Output Power Of Photovoltaic Panel Research Articles

Influence of dust deposition on photovoltaic (PV) module performance at the different sites

This research paper has been designed to investigate the effect of small dust pollutants on a solar photovoltaic panel output power capacity under different environmental conditions. The impact of the various dust pollutants on photovoltaic (PV) modules has been assessed and analyzed. The results show how dust accumulation influences the PV module's performance through output power. The panel performance gradually decreases with increasing dust accumulation on PV glass plate. Consequently, A significant amount of power loss (12.52% - 16.29%) has been recorded at the different PV sites. In which, a maximum level of power loss (16.29%) was obtained for the lime-stone site.

Research of dust removal performance and power output characteristics on photovoltaic panels by longitudinal high-speed airflow

Photovoltaic (PV) panels' photoelectric conversion efficiency will decrease as dust deposition on their surface. An approach to dust removal on the PV panel's surface by longitudinal high-speed airflow was investigated to increase the output power. In this paper, commercial CFD software was used to numerically simulate the characteristics of dust removal by longitudinal high-speed airflow. The PV panels' output characteristic model under the action of dust removal by high-speed airflow is established by Simulink software, and the influence of high-speed airflow on the output characteristic is studied. Firstly, the dust removal mechanism of the PV panel under high-speed airflow is studied. Secondly, the impact of different tilt angles of the PV panel, dust particle size, airflow velocity, and blowing time on the dust removal effect of the PV panel surface were studied. The optimal airflow velocity and blowing time were obtained according to the airflow velocity and blowing time and their influence on the dust removal rate. Finally, the effect of high-speed airflow dust removal on PV power generation's efficiency and output characteristics is studied. The findings demonstrate that as the tilt angle increases, so does the dust removal rate increases continuously. In addition, the rate at which dust is removed increases with airflow velocity and blowing time. When the tilt angle is 30°, the best airflow velocity of dust removal is 5 m/s, and the best blowing time is 8 s. When the tilt angle is 45°, the best airflow velocity of dust removal is 10 m/s, and the best blowing time is 5 s. With the increase in airflow velocity and blowing time, the output power of PV panels continues to increase. When the airflow velocity is 10 m/s, and the blowing time is 5 s, 7.5 s, 12.5 s, and 15 s, the maximum output power after dust removal is increased by 15.44 %, 23.05 %, 23.38 %, and 23.39 %. This paper's research results can guide the design of the practical engineering application of longitudinal blowing high-speed airflow in the dust removal of PV panels.

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.

Dust deposition mechanism and output characteristics of solar bifacial PV panels.

The utilization of solar photovoltaic (PV) power generation represents a highly promising technological solution for addressing environmental challenges and energy crises. Dust deposition on the front and back surfaces of solar bifacial PV panels greatly decreases the optical performance and power generation. In this study, the dust deposition characteristics and mechanism of solar bifacial PV panels are investigated using the CFD-DEM method. The effects of the dust deposition rate on the output characteristics of bifacial PV panels are discussed. The research results show that the particle deposition behaviors on the back and front surfaces of bifacial PV panels are influenced by the deposition and separation forces at the left or right inlets. The dust deposition rate of windward surfaces can be 1.48-7.60 times that of the leeward surfaces of bifacial PV panels. The particle motion trajectories on the windward and leeward sides can be mainly divided into five and three kinds, respectively. The dust deposition rate of bifacial PV panels increases when the air inlet velocity decreases and the particle size and concentration and relative humidity increase. The open circuit voltage and short circuit current of bifacial PV panels decreased by 26.7% and 16.4%, respectively, when the dust deposition rate increases by 45.8%. The attenuation rate of the maximum output power of PV panels has a positive linear correlation with the dust deposition rate, as shown in Eq. 22. The bifacial PV panels have better output characteristics than the mono-facial PV panels with consideration of dust deposition.

