Output Power Of Photovoltaic Module Research Articles

Short-Term Prediction of the Solar Photovoltaic Power Output Using Nonlinear Autoregressive Exogenous Inputs and Artificial Neural Network Techniques Under Different Weather Conditions

The power generation by solar photovoltaic (PV) systems will become an important and reliable source in the future. Therefore, this aspect has received great attention from researchers, who have investigated accurate and credible models to predict the power output of PV modules. This prediction is very important in the planning of short-term resources, the management of energy distribution, and the operation security for PV systems. This paper aims to explore the sensitivity of Nonlinear Autoregressive Exogenous Inputs (NARX) and an Artificial Neural Network (ANNs) as a result of weather dynamics in the very short term for predicting the power output of PV modules. This goal was achieved based on an experimental dataset for the power output of a PV module obtained during the sunny days in summer and cloudy days in winter, and using the data in the algorithm models of NARX and ANN. In addition, the analysis results of the NARX model were compared with those of the static ANN model to measure the accuracy and superiority of the nonlinear model. The results showed that the NARX model offers very good estimates and is efficient in predicting the power output of the PV module in the very short term. Thus, the coefficient of determination (R2) and mean square error (MSE) were 94.4–97.9% and 0.08261–0.04613, respectively, during the summer days, and the R2 and MSE were 90.1–89.2% and 0.281–0.249, respectively, during the winter days. Overall, it can be concluded that the sensitivity of the NARX model is more accurate in the summer days than the winter days, when the weather conditions are more stable with a gradual change. Moreover, the effectiveness of the NARX model has the specificity to learn and to generalize more effectively than the static ANN.

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.

Analysis of dust deposition law at the micro level and its impact on the annual performance of photovoltaic modules

Solar photovoltaic (PV) power generation is a promising clean energy technology, but dust affects its performance. This study, conducted in nine Chinese cities with varying climates and geographies, analyzed the physicochemical properties of dust and its deposition patterns at different inclination angles using microanalysis. Inclination angles were classified into three categories, and a dust density prediction (DDP) model was developed for each. The study also quantified the effect of different dust densities on PV module temperature in a controlled environment and created a PV module output power prediction (POPP) model to assess dust accumulation impacts on PV efficiency. It was found that dust insulation could increase PV module output power by 1.64 %–1.79 % at dust densities of 5–10 g/m2 and radiation levels of 400–800 W/m2. Combining the DDP and POPP models, a PV efficiency loss rate model was proposed to evaluate annual average PV efficiency and power generation losses at different tilting angles. Results showed an annual average PV efficiency loss of over 7 % at a 15° inclination angle and about 4.5 % at a 90° inclination angle. This study provides a framework for evaluating PV module mounting angles and cleaning intervals, optimizing solar energy systems for improved efficiency and returns.

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

This study proposes a novel honey bee dancing (HBD) maximum power point tracking (MPPT) algorithm inspired by the foraging behavior of honey bees. When a bee finds nectar, it returns to the honeycomb and dances to inform others about the location of the nectar. Other bees then fly towards the location and gather the nectar. The proposed HBD algorithm uses five bees searching for the nectar who communicate with each other about the location and the quantity of nectar by dancing. Finally, the five bees found the location of the most nectar which is represented by the maximum power point. The proposed HBD algorithm applies to uniform irradiance condition (UIC) and partial shaded condition (PSC). It is then compared with the PV panel output power and load relationship (OPLR) algorithm and perturb and observe (P&O) algorithm. Experimental verification has been performed under both the UIC and PSC. At the UIC's irradiance level of 200 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 120 W, 118 W, and 94.5 W, respectively, with efficiencies of 99 %, 97 %, and 78 %. Additionally, under the PSC with an irradiance level of 600 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 218 W, 189.2 W, and 74.8 W, respectively, with efficiencies of 99 %, 86 %, and 34 %, and convergence times of 4.7 ms, 6.5 ms, and 6 ms, respectively. It is evident from the results that the solar MPPT performance of the proposed HBD algorithm is better than the OPLR algorithm and P&O algorithm. This method ingeniously combines the foraging behavior of bees with solar power generation to produce the maximum natural power. This approach does not require the development of photovoltaic (PV) panel specification data, complex calculations, and additional temperature meters and heliographs. It is highly efficient and has significant economic benefits.

