Photovoltaic Module Power Research Articles

Crosslinking Behavior of Ethylene‐Vinyl Acetate Copolymer Encapsulants in Dependence of the Additive Composition

ABSTRACTThe three‐dimensional crosslinking of encapsulants in photovoltaic (PV) modules significantly defines their thermomechanical properties and is usually initiated using peroxides and crosslinking accelerators. However, it has been shown that excess peroxides lead to undesirable side reactions such as browning, which is directly linked to the PV module power losses. Therefore, the encapsulant formulation should be adjusted accordingly, on the one hand, keeping the peroxide concentration possibly low and, on the other hand, enabling a sufficiently high gel content (GC). This work investigates the basic interaction of the crosslinking peroxide Luperox TBEC and the crosslinking accelerator Perkalink 301 (TAIC) with differential scanning calorimetry (DSC), to address this issue. In addition, their reaction potential with the antioxidant butylhydroxytoluene (BHT) is investigated. It is shown that stabilizing additives can have an influence on the peroxide crosslinking process, which can affect the resulting crosslinking state and require optimization of the lamination conditions. The additive content of commercial PV encapsulants is quantified using pyrolysis gas‐chromatography mass spectrometry (PY‐GCMS). Subsequently, a number of films with different TBEC and TAIC contents are produced, and their GC is analyzed as a function of the additive concentrations and lamination conditions. A range of formulations leading to a GC > 75% was identified which can be used as a guidance for encapsulant manufacturers.

The Effect of Partial Shadings on the Output Power of the Photovoltaic Modules Connected with Different Current and Voltage Characteristics

A mismatch in the output power of photovoltaic (PV) modules in a PV array can occur due to partial shading or a module replacement. Substitution of a module in a PV array with a new one might lead to different current and voltage characteristics between the new and existing modules and result in power losses. The amount of power loss might be increased more if the PV array experiences partial shading. For this reason, this study aims to investigate the effect of partial shadings on the power output of photovoltaic arrays with different current and voltage characteristics. The PV array under test consists of 25 units of solar modules with a total cross-tied (TCT) configuration. There are five shading conditions applied to the test PV array, i.e., the short narrow (ShN), the short wide (ShW), the long narrow (LnN), the long wide (LnW), and the diagonal. A magic square method is applied to reduce the power loss when the PV modules experience partial shading conditions. The results show that the power loss due to partial shadings, either on all identical modules or partially identical, is the same. The most significant power loss occurs in the long comprehensive shading scenario, where 80% of the modules experience shading, which is 41.30%.

Photovoltaic modules fault detection, power output, and parameter estimation: A deep learning approach based on electroluminescence images

Accurately detecting faults in photovoltaic modules/cells and estimating their effective power output and parameters of the equivalent circuit representation of photovoltaic modules is becoming increasingly critical for both the reliability of associated systems and the efficiency of electricity production from renewable energy sources. Existing studies often work with datasets containing photovoltaic cells that exhibit one fault at a time, leading to the classification of photovoltaic cells with multiple faults as “mixed” faults. Moreover, factors such as cell alignment and specific fault types, collectively called “cell level features”, are not considered in current studies estimating the power output of a photovoltaic module. Therefore, this paper focuses on a comprehensive deep-learning pipeline to separately detect three types of faults in photovoltaic modules/cells using electroluminescence images. Furthermore, it addresses the estimation of the output power of photovoltaic modules and the series resistance of their equivalent circuit, considering the cell-level characteristics extracted from the electroluminescence images. The proposed model demonstrates its ability to detect “black core”, “crack”, and “edge” faults with global accuracies of 0.93, 0.868, and 0.95, respectively. Furthermore, the proposed model estimates the power output of photovoltaic modules with a normalized mean absolute error of 0.03547 and a normalized root mean squared error of 0.04892. This outperforms the base model that relies solely on non-pre-processed detected faults and significantly larger models adept at extracting features from the electroluminescence images. Moreover, the VGG16-based model estimates the series resistance in the equivalent circuit representation of photovoltaic modules with a normalized mean absolute error of 0.04472 and a normalized root mean squared error of 0.0622.

