Performance Of Photovoltaic System Research Articles

Effects of cooling and interval cleaning on the performance of soiled photovoltaic panels in Muscat, Oman

Due to the present alarming environmental problem, the world has turned to photovoltaic (PV) solar energy as a sustainable, renewable, and environmentally friendly alternative. Solar energy technology has rapidly improved in recent decades, and the global installation of photovoltaic systems has been increasing at a rate of 24% annually. However, soiling on panels and high panel temperature affect the performance of photovoltaic systems, and this leads to significant production loss, especially in the Middle East, where the dust intensity and ambient air temperature are at their highest levels. This paper presents the influence of cooling and interval cleaning on the performance of the photovoltaic system installed in Muscat, Oman. The study involved cleaning the first panel at a comparable inclination angle. The second panel had its inclination angle changed. The third panel was designed to inject water for cooling purposes. In this case, the panel was subjected to water injection for 30 s once per week, followed by daily water injection for 120 s in the second week. All the results were compared with the fourth panel, which was neither cleaned nor touched. Measurements, including open circuit voltage, short circuit current, solar panel temperature, solar radiation, and power, were made in 1-min intervals using a data logger. The results of the experiment showed that cleaning the panel manually every three days resulted in the highest output power and efficiency, reaching 85.6 W with 23.6% efficiency. The cooling of the panel resulted in an increase in power, especially for the longer injection duration. Conclusively, water injection resulted in an energy output growth of 23.9%.

Novel applications of various neural network models for prediction of photovoltaic system power under outdoor condition of mountainous region

The geography and landscape of mountainous locations are frequently varied, which causes uneven solar radiation exposure in different places which leads to photovoltaic (PV) power generation variation drastically. Therefore accurate PV power prediction is essential for industries in optimize energy production, Energy Planning and Grid Integration, Energy Storage Integration in such diverse conditions. This study aims to predict the power produced by a 2.680 kWp PV under outdoor condition in mountainous region at different time intervals of 10 seconds, 1 minute, 30 minutes, 1 hour and 1 day using different data analytics models. The results show that the radial basis neural network was the most effective model for the intervals of 10 seconds, 1 minute, 30 minutes, and 1 day. Its RMSE was 19.11 W for the 10-second interval, 22.83 W for the 1-minute interval, 25.94 W for the 30-minute interval, and 9.08 W for the 1-day interval. With an RMSE of 23.22 W, the Kernel Ridge Regressor had the best performance for the 1-hour period. This suggests that the model with only two parameters such as temperature and irradiance predict PV power more accurately. The findings can be applied to improve PV system design and performance by precisely forecasting the systems' power output in different weather scenarios.

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns.

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

Climate‐ and Technology‐Dependent Performance Loss Rates in a Large Commercial Photovoltaic Monitoring Dataset

Detailed knowledge of the performance of photovoltaic (PV) systems over their full lifetime is invaluable for evaluating their profitability and sustainability. PV systems are known to have gradually declining performance; for long‐term yield assessments, typically a performance loss rate (PLR) of −0.5% yr−1 is assumed. Herein, monitoring data of thousands of commercial PV systems is evaluated to determine how performance loss trends and PLR values are affected by operational climate, PV module technology, and other parameters. Using performance ratio (PR) measurements from almost 6000 PV systems, the long‐term trends in PR and overall PLR value are evaluated. Subsequently, the relation between climate, performance loss, and PLR for three different climate classification schemes is evaluated. Finally, how long‐term performance loss trends and estimated PLR values differ for PV module technology and other parameters are evaluated. The results show that in general, PV systems that have operated now for at least 3 but up to 25 years show a PLR of roughly −1% yr−1, indicating substantially higher performance loss compared to typical estimates used for new PV systems. Climate by itself does not seem to be the leading factor that determines overall performance loss trends or PLR values in the dataset analyzed.

Black-hole optimisation algorithm with FOPID-based automation intelligence photovoltaic system for voltage and power issues

ABSTRACT The article discusses the challenges related to voltage and power management in automated intelligent photovoltaic (PV) systems. It suggests that Fractional Order Proportional Integral Derivative (FOPID) controllers can help address these challenges effectively. The research aims to create a highly adaptable and precise automated intelligent PV system that focuses on managing voltage and power issues. It introduces a novel approach using the Black Hole Optimization (BHO) algorithm and leverages the advantages of FOPID controllers for automation. The study also considers thermal models for batteries within the automated intelligent PV system and explores different options to handle voltage and power issues stemming from PV panels. It involves fine-tuning 15 parameters using the BHO algorithm and concludes that one configuration (Instance 1) is preferable due to its superior accuracy and scalability. This choice is supported by objective validation. In essence, your abstract is about improving the performance and automation of PV systems, particularly in dealing with voltage and power challenges, through the application of FOPID controllers and the innovative BHO algorithm.

