Efficiency Of Solar Photovoltaic System Research Articles

Optimization of Neuro-controller Application for Maximum Power Point Tracking Photovoltaic Systems Through Shannon’s Information Criteria

Due to the extremely poor efficiency of solar energy, researchers created various maximum power point tracking (MPPT) techniques with the aim of enhancing the effectiveness of photovoltaic (PV) systems. Because of their propensity to address complex problems and their non-linear characteristics, Artificial Neural Network (ANN) algorithms are the most frequently used among these MPPT techniques. Nevertheless, the performance of the ANN-based MPP tracking algorithms is contingent upon various factors, including the choice of activation function, the quantity of hidden neurons, and the training algorithm employed. Shannon’s Information Criteria (SIC) is used to determine the optimal number of hidden neurons within a single hidden layer for the neuro-controller application. In this regard, a two-layer Feed-Forward Neural Network (FFNN) was trained using MATLAB/Simulink software, incorporating varying numbers of hidden neurons. The results indicate that the two-layer FFNN with five hidden neurons has the highest performance, as demonstrated by the lowest Mean Squared Error (MSE) of 4.03 × 10-9, which is statistically significant. The successful incorporation of Gaussian noise in the simulation of an 85 kW PV system demonstrates that the ANN-based MPPT algorithm is both theoretically robust and practically viable and reliable for enhancing the efficiency of solar PV systems in real-world scenarios.

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Optimization of Stable Energy PV Systems Using the Internet of Things (IoT)

The modern power grid faces rapid growth in load demand due to industrialization, leading to an unregulated environment and increasing adoption of renewable energy sources, which presents technical challenges, particularly in terms of stability. Hydrogen conversion technology revolutionizes clean electricity storage with renewable energy, and solar hydrogen is now available in autonomous solar systems. The efficiency of solar photovoltaic systems is closely related to using digital electronic maximum peak power tracking (MPPT) technology. The Internet of Things (IoT) is crucial for performance monitoring and real-time control of PV systems, enhancing the understanding of real-time operating parameters. IoT and wireless sensor networks for distributed solar energy devices and joint building design are essential for developing the photovoltaic construction industry. In this paper, the monitoring system that has been proposed offers a potentially effective solution for the intelligent remote and real-time monitoring of solar photovoltaic (PV) systems. It demonstrated a high level of accuracy, reaching 98.49%, and can transmit graphical representations to a smartphone application within a time frame of 52.34 seconds. Consequently, the battery's longevity was extended, energy consumption was diminished, and the quality of service (QoS) for real-time applications inside the Internet of Things (IoT) was enhanced.

Maximizing solar photovoltaic system efficiency by multivariate linear regression based maximum power point tracking using machine learning

Introduction. In recent times, there has been a growing popularity of photovoltaic (PV) systems, primarily due to their numerous advantages in the field of renewable energy. One crucial and challenging task in PV systems is tracking the maximum power point (MPP), which is essential for enhancing their efficiency. Aim. PV systems face two main challenges. Firstly, they exhibit low efficiency in generating electric power, particularly in situations of low irradiation. Secondly, there is a strong connection between the power output of solar arrays and the constantly changing weather conditions. This interdependence can lead to load mismatch, where the maximum power is not effectively extracted and delivered to the load. This problem is commonly referred to as the maximum power point tracking (MPPT) problem various control methods for MPPT have been suggested to optimize the peak power output and overall generation efficiency of PV systems. Methodology. This article presents a novel approach to maximize the efficiency of solar PV systems by tracking the MPP and dynamic response of the system is investigated. Originality. The technique involves a multivariate linear regression (MLR) machine learning algorithm to predict the MPP for any value of irradiance level and temperature, based on data collected from the solar PV generator specifications. This information is then used to calculate the duty ratio for the boost converter. Results. MATLAB/Simulink simulations and experimental results demonstrate that this approach consistently achieves a mean efficiency of over 96 % in the steady-state operation of the PV system, even under variable irradiance level and temperature. Practical value. The improved efficiency of 96 % of the proposed MLR based MPP in the steady-state operation extracting maximum from PV system, adds more value. The same is evidently proved by the hardware results.