Power optimization of photovoltaic modules under varying environmental conditions based on current equalization collaborating constant voltage control

Abstract The performance of photovoltaic modules is affected by environmental factors such as irradiance and temperature, which can lead to a decrease in output performance or even damage. This study proposes an improved formula for calculating the real maximum power of photovoltaic modules by analyzing the influence of irradiance and temperature. A simulation model is developed using PLECS software to simulate the global maximum power of photovoltaic modules under different environmental conditions, and the results are compared with the calculated real maximum power. A power optimization scheme for photovoltaic modules is then proposed based on current equalization and constant voltage control. This scheme employs a single-switch multi-winding forward-flyback converter to equalize the mismatched currents between cell strings, thereby enhancing the output performance. Traditional proportional integral controllers are utilized to achieve constant voltage control and obtain the real maximum power of photovoltaic modules. Simulation models are built in the PLECS simulation platform to evaluate the performance of a global maximum power point tracking scheme based on the traditional perturb and observe algorithm with current equalization, a segment perturb and observe algorithm without current equalization, and the proposed power optimization scheme. The simulation results demonstrate that the proposed constant voltage control has greater efficiency than the traditional perturb and observe algorithm. The proposed scheme achieves a significant improvement in efficiency, with a 27.87% increase compared to the segment perturb and observe algorithm without current equalization.

Power equalization and optimization of photovoltaic module based on forward-flyback converter

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes.

Solar power forecasting model as a renewable generation source on virtual power plants

This paper describes modeling solar power generation as a renewable energy generator by simulating the analytical approach mean absolute error and root mean square error (MAE and RMSE). This research estimates the error referring to long short-term memory (LSTM) network learning. Related to this, the Indonesian government is currently actively developing solar power plants without ignoring the surrounding environment. The integration of solar power sources without accurate power prediction can hinder the work of the grid and the use of new and renewable generation sources. To overcome this, virtual power plant modeling can be a solution to minimize prediction errors. This study proposes a method for on-site virtual solar power plant efficiency with a research approach using two models, namely RMSE and MAE to account for prediction uncertainty from additional information on power plants using virtual solar power plants. A prediction strategy verified against the output power of photovoltaic (PV) modules and a set based on data from meteorological stations used to simulate the virtual power plants (VPP) model. This forecast prediction refers to the LSTM network and provides forecast errors with other learning methods, where the approach simulated with 12.36% and 11.85% accuracy for MAE and RMSE, respectively.

A fuzzy classification method based on rules learning for shaded and unshaded hotspot faults on photovoltaic modules

Hotspots (HS) reduce the output power (P) of photovoltaic modules (PVMs) and sometimes lead to fires. However, it is difficult to effectively classify and detect HS faults. In this paper, we focus on the classification and detection of Shaded HS and Unshaded HS. HS faults not only occur due to non-uniform illumination and parameter mismatch but due to internal defects as well, such as; low resistance defects of a cell. The Shaded HS can be repaired sometimes by water washing, such as PVMs in the desert, while the Unshaded HS can only be repaired by replacing them. Therefore, to classify and detect such HS faults, we propose a fuzzy classification method based on data-driven rules learning. Firstly, the characteristic performances of Shaded and Unshaded HS PVMs are compared with the performances of normal PVMs. Secondly, the percentage reduction algorithm is presented for extracting three fault indicators to identify such HS faults. Then, a classification method is proposed using a technique called the Mamdani-type Fuzzy logic system, in which the rules are obtained by a process called the “Data-Driven Rule Generation Method”. Finally, a low-resistance HS model is built using Matlab Simulink. The simulation and experiment results show that the proposed method has more classification accuracy for Shaded and Unshaded HS faults.