Improving the overall performance of a hybrid photovoltaic system by reflectors and cooling with water jet

In the present research, the overall performance of the hybrid photovoltaic thermal PVT system used in the production of electric power and water heating has been evaluated. The PVT was coupled with reflectors to increase the energy production and cool it by water jet. The experimental tests were conducted in Iraq – Samarra located at the longitude (43 40 East) and the latitude (34 35 North). Several experiments were carried out through April 2021 in the outdoors. The highest overall instantaneous efficiency was 64%, and the highest percentage improvement in the PVT electrical performance with reflectors was 42.6% at a (0.0084 kg/s) load flow rate. The water temperature in the thermal storage tank increased by about (6°C/6 h) when the electric heater was turned on, while it decreased (6°C/6 h) without turning it on, and it consumed 18.7% of the total batteries energy, which makes it an ideal solution to compensate for the period of sun absence.

Wireless internet of things solutions for efficient photovoltaic system monitoring via WiFi networks

<div align="center"><span>The imperative for sustainable energy production has necessitated the significant expansion of renewable energy sources, particularly photovoltaic (PV) systems. The utilization of real-time monitoring and data analysis is imperative to enhance the efficiency and performance of photovoltaic systems. This abstract presents developing and deploying a wireless monitoring system for a photovoltaic system. The system utilizes a Raspberry Pi device connected to a WiFi network and an SD card for data storage to enable remote monitoring and management of PV systems. The proposed monitoring system comprises a Raspberry Pi equipped with sensors to measure various parameters such as voltage, current, temperature, and the ambient conditions of the solar panels; the monitoring system can be remotely accessible through the wireless capabilities of the Raspberry Pi, which are activated by establishing a connection to an existing WiFi network. The proposed configuration facilitates the placement of the monitoring station in any desired location, hence eliminating the requirement for intricate wiring connections. These real-time data enable solar system managers to quickly identify anomalies, anticipate breakdowns, and optimise energy production. The paper presents a wireless monitoring system with a cost-effective and scalable solution for monitoring photovoltaic systems.</span></div>

Design of a novel control hysteresis algorithm for photovoltaic systems for harmonic compensation

Solar photovoltaic (PV) system design and integration with an existing AC grid is growing very fast in recent years and used by many of them as they are pollution-free, structure is limited and maintenance free. From the factors considering, the performance of PV system depends upon the inverter output voltage tested for linear, non-linear, with harmonic, and without harmonic loads. Generated due to the nonlinear loads. Better inverter control techniques are developed to maintain grid power quality. This article discusses the analysis and comparison of pulse width modulation (PWM) converters with unique and state of the art nonlinear control schemes and various modulation approaches. The primary objective of this research is controlling an active filtering hysteresis PWM converter with no sensors. A simple structure with hysteresis current control method total harmonic distortion (THD) is lower when compared with the sinusoidal pulse width modulation (SPWM) method. The said claims are supported by employing computer simulations using MATLAB/Simulink and different control approaches, such as proportional plus integral and artificial neural network controllers.

Proposal for the Sustainable Electrification of a Primary Healthcare Centre (PHC) Facility in Nigeria

Energy is a prerequisite to running health facilities and is, therefore, key to the success of the service. Karshi primary healthcare centre (PHC) facility is confronted with an unreliable power supply that leads to a high cost of generating power from diesel to operate its equipment. This facility needs a sustainable and reliable electricity supply and therefore a change in their energy system to enable it improve its quality of healthcare delivery services. A solar energy-based electricity generation system that would make the PHC to become resilient and contribute to climate mitigation was selected. A step by step on the design of solar powered system for the health facility was demonstrated, and therefore a grant to develop, install and train health workers on how to use the solar powered system effectively is needed. This program will measure the increased availability of energy, and there is a need for a follow-up program to measure the improvement in healthcare delivery. It was suggested that a data logging system and energy metering equipment should be installed to monitor the function and performance of the solar photovoltaic (PV) system. Monitoring and evaluation is specifically for project installation and management, and recommend that monitoring and evaluation team be set up by the Ministry of Health to collect data that will be used to measure these improvements. This project is aimed at providing 24 hours green and reliable electricity supply to the facility, and reduce CO<sub>2</sub> emissions compared to diesel generator. The project will cost 27,169.00 euros, and the organization of this paper follows EREF grant proposal guidelines.

Analysis of Grid-tied Solar Photovoltaic Energy Generation under Uncertain Atmospheric Conditions Using Adaptive Neuro-fuzzy Control System

The grid-tied photovoltaic (PV) power system has remained the most practical and sustainable configuration among renewable energy generation systems. Although uncertainties persist in solar irradiance and temperature, the grid-tied system faces transient instability issues during maximum power point tracking, adversely affecting power quality and resulting in substantial costs. To overcome this issue, we proposed analyzing the grid-tied system under uncertain atmospheric conditions based on an adaptive neuro-fuzzy control system (ANCS). This control scheme incorporates a hybrid learning algorithm and undergoes evaluation across various operating conditions. The obtained results demonstrate the effectiveness of the learning algorithm in maintaining a fast convergence speed. Consequently, this capability ensures the consistent preservation of sufficient power quality in the power system without any discernible transient impact. Furthermore, the investigation reveals the significant impact of solar radiation and temperature on the performance of the solar grid-tied PV system. Specifically, temperature alone contributes to over 15% power reduction when reaching 45 °C. As the temperature decreases to 5 °C at 1000 W/m2 irradiance, the ANCS influences an increase in the system's power generation from 100.72 kW at 25 °C to 103.01 kW.