Efficiency estimation and hardware implementation of solar PV module system assisted by using surface plasmon resonance sensor

This article presents a multi-mode approach for controlling and optimizing the efficiency of solar photovoltaic systems, as well as for analyzing the response output of optical sensing systems. Solar energy has been detected efficiently in this paper using a surface plasmon chip. Initially, the dual axis tracking system was designed and manufactured according to the standard procedure outlined in the literature. The primary factors affecting the efficiency of a photovoltaic panel are proper site heating and panel cleaning. As a result, the proposed system is aimed at increasing the efficiency of photovoltaic panels by addressing the aforementioned issues. Dual axis tracking ensures that the irradiance is always perpendicular to the photovoltaic panel. Additionally, the cooling system mitigates losses caused by panel heating, and the cleaning system increases the PV panel's efficiency. The solar photovoltaic system's output is analyzed using an optical sensing system equipped with a surface plasmon resonance (SPR) sensor. To monitor and control the performance of a proposed prototype solar photovoltaic system using the Resbri Pi3 controller. The proposed system's efficiency is calculated experimentally and presented to confirm the prototype's efficiency. The result is analyzed in the optical domain by the SPR sensor chip.

Application of RSAPSO Hybrid Optimizer for Parameter Extraction of Solar PV Cell Models under Temperature Variations

Accurate modeling of photovoltaic (PV) modules/cells is crucial for evaluating the efficiency of solar PV systems. However, the lack of specific parameters of solar cells, which are not included in the manufacturer’s datasheet, often results in flawed cell modeling. Quick and convenient parameter extraction techniques are required to overcome this challenge and create a robust solar PV cell model. These models are useful in optimization, simulation, and enhanced energy harvesting from PV-based renewable energy systems. This paper uses a recent optimizer called Rat Search Algorithm Particle Swarm Optimization (RSAPSO), for extracting the parameters of single diode and double diode models of PV cells. RSAPSO combines the exploration and exploitation advantages of both algorithms. The RSAPSO's parameter optimization results were compared against five other techniques, and the superiority of the suggested algorithm was confirmed through ranking tests, statistical error analyses, and temperature variation sensitivity analyses.

An improved numerical model to predict the operating temperature and efficiency of solar photovoltaic systems.

Solar photovoltaic (PV) technology has a huge potential for producing renewable energy and reducing greenhouse gas emissions. An increase in the PV cell temperature in real operating conditions reduces the actual output of a solar PV system. A 1D transient multi-layered model, based on the fundamentals of the finite difference method, has been developed to predict the operating cell temperature. Since a PV system operates in stochastic wind conditions and is not subjected to any predefined thermal boundary condition, several expressions of convection coefficient have been scientifically analyzed to determine the most suitable expression. The novel calculation approach assumes explicit radiation terms and implicit convection terms to linearize the equations and get rid of any iterative process. Comparison with experimental results shows that the convection coefficient derived from boundary layer theory corresponding to uniform heat flux predicts the cell temperature with the best accuracy showing a mean error of only [Formula: see text] and [Formula: see text]. Splitting the heat source across different solar PV layers produces a maximum change of [Formula: see text] only and can be avoided due to the involved complexity. The study proposes a new piece-wise function for PV efficiency in terms of cell temperature and irradiation. This novel function predicts PV efficiency on a sunny and a cloudy day with [Formula: see text] and [Formula: see text] mean errors, respectively, which are considerably lower than errors obtained using other popular functions in the literature. The model helps in predicting actual output from a PV system more accurately which should enable taking more informed decisions regarding the location of installation, PV technology, and the need for a cooling method.