Review of Energy Management Systems in Microgrids

Microgrids usually employ distributed energy resources such as wind turbines, solar photovoltaic modules, etc. When multiple distributed generation resources with different features are used in microgrids, managing these resources becomes an important problem. The generated power of solar photovoltaic modules and wind turbines used in microgrids is constantly changing with solar irradiation and wind speed. Due to this impermanent and uncertain nature of renewable energy resources, generally, energy storage systems are employed in microgrid systems. To control the distributed energy resources and energy storage units and sustain the supply and demand balance within the microgrid and provide sustainable and reliable energy to the loads, energy management systems are used. Many methods are used to realize and optimize energy management in microgrids. This review article provides a comparative and critical analysis of the energy management systems used in microgrids. The energy management system can be tailored for different purposes, which are also discussed in detail. Additionally, various uncertainty measurement methods are summarized to manage the variability and intermittency of renewable energy sources and load demand. Finally, some thoughts about potential future directions and practical applications are given.

Design and development of photovoltaic solar system based single phase seven level inverter

For solar photovoltaic (PV) systems, an upgraded triple gain seven-level inverter that works both independently and while connected to the grid is proposed. The two-stage configuration of the system is boost cascaded. The first stage has a one switch improved gain converter (OSIGC) to increase and normalize the input direct current (DC) voltage, and the second stage includes a unique seven level alternating current (AC) is produced via a multilevel inverter (MLI) design with triple voltage gain. The proposed OSIGC is appropriate for a broad range of conversions. The voltage gain in MLI was achieved using switched capacitor techniques. The DC-DC converter can achieve a maximum voltage gain of twelve and the MLI can achieve a maximum voltage gain of three, resulting in a DC-DC-AC voltage that can reach 36. Maximum power point tracking (MPPT) technique based on modified perturb and observe (PO) is used in OSIGC to maximise PV module power utilisation, and MLI control utilises sinusoidal pulse width modulation (SPWM) realistically. For the purpose of analysing the suggested system, a 200 Watt prototype statel is created. With a total harmonic distortion (THD) of 0.181%, up to 92.12% of the converter system’s overall efficiency is possible.

Mathematical models to forecast temporal variations of power law shape parameters of a PV module working in real weather conditions: Prediction of maximum power and comparison with single-diode model

In this study, an innovative approach has been proposed to generate real time current–voltage characteristics and forecast peak power of photovoltaic (PV) modules working outdoors beneath real meteorological conditions. Power-Law Model (PLM) including four unknown parameters, two PV metrics (ISC, VOC), and two profile parameters (γ, m), has been used for fast, accurate and reliable modelling of PV modules. Explicit expressions giving γ and m parameters as functions of cardinal point coordinates (IMPP,VMPP, ISC and VOC) have been established. A daily monitoring of PV quantities, throughout one reference day, has been used to derive novel mathematical models of γ and m depending on module temperature and solar irradiance. To describe PV module behavior under changing meteorological conditions during an arbitrary day, mathematical models have been called to reproduce dynamical values of PLM shape parameters γ and m. Reliability of the approach has been assessed by comparing current–voltage characteristics and maximum power coming from PLM and Single-Diode Model (SDM) to experimental data of twenty-two PV modules measured at NREL. Comparison also reveals that I-V characteristics generated and predicted separately by PLM and SDM parameters are very close. In some cases, I-V characteristics generated by PLM parameters are more accurate. Furthermore, NRMSE values for generated and predicted I-V characteristics for all kinds of PV modules evaluated for different working conditions do not exceed 4% for both PLM and SDM, and determination coefficient values for long-term prediction of maximum power are close to one and annual average value is greater than 0,95.