Study on the performance of solar cells cooled with heatsink and nanofluid added with aluminum nanoparticle

The cooling of photovoltaic (PV) panels based on nanofluids is one of the emerging cooling methods to improve the efficiency of PV panels. In this study, the effects of aluminum nanoparticles on the cooling performance and conversion efficiency of PV panels were investigated experimentally. The surface temperature, output power, and efficiency of the PV panels were assessed in Mashhad, Iran on a sunny winter day in November 2020 under ambient temperatures between 10 and 17 °C. Experimental results indicated that the nanofluid with aluminum nanoparticle improved the solar panel efficiency and solar PV panel's output power by an average of 13.5 and 13.7%, respectively compared to that of water cooling without aluminum nanoparticles. A temperature reduction between 13.08 and 16.34 °C on the solar PV panels surface was observed for heatsink cooling with nanofluid containing aluminum nanoparticles. Overall, the results suggest that nanofluid added with aluminum nanoparticles is effective in improving the conversion efficiency of PV panels.

Solar Power Estimation Methods Using ANN and CA-ANN Models for Hydrogen Production Potential in Mediterranean Region

Among renewable energy sources, investments in solar and hydrogen energy as eco-friendly energy sources are continuously expanding. The energy generated by solar photovoltaic (PV) panels, varies during the day. In order to ensure energy continuity, hybrid systems are being developed by integrating solar energy systems with different energy systems. Solar-assisted hydrogen production energy systems have a number of advantages, such as having zero carbon emissions, being able to be used as fuel in fuel cells, and having a high energy density of hydrogen. The sustainable energy obtained from solar energy is used to meet the energy demand in numerous applications due to the high solar energy potential in Mediterranean Region. In this article, solar PV panel output power prediction based on Artificial Neural Network (ANN) and Cultural Algorithm-Artificial Neural Network (CA-ANN) for a solar-assisted hydrogen production energy system have been performed. In this study, the real time data of a 4 MW solar PV power plant is used to estimate solar PV output power. Predicting the output power of PV power plants with high accuracy is of great importance in the realization of energy management, hydrogen production and storage strategies. The numerical results obtained at the end of the study are presented. The results obtained with the ANN and CA-ANN methods are given in detail. In addition, the hydrogen production amount in the Mediterranean region is calculated using MATLAB/Simulink software. The analysis results show that the Mediterranean region has high efficiency hydrogen production potential.

Output Power Prediction of Solar Photovoltaic Panel Using Machine Learning Approach

Solar power-based photovoltaic energy conversion could be considered one of the best sustainable sources of electric power generation. Thus, the prediction of the output power of the photovoltaic panel becomes necessary for its efficient utilization. The main aim of this paper is to predict the output power of solar photovoltaic panels using different machine learning algorithms based on the various input parameters such as ambient temperature, solar radiation, panel surface temperature, relative humidity and time of the day. Three different machine learning algorithms namely, multiple regression, support vector machine regression and gaussian regression were considered, for the prediction of output power, and compared on the basis of results obtained by different machine learning algorithms. The outcomes of this study showed that the multiple linear regression algorithm provides better performance with the result of mean absolute error, mean squared error, coefficient of determination and accuracy of 0.04505, 0.00431, 0.9981 and 0.99997 respectively, whereas the support vector machine regression had the worst prediction performance. Moreover, the predicted responses are in great understanding with the actual values indicating that the purposed machine learning algorithms are quite appropriate for predicting the output power of solar photovoltaic panels under different environmental conditions.

Numerical simulation of the dust particles deposition on solar photovoltaic panels and its effect on power generation efficiency