Evaluation and Solution Suggestions for Engineering and Workmanship Failures during Design and Installation of Solar Power Plants

Among the various renewable energy generation systems, the solar photovoltaic occupies a leading position today due to its simple structure. However, increasing the efficiency of solar photovoltaic systems is a highly researched topic. In this study, possible connection failures in maximum power inverters and other failures, which decrease the efficiency in solar power plants, are examined. Furthermore, the possible consequences of these losses and their effects on the performance of solar power plants are explained. Some missing-failure processes were identified and corrected in the field analysis of the solar power plant in Turkey. Detected missing failures include connection failures of solar inverters, incorrect network configuration of camera system, fixing lighting time settings. The inverter string connection failure made during the projecting and assembly phase was eliminated and the maximum output was determined as 584.25 kW after the DC string arrangement. An increase of approximately 10% was achieved in production. In the project and application phase, the connection details of the inverters should be drawn and given to the field application personnel as a full-fledged project. In this way, incorrect connections that are not shown in the project and made in the field are prevented. This ensures that the installed power plant operates more efficiently, and the budgetary payback period of the investments made is shortened.

An Advanced MPPT Scheme for PV Systems Application with Less Output Ripple Magnitude of the Boost Converter

The purpose of this paper is to enhance the performance and tracking efficiency of solar photovoltaic systems. This aim can be achieved by operating the photovoltaic array at its optimum power and reducing the output ripple problem of DC-DC converters that affect and stress sensible electronic loads. In view of that, an advanced maximum power point (MPP) tracking (MPPT) scheme, which can guarantee zero oscillation tracking of the accessible MPP and less ripple magnitude on the output side of the DC-DC boost converter, is used. Various simulations are carried out under three conditions of solar irradiance variation, namely, standard test conditions (STC), rapid, and Sin scenarios, using the MATLAB/Simulink® environment, to assess and benchmark the robustness of the tracking of the new MPPT scheme over the celebrated Increment of Conductance (INC) MPPT scheme. Based on the simulation results, the proposed scheme can significantly improve tracking accuracy and reduce the magnitude of ripples on both sides of the boost converter compared to the INC scheme. Certainly, the proposed scheme can provide a shorter time response (0.011 seconds) to locate and track the expected MPP, which is 2.55 times less than that of the INC scheme; a zero power magnitude oscillation instead of 15.9 watts of the INC scheme; and six-time minimization of the magnitude of output voltage ripples compared to the INC scheme. Furthermore, the suggested MPPT scheme has the better tracking efficiency in all scenarios; 99.86%, 99.60%, and 99.62% in the STC, rapid, and Sin scenarios, respectively, with an average value of 99.69% compared to the INC MPPT scheme, which has 94.23%, 95.28%, and 97.87% in the STC, rapid, and Sin scenarios, respectively, with a moderate average tracking efficiency of 95.79%. Finally, the accuracy and tracking performance of the proposed MPPT scheme are verified by real-time examination using the RT-LAB simulator. According to the results obtained, the proposed scheme provides the highest tracking efficiency of 99.80% and 97.77% under the STC and sudden insolation change scenarios, respectively, compared to the INC scheme, which shows, respectively, 97.8% and 96.5% under both scenarios.

Risk matrix approach of extreme temperature and precipitation for renewable energy systems in Malaysia

Renewable energy is crucial to reducing climate change as it replaces fossil fuel-based energy sources partially. However, renewable energy facilities are highly dependent on climate conditions. There have been few studies in Malaysia focusing on the impacts of extreme temperature and precipitation on energy systems. None of the studies used the risk assessment method to examine the effects. This research aims to study the possible risks associated with common renewable energy systems in Malaysia (solar photovoltaic, anaerobic biogas system, biomass, and hydropower) using a risk matrix approach. The mitigation action plan to reduce extreme weathepact on each energy system will also be presented in this study. Three extreme temperature and precipitation scenarios were used to explore the potential impacts on infrastructures, energy resources, power capacity, and efficiency of solar photovoltaic systems, hydropower, biomass, and biogas power plant. The trend of daily temperature and precipitation in Malaysia from 1970 to 2070 is also presented. By 2070, Malaysia will be experiencing greater change in rainfall, as well as warmer temperatures and more droughts. Results of the study show that biomass is the most vulnerable to extreme temperature and precipitation.