Impact of dust particles on the output power of photovoltaic modules in Calabar, Cross River State

During the dry season, dust particles on photovoltaic modules (PV modules) are a regular occurrence in Calabar, Cross River State. This dusty condition or environment is caused by the migration of dust particles from the Sahara region via the east-west wind. The concentration of dust on PV modules has an impact on the overall performance of solar energy systems. This research looks into the effect of dust particles on the output power of solar modules. This investigation was carried out by subjecting 80-watt monocrystalline PV modules to an outdoor experiment and analyzing the changes in current and voltage from the control and dusty PV modules with a digital multimeter. Other environmental factors such as relative humidity, ambient temperature, and solar radiation were also measured with a hygrometer, thermometer, and solar power meter respectively. Appropriate formulars were employed to calculate output power and efficiency. Dust particles on the surface of PV modules dramatically reduces the quantity of sunlight that penetrate the panel by 21.10%, resulting in a drop in the system's overall output power. The study emphasizes on the importance of frequent cleaning and maintenance of PV systems.

A STANDALONE DC MICROGRID ENERGY MANAGEMENT STRATEGY USING THE BATTERY STATE OF CHARGE

This article introduces an enhanced energy management strategy that employs the state of charge (SoC) of batteries in standalone DC microgrids with photovoltaic (PV) modules. Efficient energy management is crucial to ensure uninterrupted power supply to the load units in microgrids. To address the challenges posed by external factors such as temperature fluctuations and variations in solar irradiance, energy storage systems are deployed to compensate for the negative effects of the external factors on the output power of PV modules. The proposed approach takes into account various parameters of the microgrid elements, including the available power from the sources, demand power, and the SoC of batteries, in order to develop an efficient energy control mechanism with load-shedding capability. By considering these parameters, the strategy aims to optimize the utilization of available resources while ensuring a reliable power supply to the connected loads. The SoC of the batteries plays a critical role in determining optimal charging and discharging profiles, enabling effective energy management within the microgrid. To evaluate the effectiveness of the proposed approach, an algorithm is designed and simulations are conducted. The proposed algorithm utilizes a hybrid approach by combining power and SoC-based methods for efficient control. Through analysis of the simulation results, it is found that the presented approach is capable of delivering the intended load power while increasing the life cycle of the batteries with the pre-defined SoC levels.

Effect of ultraviolet aging of backsheet on electrical performance of silicon photovoltaic module

Photovoltaic (PV) backsheets protect solar cells from corrosion, moisture intrusion, and environmental pollution, and they also reflect light to be reabsorbed by solar cells. Previous studies have confirmed that PV backsheet materials can affect the performance of PV modules. In this study, the performance of the backsheet under a single ultraviolet (UV) aging condition and its influence on the electrical performance of the PV module were explored so as to establish the relationship between backsheet aging and PV module performance. The results of this study show that UV irradiation is the main factor leading to the aging and cracking of backsheets. When the backsheet was exposed to a UV dose of 60 kWh/m2, the elongation at break of the backsheet was reduced by 97.04%, the tensile strength was reduced by 60.10%, the reflectivity of the backsheet was reduced by 8.36%, and the power and short-circuit current of the PV module decreased by 3.69% and 3.96%, respectively. The primary cause for the decline in PV module power was determined to be the reduction in backsheet reflectivity, which diminished the available light that could undergo re-reflection onto the solar cell, thereby resulting in a reduction of the short-circuit current. These findings provide valuable insights into the changes in the physicochemical properties of UV-aged backsheets and their power decay mechanism in PV modules. The results establish an invaluable link between the testable characteristics of backsheet aging and PV module performance.

Performance numerical evaluation of modified single-ended primary-inductor converter for photovoltaic systems

<span lang="EN-US">Single-ended primary-inductor converter (SEPIC) was considered a good alternative to a DC-DC converter for photovoltaic (PV) systems. The SEPIC converter can operate with an input voltage greater or less than the regulated output voltage, or as a step-up or step-down. As a step-up converter, SEPIC boosts PV voltage to specific levels. However, gain limitation and voltage stress continue to reduce the efficiency of conventional SEPIC converters. Because of this, researchers created a modified SEPIC converter to improve performance. In this paper, six modified SEPIC converters were compared and evaluated. To compare fairly, all modified SEPIC converters are non-isolated and use a single switch. Power simulator (PSIM) software was used to simulate each converter with a BISOL BMO-250 PV module and maximum power point tracking (MPPT) P&O controller. The converter with the highest static voltage gain and lowest duty cycle has been identified. It results in up to ten times voltage increment with a 0.8-duty ratio. All topologies have the same voltage stress, with maximum and minimum values of 30.1 and 29.5 V, respectively. On the other hand, each topology produces different average efficiencies, with the highest and lowest efficiency at 99.5% and 97.2%, respectively.</span>