The deposition of dust particles on the surface of solar photovoltaic panels leads to a decrease in power generation efficiency, so it is necessary to study the interaction mechanism between dust particles and solar photovoltaic panels. Aiming at the problem that the simplified two-dimensional model cannot reflect the actual photovoltaic power station in the traditional research, a geometric model of the distribution form of the 3 × 3 solar photovoltaic panel array is constructed. The shear stress transport k−ω turbulence model is used to predict the flow field changes around solar photovoltaic (PV). A discrete particle model is used to predict the particle deposition rate of photovoltaic panels. The effects of different wind speeds, particle sizes, and wind angles on particle deposition were analyzed. With the increase of wind speed, the deposition amount of the photovoltaic panels in the last two rows gradually decreased, while the photovoltaic panels in the first row showed a trend of first decreasing and then increasing. As the particle size increases, the particle deposition amount increases gradually. When the particle size is less than 60 μm, the deposition amount on the photovoltaic panel is less than 0.03g. When the particle size is greater than 60 μm, the deposition amount of particles increases exponentially. The maximum deposition amount is 3.98g, and the maximum deposition rate is 0.796% for 160 μm particles. Wind angle has different effects on the deposition on different rows of photovoltaic panels. When the wind Angle is 0°, the deposition amount of the first row of photovoltaic panels is the largest, the deposition amount is 4.66g, and the deposition rate is 0.932%. When the wind angle is 45°, the deposition amount on the second and third rows of photovoltaic panels is the largest, the deposition amount is 3.21g, and the deposition rate is 0.642%. Finally, the changes in the maximum output power of photovoltaic panels after 60 days of exposure were predicted using an improved dust particle occlusion model.

Comparative Analysis of Particle Swarm Optimization and Artificial Neural Network Based MPPT with Variable Irradiance and Load

The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed. The PV system consisting of PV array and boost converter with MPPT controller feeds the DC load. The power conversion and panel efficiency are the significant factors to determine the effectiveness of tracking maximum power point. The simulation results show the performance of these controllers on the PV panel output power and the load side output power under changing loads and irradiance. In addition, the comparison of PV panel efficiency of ANN and PSO based MPPT techniques w.r.t changing loads is carried out. Based on the above analysis, PSO based MPPT algorithm marginally outperforms the ANN based MPPT algorithm. Further, the implementation of hybrid MPPT (ANN &PSO) for higher accuracy and tracking capability can be carried out as future work.

Transient analysis of a green hydrogen production and storage system using Alkaline electrolyzer

Solar energy as a renewable energy resource has many applications in industry. Among them, green hydrogen production is one of the issues that has recently attracted a lot of attention. In this study, the time-dependent behavior of a solar-based hydrogen production system has been investigated by employing TRNSYS program. Results show that the hydrogen production of the system is significantly related to the seasonal climate changes during a year. The output power of photovoltaic panels is outmost in summer and spring. Due to restrictions on the volume of the storage tank, hydrogen production is limited, especially in summer. Investigations for a 5 m3 tank with a constant hydrogen discharge rate indicate that April and May are the most productive month of the year with 1137 m3 and 1110 m3, respectively. Total annual 46.67 MW of electrical power generated in panels produced over 9600 m3 of hydrogen. Increasing the tank volume to 20 m3 increases annual hydrogen production by 9%. Utilizing an increased discharge plan in summer for the 5 m3 storage tank also increased the productivity of the system by 12% for the same annual solar panel-generated power.

Development and Evaluation of Dust Cleaning System for a Solar PV Panel

The most promising application of solar energy is the conversion of solar energy into electrical energy by using solar photovoltaic (PV) panel. The performance of solar based PV panel is definitely influenced by the amount of solar radiation, which are reaching on the panel surface. Since the solar PV panels are operating in open atmosphere dust particles get deposited on their surfaces and most of the times they have to work in this condition. These deposited dust particles create a layer of dust particles over the panel surface which prevents the 100% penetration of solar radiation into the panel surface. Therefore, proper cleaning of the panel surface becomes very necessary. In order to improve the performance of the PV panel an automatic microcontroller driven dust cleaning technique is developed which is capable of removing the accumulated dust particles from the PV panel surface. Moreover, an experimental study has been performed to analyse the efficiency of this developed techn ique. The developed cleaning system showed an improvement of 27.98% in the output power of PV panel when compared to the dusty panel.