Categorizing Indian states based on operating condition of photovoltaic system

Electricity generation of a photovoltaic (PV) module is primarily affected by local weather conditions, which vary significantly across vast geographic areas. This work introduces an approach to categorize Indian states based on outdoor operating conditions of PV modules that influence performance and reliability. Module temperature and irradiance are the two most important parameters which affect PV performance. Relative humidity (RH), module temperature, and global horizontal irradiance (GHI) are the three most important parameters that affect PV reliability. In this work, the PV module's most frequent operating condition (MFOC) of temperature and irradiance corresponding to maximum energy production has been analyzed for dominant PV technology (multi-crystalline silicon). Data from various sites across India were analyzed and subsequently grouped by state as PV installation decisions are generally based on state-level factors, such as state business policies, incentives, availability of local human resources, state power policies, etc. The MFOC method used in this work was supported by experimental results. Based on estimated states' MFOC, PV module output power has been obtained and compared with its rated power. Further in this work, major stressors affecting PV modules namely average RH, module temperature, and total annual GHI have been analyzed for different Indian states. Based on these specific stressors, states with similar stressor patterns have been grouped by the k-means clustering method. Results of MFOC estimation show potential for additional standardization methods to estimate PV system performance accurately. Statistical analysis of stressors highlights the importance of selecting PV technology modules carefully.

PERFORMANCE ANALYSIS OF P&O AND PSO MPPT ALGORITHMS FOR PV SYSTEMS UNDER PARTIAL SHADING

Photovoltaic (PV) modules are devices that transform photon energy into electrical energy. The output power of the PV modules is influenced by the intensity of solar radiation and the ambient temperature. Non-uniform shading can cause variations in the extent of sunlight absorbed by PV modules, resulting in a decrease in power output. Maximum power point tracking (MPPT) techniques are employed to optimize the power output of PV modules by operating them at their maximum power point (MPP). The main objective of the study is to investigate the performance analysis of Perturb and Observe (P&O) and Particle Swarm Optimization (PSO) MPPT strategies in uniform and partially shaded conditions with equally and unequally different irradiance differences. Simulation studies were conducted on the PV circuit model using Matlab/Simulink, and the results were evaluated. MPPT algorithms are compared based on their tracking efficiency and convergence speed when solar radiation conditions vary. The findings of the simulation indicate that the P&O is unable to determine global MPP and gets trapped in one of the local MPPs. However, the PSO is very effective in tracking MPP under different partial shading patterns with more than 96% tracking efficiency. In the first partial shading configuration where the sunlight intensity of the PV modules is uniformly distributed, the PSO technique has reduced steady-state oscillations around the MPP. However, the P&O technique demonstrates superior response time and convergence speed in comparison to the PSO technique.

Experimental study on the effect of dust particle deposition on photovoltaic performance of urban buildings

The output characteristics of photovoltaic (PV) modules can be negatively affected by the density of snowfall in winter and ash accumulation in summer. In order to solve this problem, an experimental platform for PV power generation with four angles of 0°, 30°, 45° and 60° was built in Shenbei New District, Shenyang City, China. The characteristics of ash accumulation on the surface of PV modules were studied and analyzed by SEM and XRD. The effects of snowfall, ash accumulation density and PV module angle on the output power and power attenuation rate of PV modules were investigated. The experimental results show that snowfall had both positive and negative effects on PV modules. The output power of the PV module decreases as the ash density increases. Based on the dust coverage density and tilt angle in the test data, empirical formulas are proposed to predict the average power reduction rate.

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.

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.

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.