Solar photovoltaic power prediction using artificial neural network and multiple regression considering ambient and operating conditions

This paper proposes artificial neural network (ANN) and regression models for photovoltaic modules power output predictions and investigates the effects of climatic conditions and operating temperature on the estimated output. The models use six days of experimental data creating a large dataset of 172,800 × 7. After data preprocessing, the appropriate attributes were selected as inputs and taken into account as features; solar irradiation, ambient air and module temperature, wind speed, and relative humidity, while the power generation as a target. In light of these data, the effect of training algorithm on the predictive performance of the ANN model was investigated. Results show that solar irradiation, ambient and module temperatures are key factors in predicting PV module power generation, as these variables are strongly correlated with PV power output. Moreover, the Levenberg-Marquardt algorithm was found to be the best training procedure. The ANN model demonstrated higher accuracy than the developed multiple linear regression models. However, the proposed Rational-Power-Law (RPL) and Power-Law (PL) models were able to capture the nonlinearity in the system, as assessed by coefficient of determination (R2) and the Mean Absolute Error (MAE), and successfully supplied a very high level of precision. The ANN, and both RPL and PL models provided comparable performance, attaining an R2 of 0.997, 0.998 and 0.996, and a MAE of 1.998, 1.156, and 1.242, respectively, when compared to experimental results. Furthermore, models proposed in this study were evaluated and compared with others available in literature and have demonstrated superior performance and better accuracy.

Prediction of the output power of photovoltaic module using artificial neural networks model with optimizing the neurons number

Artificial neural networks (ANNs) is an adaptive system that has the ability to predict the relationship between the input and output parameters without defining the physical and operation conditions. In this study, some queries about using ANN methodology are simply clarified especially about the neurons number and their relationship with input and output parameters. In addition, two ANN models are developed using MATLAB code to predict the power production of a polycrystalline PV module in the real weather conditions of Iraq. The ANN models are then used to optimize the neurons number in the hidden layers. The capability of ANN models has been tested under the impact of several weather and operational parameters. In this regard, six variables are used as input parameters including ambient temperature, solar irradiance and wind speed (the weather conditions), and module temperature, short circuit current and open circuit voltage (the characteristics of PV module). According to the performance analysis of ANN models, the optimal neurons number is 15 neurons in single hidden layer with minimum Root Mean Squared Error (RMSE) of 2.76% and 10 neurons in double hidden layers with RMSE of 1.97%. Accordingly, it can be concluded that the double hidden layers introduce a higher accuracy than the single hidden layer. Moreover, the ANN model has proven its accuracy in predicting the current and voltage of PV module.

Design and Optimization of Photovoltaic System in Full-Chain Ground-Based Validation System of Space Solar Power Station

In the face of the increasing depletion of non-renewable energy sources and increasingly serious environmental problems, the development of green and environmentally friendly renewable energy sources cannot be delayed. Because of the far-reaching development potential of solar energy, solar power has become an important research object for power development. The available solar energy in space is several times greater than that on Earth. Solar energy from space can be collected by a space solar power station (SSPS) and transmitted to the ground by wireless power transfer. In the full-chain ground-based validation system of SSPS-OMEGA, the spherical concentrator is used, and the light intensity distribution on the solar receiver is non-uniform. The non-uniform light intensity makes the output current of each photovoltaic (PV) cell on the solar receiver greatly different, and causes power losses, known as the mismatch problem. This paper proposes a simple, efficient and easy-to-implement method to optimize the structure of PV arrays to reduce the effect of non-uniform light on the output performance of each PV cell, which is beneficial to the topology of PV arrays and also effectively improves the layout rate. Then, a differential power processing (DPP) converter with a simple structure and easy control is designed to further deal with the power mismatch problem between series-connected PV modules. Finally, a simulation circuit model and a physical hardware model of the differential power processing PV system are built and used in the full-chain ground-based validation system of SSPS-OMEGA. The results demonstrate that the influence of non-uniform lighting on PV cells is effectively reduced, the output power of PV modules connected in series under non-uniform light distribution is substantially increased, and the photoelectric conversion efficiency is significantly improved.