Research on Photovoltaic Grid-Connected Control Strategy Based on Active Disturbance Rejection of Adaptive Extended State Observer

In the photovoltaic inverter grid-connected power generation system, the output power of photovoltaic panels is affected by illumination and temperature. The change of output power of photovoltaic panels will lead to the fluctuation of DC bus voltage. If the control is improper, it will directly affect the regular operation of the system. In order to improve the performance of the grid-connected inverter system, an active disturbance rejection control method based on adaptive extended state observer (ESO) is proposed. Firstly, a feedforward PI current inner loop controller is designed, which simplifies the structure of the control system and improves the tracking performance of the current. Then, the DC bus voltage outer loop ADRC is designed, and a conversion method that ignores the essential difference between nonlinear/time-varying and time-varying/linear is proposed. Through the conversion of time-invariant nonlinear system and time-varying linear system, the stability of the extended state observer is proved by the Routh criterion. Secondly, to solve the problem of mutual restriction between the stability and observation accuracy of the extended state observer, an adaptive function online automatic tuning ESO parameter method is proposed. Finally, the simulation results show that the proposed method has better dynamic and static performance, and the grid-connected voltage and current harmonics are small, which proves the correctness and effectiveness of the proposed method.

A New Design of Dual-Axis Solar Tracking System With LDR Sensors by Using the Wheatstone Bridge Circuit

Nowadays, using photovoltaic (PV) cells is among the power generation methods that absorb solar energy and convert it into electrical energy. The sun moves from east to west during the day and its radiation angle changes relative to the Earth in different seasons. So, the output power of PV panels changes as well. The output power of PV panels increases by being located in a position perpendicular to the angle of the sun's rays. This study aims to design and implement a dual-axis solar tracker (DAST) to increase the output power of the PV panel. This simple system has high efficiency and adjusts the PV panel based on solar radiation by moving simultaneously on two axes. An analog controller is used for its control system. DAST control system is a closed-loop system that uses Wheatstone bridge circuit function along with light-dependent resistors (LDRs). A small DAST was designed and built to validate the proposed system, the performance of which was verified. Based on the experiments, I-V and P-V specifications were obtained. Finally, it was found that the output power of the PV panel using solar tracker was higher than that of the fixed panel.

Quantification of PV Power and Economic Losses Due to Soiling in Qatar

Soiling losses of photovoltaic (PV) panels due to dust lead to a significant decrease in solar energy yield and result in economic losses; this hence poses critical challenges to the viability of PV in smart grid systems. In this paper, these losses are quantified under Qatar’s harsh environment. This quantification is based on experimental data from long-term measurements of various climatic parameters and the output power of PV panels located in Qatar University’s Solar facility in Doha, Qatar, using a customized measurement and monitoring setup. A data processing algorithm was deliberately developed and applied, which aimed to correlate output power to ambient dust density in the vicinity of PV panels. It was found that, without cleaning, soiling reduced the output power by 43% after six months of exposure to an average ambient dust density of 0.7 mg/m3. The power and economic loss that would result from this power reduction for Qatar’s ongoing solar PV projects has also been estimated. For example, for the Al-Kharasaah project power plant, similar soiling loss would result in about a 10% power decrease after six months for typical ranges of dust density in Qatar’s environment; this, in turn, would result in an 11,000 QAR/h financial loss. This would pose a pressing need to mitigate soiling effects in PV power plants.

Hybrid Energy System with MPPT Techniques: A Review

The efficient use of energy produced from renewable energies is more important in this scenario. This document provides a detailed overview of the hybrid power system (HPS) with solar and MPPT controls. An autonomous solar system is the best choice for a rural area to provide uninterrupted energy. MPPT (Maximum Power Point Tracking) is generally used in photovoltaic (PV) systems to maximize the output power of photovoltaic panels regardless of climate change. Fast response and high tracking accuracy are two essential design requirements for MPPT control.