Design of Boosted Multilevel DC-DC Converter for Solar Photovoltaic System

Integration of renewable energy sources to the grid-connected system has influenced scholarly research in recent times to evolve solutions for power electronic conversion. Particularly, solar photovoltaic (SPV), being a resource available throughout the year, demands needful research to meet the demand for industrial applications. To facilitate SPV, multilevel inverters (MLIs) and cascaded H-bridge inverters (CHBIs) are proposed in the literature to meet the power requirement. However, these circuits suffer from efficiency loss, economic aspects of DC sources usage, and switching losses. Hence, in this research, a new power converter topology is projected to improve the overall efficiency of SPV systems. Further, a three-level approach involving (i) SPV Panel-Temperature Reduction (SPV-PTR) Setup, (ii) Boost Multilevel Direct Current Link Converter (BMLDCLC), and (iii) use of effective snubber modules (SM) are effectively handled to promote the industry readiness of the proposed system. From a detailed system investigation, it is seen that the proposed arrangement has minimized the power loss to ensure better quality in output. Furthermore, the software-based results and hardware setup of the planned comprehensive converter have shown promising results in terms of (i) reduced voltage stress, (ii) reduced total harmonic distortion (THD) without filter component, and (iii) reduced power loss. It is observed that the experimental setup has reported a 12.9% of excess heat removal, 5% decrease in harmonics, and 33% switch reduction than the existing MLI schemes. In addition, the proposed setup is suggested to apply for industrial purposes indicate its efficacy to be a solution in real time.

Second law analysis of the 160 Wp standalone solar photovoltaic system

ABSTRACTThe demand for solar photovoltaic (SPV) systems is growing all over the world due to the continuous increase in the cost of conventional means of power. India has the advantage of around 300 clear sunny days in a year. However, the biggest problem is conversion efficiency. In this paper, an attempt has been made for evaluating the second law efficiency of 160 Wp stand-alone SPV systems installed in RKGIT Ghaziabad premises. The performance of a SPV system depends on climate conditions like temperature, air velocity and a number of sunny days. The solar energy striking the solar panels gets converted into electric and thermal energy. In this work, we have considered the effect of ambient temperature and air velocity on the efficiency of solar panels. The average second law efficiency of SPV systems was found to be 10.7%, whereas maximum second law efficiency was 13.8% at 9am on the same day. The efficiency of the SPV system can be improved by maintaining the temperature of the module.

Effects of Particle concentration, deposition and accumulation on Photovoltaic device surface

Abstract The efficiency of solar photovoltaic system is affected by many factors, among which the influence of dust deposition on photovoltaic panels is a prominent problem. In the desert which has sufficient radiation, the problem of dust deposition is quite serious. In some developed cities, photovoltaic devices are affected by dust or aerosol deposition. This short paper, thus, briefly summarizes studies of dust deposition on PV panels of urban and desert areas in recent years, and analyses the influence of dust deposition on the panel, particle characteristics and dust deposition process based on these studies. In general, there are three fields that suggests more research: (1) study on characteristics of sand or dust combined with local environmental characteristics; (2) modeling and simulation of particle dynamics around photovoltaic array; (3) comprehensive laboratory designed experiments to understand the specific influencing factors.

Assessment of solar PV power potential over Asia Pacific region with remote sensing considering meteorological factors

The intensity of solar radiation (SR) is one of the most important required inputs for the estimation of photovoltaic (PV) power station output. Meanwhile, the efficiency of solar PV systems is affected by meteorological factors such as temperature, dust, precipitation, and snow. Meteorological data from satellites provide a viable way for estimating PV potential due to its advantage in spatial coverage and temporal resolution. This paper presents a new approach to adjust SR data from satellites based on the cloud optical thickness (CLOT) before evaluating the solar PV power (PPV) potential, with the effective efficiency of solar cells computed based on temperature, dust, precipitation, and snow. The objective of this study is to evaluate the over-all spatiotemporal solar PV potential in the Asia Pacific region which will holistically include limiting meteorological factors and identify which factor contributes most significantly to the decrease in solar PV potential in selected cities in the region. First, SR and CLOT data from Advanced Himawari Imager 8 and a SKYNET station were processed to derive the correction factor for solar radiation data. Second, satellite data for temperature (MOD11), precipitation (global satellite mapping of precipitation), dust (MOD04), and snow cover (MOD10) were processed to derive the effective solar PV efficiency. Finally, maps showing the seasonal PV power potential over the Asia Pacific region were generated, with selected cities zoomed in for detailed analysis using mean monthly values from March 2016 to February 2017. The results showed that the maximum theoretical PPV in the region was estimated to be 1.9 GW per 17.5 km2 effective pixel area. Moreover, PPV decreased by maximum values of 180 MW, 550 MW, and 225 MW due to temperature, dust, and snow, respectively. For Beijing, Tokyo, and Jakarta, the major contributor to the decrease in PPV is dust, while Khabarovsk is consistently affected by snow effects. Initial validation of the model shows over- and underestimation of solar PV output compared to the actual values by as high as 30%. However, very high values of coefficient of determination (>0.90) show promising results of the model. The contribution of this study is two-fold: regional-scale assessment of PPV potential and investigation of the collective effect and individual contributions of dust, temperature, and snow to the decrease in PPV potential.