Improved Flexible Power Point Tracking Algorithm for PV System Under Fast-Changing Irradiance Conditions

Flexible power point tracking (FPPT) has been presented in the past years to control the output power of photovoltaic (PV) modules to a specific value, which makes PV generation much more friendly to the system. However, the tracking accuracy of existing FPPT algorithms is low during fast-changing irradiance. The improved FPPT algorithm, which consists of modified maximum power point tracking strategy and optimized constant power generation method, is proposed. Compared with existing FPPT algorithms, the tracking accuracy of the proposed algorithm for PV systems under various working conditions is improved while the energy difference is decreased. Followed by theoretical analysis, the simulation and experimental results validate the claimed advantages of the proposed solution.

New models for the influence of rainwater on the performance of photovoltaic modules under different rainfall conditions

Solar photovoltaic (PV) technology is considered one of the most promising clean energy technologies. Dust accumulation on the surface of photovoltaic modules is one of the important reasons for the decline of photovoltaic system performance, and the influence of rainfall is particularly significant. The purpose of this paper is to investigate the influence of rainwater on the output power of photovoltaic modules under different rainfall conditions. In this paper, a prediction model for dust density under different inclination angles is established based on outdoor test data, and the control variable method is used to design three different PV module performance testing schemes under different rainfall conditions, and a dimensionless model for output power is proposed. Empirical models of global solar radiation are combined, and thus output power prediction models under different rainfall conditions are developed. It is found that rainstorm conditions (Rainfall 50–100 mm) increased the peak PV module output power by 16.1%–28.2% compared to light rainfall conditions (Rainfall less than 10 mm), with the rain showing better cleaning effects for PV modules with higher dust densities. It is worth noting that PV module surface dust does not necessarily have only a negative effect on system performance. The influence mechanism of rainwater on the output power of photovoltaic modules and the prediction model established in this paper is helpful in improving the prediction accuracy of the output power of photovoltaic modules under rainfall conditions, and thus the efficient cleaning strategy is formulated.

Low‐cost outdoor spectral irradiances determination and its application on Concentrator Photovoltaic module power rating and energy yield calculation

AbstractIn this work, a method to calculate the aerosol content in the atmosphere and the prevailing spectral irradiance of the sunlight is introduced. This method does not aim for low measurement uncertainty but for cost effectiveness and does not require sophisticated equipment. The bases of the method are the clear sky direct normal irradiance (DNI) and a look‐up table calculated with the software tool SMARTS2. The ratio of clear sky DNI to the prevailing DNI in combination with the look‐up table allows the determination of aerosol optical depth (AOD), spectral matching ratios (SMR), and spectral irradiances. Besides the prevailing DNI, the following ambient condition parameters are required: Air pressure, relative humidity, and ambient temperature. This means a weather station and a pyrheliometer are sufficient for the determination of the prevailing spectral irradiance. Obviously, this method cannot guarantee the same accuracy as conventional spectral irradiance measurement methods (e.g., using a spectroradiometer). However, in this work, we demonstrate the potential of this new method as a fall‐back strategy for missing spectral irradiance data. This method is worth using when (C)PV module power output data needs to be evaluated in dependence of the composition of the spectral irradiance, but no regular spectral irradiance data are available. In this paper, the AOD and SMR values determined with the introduced method are compared to measurement data with common measurement devices demonstrating a satisfying agreement. The spectral irradiances calculated with this method are successfully tested for using as the basis for energy yield calculations and for the determination of the CPV module power output at Concentrator Standard Conditions according to IEC 62670‐1.