of forty years, in real conditions in the tropical environment, the degradation of these electrical parameters of the photovoltaic module. The results obtained are compared to those obtained during the Hulkoff experimental tests in the state of Miami in Florida in 2009. The degradation rates obtained in the six cities concerned by the study vary between 17.33-35.67% for resistance shunt, between 3.77-7.55% for the series resistance and between 8-19% for the fill factor. In addition, these electrical parameters degradation rates obtained experimentally by Hulkoff and taken up by [1], are respectively 35.14% for the shunt resistor, 9.43% for the series resistor and 3.03% for the fill factor over the study period.
 So, future researches on crystalline silicon photovoltaic solar module can be effect of Damp-Heat on short circuit current and open circuit voltage in tropical area.

Evolution of the shape parameters of photovoltaic module as a function of temperature and irradiance: New method of performance prediction without setting reference conditions

The accurate characterization and prediction of current–voltage (I–V) characteristics of photovoltaic (PV) modules under different weather conditions are essential for solar power forecasting and ensuring grid stability. This paper proposed a novel method based on the power-law model (PLM) for estimating PV module performance under different weather conditions without setting reference conditions. The effects of solar irradiance, ambient temperature, and module types are all fully considered. The dependence of shape parameters in PLM on irradiance and temperature are thoroughly investigated and the effect of the selection of reference conditions is eliminated by modifying a set of new transforming equations. The parameters of the new transforming equations can be extracted from experimental data by an optimization algorithm. Due to the advantages of PLM, the proposed method applies to any type of PV module and the I–V characteristics can be expressed explicitly without Lambert W-function or iterative solution. The effectiveness and accuracy proposed method are verified and tested on the large datasets of eighteen PV modules in three locations. Compared with existing methods, the proposed method shows higher accuracy and better performance in the estimation of I–V characteristics and maximum power under different weather conditions.

Optimal parameter assessment of Solar Photovoltaic module equivalent circuit using a novel enhanced hybrid GWO-SCA algorithm

Solar Photovoltaic (PV) modules provide a reliable and clean electricity source that can suit various applications. Based on the quality and type of crystals, the PV modules are classified as monocrystalline, polycrystalline, and thin film. The improvements on different solar PV modules can be made efficiently with accurate mathematical models, which requires extracting its parameters. This paper suggests a novel enhanced hybrid grey wolf optimizer-sine cosine algorithm (EHGWOSCA) to extract solar PV module parameters. The recommended algorithm is validated on CEC-C06 2019 benchmark functions which contain ten standard functions. The PV module parameter extraction is performed on different kinds of PV modules, namely, monocrystalline type Shell CS6K280M, polycrystalline type Shell S75, and thin-film type Shell ST40. Two well-known models of PV modules are considered with a requirement of 5 parameters and 7 parameters extraction. The proposed algorithm is implemented by minimizing the sum of squared errors at open circuit point, short circuit point, and maximum power point (MPP). The results obtained show that in the Monocrystalline model using the proposed hybrid approach with single diode model, an error is 1.0718E−15, and it is further reduced to 1.3229E−16 with the double diode model. The proposed EHGWOSCA Error in the Polycrystalline model with double diode is 6.1594E−19, and the Thin-film model error is 2.91E−22. The effectiveness of the proposed approach is verified by comparative analysis with other methods available in the literature.

Determining solar cell parameters and degradation rates from power production data

• Power production data can be used to infer solar cell parameters. • Heuristic algorithms can extract these parameters from a few data points. • This method can be used to understand photovoltaic degradation remotely. • This approach is scalable and not very sensitive to environmental data. Practical but accurate methods that can assess the performance of photovoltaic (PV) systems are essential to all stakeholders in the field. This study proposes a simple approach to extract the solar cell parameters and degradation rates of a PV system from commoditized power generation and weather data. Specifically, the teaching-learning-based optimization algorithm was used to estimate the single-diode model parameters of a monocrystalline silicon PV module from a handful of power production data points that capture the operating current and voltage under real working temperatures and irradiance levels. These parameters can reproduce the solar panel’s actual behavior under all operating conditions and provide insights into its underlying degradation mechanisms. The results were validated by site measurements as well as a sensitivity analysis, thus offering exciting possibilities for the future of PV performance analysis, power forecasting, and remote fault detection for real-life applications.