A sunny future: expert elicitation of China's solar photovoltaic technologies

China has emerged as the global manufacturing center for solar photovoltaic (PV) products. Chinese firms have entered all stages of the supply chain, producing most of the installed solar modules around the world. Meanwhile, production costs are at record lows. The decisions that Chinese solar producers make today will influence the path for the solar industry and its role towards de-carbonization of global energy systems in the years to come. However, to date, there have been no assessments of the future costs and efficiency of solar PV systems produced by the Chinese PV industry. We perform an expert elicitation to assess the technological and non-technological factors that led to the success of China’s silicon PV industry as well as likely future costs and performance. Experts evaluated key metrics such as efficiency, costs, and commercial viability of 17 silicon and non-silicon solar PV technologies by 2030. Silicon-based technologies will continue to be the mainstream product for large-scale electricity generation application in the near future, with module efficiency reaching as high as 23% and production cost as low as $0.24/W. The levelized cost of electricity for solar will be around $34/MWh, allowing solar PV to be competitive with traditional energy resources like coal. The industry’s future developments may be affected by overinvestment, overcapacity, and singular short-term focus.

Optimize the Energy Conversion Efficiency for Solar PV Grid-Connected System at Low Distribution Voltage

Renewable energy found in many forms such as solar and wind energy, tidal energy, etc. In this work, we interested to focus on solar power because of it is clean and the large amount of solar radiation arrived to the surface earth. The main purpose of this article aims to maximize the amount of extracted power from the solar system. This research investigates the connotation of maximum power point tracking (MPPT) methods which significantly increases the efficiency of the solar photovoltaic (PV) energy system. This paper presents a simulation based comparison between two different MPPT methods to optimize the energy conversion efficiency of solar PV system.

The operational performance of “net zero energy building”: A study in China

There is no lack of studies on “net zero energy buildings” (“nZEB”). However, the vast majority of these studies focus on theories and simulation. The actual operational performance of “net zero energy building” during occupation has been largely overlooked by previous studies. This study aims to investigate the operational performance of net “zero energy buildings” via the case study of an office building in Tianjin, China. Using simulation, the energy consumption of the building at design phase was estimated and a solar photovoltaic (PV) system was selected. A whole year operation of the occupied building showed that energy consumption of the case building was much higher than the energy generated from the solar PV system. This was mainly due to three issues. Firstly, the equipment was different in terms of category, quantity and running time between operation and design stages, leading to considerable underestimate of energy consumption at the design stage. Secondly, the operational strategies need to be further improved in order to regulate users’ behaviors. Thirdly, the efficiency of solar PV system was substantially reduced due to poor atmospheric environment (i.e. haze weather). Therefore, during the design process of “net zero energy buildings”, it is imperative to ensure that the energy simulation accurately reflects how the building will actually operate once occupied. The research also revealed other barriers to the design and implementation of “nZEB” in China, such as extra efforts required for effective communicating the capacity of the HVAC design and systems to clients, and the increased cost of “nZEB” (e.g. solar PV system) particularly for public buildings. Finally, the solar radiation intensity of standard year adopted in the simulation needs to be replaced by the most recent meteorological data.