Module-level direct coupling in PV-battery power unit under realistic irradiance and load

A photovoltaic (PV) module, battery and consumer or load is usually tied together by a complex power electronics, including maximum power point tracking (MPPT) device for power coupling to maximize output of the PV modules. At the same time, a typical battery itself can play the role of a power coupling element in addition to its main energy storage function. In principle, a properly chosen PV-battery pair can maintain a high degree of internal power coupling even under variable irradiance and load without MPPT electronics. This option is of interest for e.g. module-level integration of PV and battery to cope with natural intermittency of a PV module power output. In this work, we experimentally examine the function of a laboratory scale unit of a 7-cell silicon heterojunction PV module directly connected to a lithium-ion battery and variable load. The unit is the simplest PV-battery module representative for detailed study under a series of emulated realistic profiles of irradiance and power consumption. The directly coupled PV-battery unit shows coupling efficiencies of above 99.8% at high irradiance and approx. 98% on average through the daily cycle – a value that is comparable to modern MPPT devices.

Modeling and Experimental Studies on Water Spray Cooler for Commercial Photovoltaic Modules

This paper presents modeling and experimental studies on water spray coolers for commercial photovoltaic modules. This paper has compared the energy yield of four photovoltaic commercial modules that were installed with a fixed tilt angle being equal to the local latitude in central Vietnam, including one photovoltaic module using a water spray cooler and three photovoltaic modules without cooling. Experimental results on sunny days have been shown that the energy yield difference between four PV modules under the same working condition is lower than 1%. In addition, on sunny days when the set working temperature of the water spray cooler is 45 °C, the average improvement efficiency of a photovoltaic module using a water spray cooler compared to three reference photovoltaic modules is 2.64%, 3.83%, and 6.18%, for an average of 4.22%. A simple thermal–electrical model of a photovoltaic module with a water spray cooler has been developed and tested. The normalized root mean square error between simulated and measured results of photovoltaic module power output on a sunny day without cooling and with water spray cooler reached 6.5% and 8.5%, respectively. The obtained results are also demonstrated that the reasonableness of the simple thermal–electrical model of the photovoltaic module with water spray cooler and the feasibility of a cooling system is improved to increase the efficiency of the photovoltaic module. In addition, they can be considered as a basis for new experimental models in the future.

PV module life prediction based on coupled failure model

Due to environmental stresses, photovoltaic (PV) modules operating outdoors may trigger multiple power degradation patterns. In this paper, four types degradation patterns are studied, among which humidity cycling is proposed for the first time. To verify the necessity of the proposed humidity cycle degradation, the sensitivity of the environment on the output power of PV modules is analyzed using orthogonal tests, and the results show that the power degradation of PV modules by humidity cycle is second only to the damp heat degradation. In addition, a method to model the coupling relationship between each degradation patterns based on weight values is proposed to accurately quantify the influence of environment on the output power of PV modules, and the accuracy of the model is verified by actual power plant data.

The Influence of Temperature and Irradiance on Performance of the photovoltaic panel in the Middle of Iraq

The photovoltaic (PV) panels are expected to be the most important systems to meet global energy demand by converting solar energy into electricity. The main obstacle to the widespread deployment of the PV systems its the limited efficiency, which are greatly affected by the solar radiation and the operating temperature. The full knowledge of the performance, efficiency and output power of photovoltaic modules and the extent of their change with the fluctuations of solar radiation and temperature is necessary to determine the optimal size of the system and avoid the financial risks of the project. This paper investigated numaricaly and experimentaly the influence of operating temperature and solar radiation on the output power and efficiency of polycrystalline PV panels in Baghdad-Iraq. The PVsyst software was used to simulate a model implementing simulation results presented the impact of variations temperature and solar radiation in the curves of I-V, P-V and efficiency. In order to verify the reliability of the simulated results with experimental ones, several measuring devices have been used to conduct field experiments in the outdoor conditions. It were used to determine the characteristics and performance of a 120W polycrystalline PV panel for different ranges of solar radiation and operating temperature. The simulation results showed that the current, voltage, output power and efficiency increased with increasing solar radiation, while they decreased with increasing temperature except the current that was increased. The experimental and simulated results were identical in terms of the effect of temperature and solar radiation on the current, voltage, output power and efficiency of the PV panel. The experimental tests showed that when the temperature is increased by 1°C, the current was increased by about 0.068%, the voltage decreased by 0.34%, the output power decreased by 0.489% and the efficiency decreased about 0.586%. The experimental results displayed that the parameters of the PV panel under real operating conditions behave differently than in the standard test conditions (STC), as they are strongly affected by weather fluctuations in terms of temperature and solar radiation