Matching of air conditioning power and PV panel output power based on dynamic controlling of air conditioning air supply volume

ABSTRACT This paper presents a dynamic control strategy of air-conditioning air supply volume based on statistical data of the spatial and temporal distribution of occupants in the building, aiming at reducing overheating in buildings under local heat island effect. Nowadays, energy consumption in air-conditioning system is relatively high when considering the limited number of occupants and chiller plants operating inefficiently because the traditional central air-conditioning system was not designed to operate at different cooling load demands. Therefore, variable air volume (VAV) air-conditioning system is applied in this paper with corresponding wind volume control method. To address the above issue, in this paper: (1) A spatiotemporal distribution of occupants in the building is analyzed, and the relationship between occupant distribution and characteristics of the thermal dissipating load is established; (2) A strategy for dynamic controlling of the air supply volume in time and space is proposed; (3) A distributed photovoltaic system supplies power to cooling load of the building, and a strategy is proposed for optimizing the match between cooling power and photovoltaic (PV) output power using competitive swarm optimization algorithm; and (4) Air blower total amount of wind control method is adopted in VAV air-conditioning system for dynamic controlling of the air supply volume. This paper uses a simulation to verify the feasibility and optimality of the above strategies. The simulation results show that the building energy consumption is greatly reduced by 39.52%, the PV accommodation is improved as 77.14%, and four control indicators are well satisfied due to the air blower total amount of wind control method.

Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNS), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network types, namely, the nonlinear autoregressive network with exogenous inputs (NARX) and the deep feed-forward (DFF) neural network, have been developed and compared for modeling the maximum output power of HPV panels. Both neural networks have four exogenous inputs and two outputs. Matlab/Simulink is used in evaluating the proposed two models under a variety of atmospheric conditions. A comprehensive evaluation, including a Diebold-Mariano (DM) test, is applied to verify the ability of the proposed networks. Moreover, the work further investigates the two developed neural networks using their actual implementation on a low-cost microcontroller. Both neural networks have performed very well; however, the NARX model performance is much better compared with DFF. Using the NARX network, a prediction of PV output power could be obtained, with half the execution time required to obtain the same prediction with the DFF neural network, and with accuracy of ±0.18 W.

Shading position effects on photovoltaic panel output power

Shading position effects on photovoltaic panel output power

Measuring factors influencing performance of rooftop PV panels in warm tropical climates

The integration of photovoltaic (PV) panels and green roofs, which is a system known as green roof integrated photovoltaics (GRIPV), can provide mutual benefits such as improving the conversion efficiency of the PV panels and reducing the incident solar irradiation on the vegetation. Although previous studies have quantified the increase in the output power of PV panels in these systems, there is a need for experimental research to characterise the influence of several factors on system performance. Accordingly, this work presents an experimental study that seeks to describe the influence of three factors on the performance of PV panels in warm tropical climates: height of installation (25, 50, 75, and 100 cm), type of roof (green-vegetated and concrete), and air velocity (0, 1.15, and 2.10 m/s). The results obtained indicate that concrete roofs slightly increase the surrounding air temperature up to 100 cm above the surface, but this effect is not observed on green roofs. In addition, a PV panel installed at heights between 50 and 75 cm on a green roof can generate up to 1.3 ± 0.4% more energy than one installed on a concrete roof. Finally, increasing the air velocity from 0 to 2.10 m/s on either surface can produce up to 2.0 ± 0.4% more energy per day from each PV panel.

Research on a Photovoltaic MPPT Based on Fuzzy Control Algorithm

In photovoltaic power generation systems, the output power of photovoltaic panels is greatly affected by the environment. Experiments show that each point formed by temperature and light intensity corresponds to a power output curve of a photovoltaic panel. Under conditions with certain external conditions, guarantees Photovoltaic panel output maximum power becomes a difficult problem for PV development and utilization. In the traditional MPPT algorithm, disturbance observation method is widely used. Although the method has fast response and simple structure, the accuracy is not high, and it fluctuates at the maximum power point. More severe, causing a waste of power to a certain extent. For this problem, an improved algorithm based on fuzzy control is proposed to optimize the disturbance observation method to further increase the tracking speed of the maximum power and to eliminate the static error to some extent. The output power of the photovoltaic panel is kept at its maximum.