Output performance prediction of PV modules based on power-law model from manufacturer datasheet

The current–voltage (I–V) equation in the equivalent circuit model of the photovoltaic (PV) module is implicit, and the dependence of model parameters on environmental conditions is uncertain, which causes inconvenience in output performance prediction. In this paper, a novel method based on the power-law model (PLM) is proposed to predict the I–V characteristics and output power of PV modules under varying operating conditions. The relationship between parameters in the PLM and manufacturer datasheet information is established. The irradiance and temperature dependences of shape parameters in PLM are obtained and investigated thoroughly. Due to inherent simplicity and explicit expression of PLM, the proposed method predicts the I–V characteristics and output power without using any iterative process, which reduces the computational complexity. The proposed method is validated by different types PV modules and under a wide range of environmental conditions. Comparing with traditional methods based on a single-diode model, the proposed method has better agreements with experimental results in all irradiance and temperature intervals. The accuracy and effectiveness are verified both in short-term and long-term output power prediction. The proposed method is simple and suitable to predict the actual output properties of PV modules under varying operating conditions.

Experimental and simulation-based comparative analysis of different parameters of PV module

Renewable Energy (RE) has been rapidly growing day by day as a need of the world due to the energy crises and environmental effects. In recent years, the use of solar systems for the generation of electricity has gained considerable popularity. This increase mainly results from a scarcity of other energy sources, such as fossil fuels. As a result, there is a pressing need to transition to more dependable and long-term resources, such as photovoltaic (PV) systems. To improve the performance of PV proper design and development have been required for adequate extraction of their essential parameters. This study proposed the implementation and behaviour of a photovoltaic module (PVM) and describes the individual main equation situated on the Shockley diode to enable a detailed study of semiconductor physics and PV occurrence. The environmental performance of a PVM is represented using MATLAB which can be illustrative of the PVM for simple use in the simulation phase. The model was designed in MATLAB, an easy-to-use icon and dialog box that depends on the effect of solar radiation (SR) and cell temperature, output current (I) versus (vs) voltage (V), and power vs. Voltage. PVM is made with the simulated models are simulated and optimized. These models have been used to analyze the outcome of variations in various specifications on the PVM, including the operating temperature and the level of SR. The observed results have been compared with outcomes characteristics of PV, which are specified on the technical datasheet of the PVM. Simulation results have been obtained by using MATLAB software. From the results, it can be seen that at insolation 900W/m2 the output power of PVM is 280 but at 10°C the power of PVM is 270.

Novel Global-MPPT Control Strategy Considering the Variation in the Photovoltaic Module Output Power and Loads for Solar Power Systems

This research proposed a novel global maximum power point tracking (global-MPPT) algorithm. The proposed algorithm eliminates the perturbation and observation (P&O) technique disturbance problem that the power point will be stuck at the local peak power point under a partial shading condition (PSC). The proposed global-MPPT algorithm detects the photovoltaic module (PV-M) environment irradiance level by the relationship between the output power and voltage of the PV-M. In the proposed algorithm, the important parameter w is determined by the PV-M output power and irradiance level, which is also the compensation parameter that corresponds to the relationship of temperature. The proposed global-MPPT algorithm is aimed to predict the best duty cycle of the global-MPPT based on the irradiance level, parameter w, PV-M output voltage, and load, and then achieve the maximum power point (MPP) quickly and accurately. The measurement results under UIC and PSC verify that the proposed global-MPPT technique performs better than the particle swarm optimization (PSO) and P&O techniques. This research contributes to the proposed method that can find the global-MPP in time based on the irradiance level, temperature, and load.