Theoretic and experimental performance of a grid-connected photovoltaic system: Multiple prediction model of efficiency and annual energy generation

In this article, the performance of a 3.36 kWp grid-connected photovoltaic system (GCPVS) under warm and subhumid weather conditions and the development of a predictive mathematical model is presented. Climate data of the 2021 year were used to evaluate energy generation, different types of performance, and efficiency. The average annual yield, corrected yield, array, and final yields were 6.45 h/day, 6.18 h/day, 5.16 h/day, and 4.97 h/day, respectively. The overall annual mean capacity factor and efficiency ratios were 20.73% and 77.22%, correspondingly. Experimental data were analyzed and correlated by multivariate linear regression (MLR) prediction and simulation to validate models. The MLR analysis showed that the efficiency is highly dependent on the temperature of the PV modules and that climatic parameters significantly affect the efficiency and output electric power. The prediction models for PV module efficiency, system efficiency, and direct current energy exhibit an uncertainty of ±1.04%, ±0.57%, and ±35.38 kWh, one-to-one. The monthly generation was compared with results obtained by Energy3D simulation-free software, showing an absolute error of ±2.33 kWh. This information can be used as a methodological tool for predicting efficiency and power generation in direct current.

Simulation of integration of photovoltaic solar system with storage unit incorporating mixture of paraffin and nanoparticles

In this study, we conducted a simulation of the cooling process for a silicon layer within a Photovoltaic (PV) system, integrating a paraffin layer. To augment the cooling rate and expedite the melting process, we introduced nanoparticles into the paraffin (specifically, RT28HC). The dynamic nature of the process was accurately modeled using an implicit method. To further enhance the melting rate, the container was equipped with fins. The incorporation of a Phase Change Material (PCM) like paraffin with PV technology represents a novel approach in this research. This combination not only improves the cooling efficiency but also addresses the issue of expedited melting, offering a significant advancement in the field. By effectively utilizing the latent heat features of the paraffin, we can enhance the overall performance and reliability of PV systems, potentially leading to more efficient energy conversion and utilization. The equations were explained using FVM, accounting for the positioning of the hot wall. Gravity was omitted from the model, and a uniform approximation was applied for mixture characteristics. The introduction of fins led to a notable increase in the melting rate by approximately 42.9 %. Additionally, the incorporation of nano-powders resulted in a reduction of melting time by about 6.27 % and 4.39 % in scenarios both with and without fins, respectively. These outcomes signify that the presence of fins and the inclusion of nano-powders significantly influence the charging process. Fins enrich the rate of melting, while the adding of nano-powders accelerates the overall melting time. This research highlights the potential benefits of utilizing these enhancements in practical applications, potentially leading to more efficient and effective melting processes.

Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier

Accurate and reliable fault detection procedures are crucial for optimizing photovoltaic (PV) system performance. Establishing a trustworthy PV array model is the primary step and a vital tool for monitoring and diagnosing PV systems. This paper outlines a two-step approach for creating a reliable PV array model and implementing a fault detection procedure using Random Forest Classifiers (RFCs).Firstly, we extracted the five unknown parameters of the one-diode model (ODM) by combining the current–voltage translation method to predict the reference curve and employing the modified grey wolf optimization (MGWO) algorithm. In the second step, we simulated the PV array to obtain maximum power point (MPP) coordinates and construct operational databases through co-simulations in PSIM/MATLAB. We developed two RFCs: one for fault detection (a binary classifier) and another for fault diagnosis (a multiclass classifier).Our results confirmed the accuracy of the PV array modeling approach. We achieved a root mean square error (RMSE) value of 0.0122 for the ODM parameter extraction and RMSEs lower than 0.3 in dynamic PV array output current simulations under cloudy conditions. Regarding the fault detection procedure, our results demonstrate exceptional classification accuracy rates of 99.4% for both fault detection and diagnosis, surpassing other tested models like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Neural Networks (MLP Classifier), Decision Trees (DT), and Stochastic Gradient Descent (SGDC).

Designing Large scale Photovoltaic Systems

This paper presents a plan and procedure for the design and performance analysis of large-scale grid-connected solar Photovoltaic (PV) systems. A 1MW grid-connected PV system was designed and its performance was analyzed over the guaranteed life of the system using a photovoltaic system performance analysis program. In the system designed and analyzed in this paper, half of the produced electricity is fed to the load and the other half is stored to the grid. If the solar system cannot produce electricity for the load, then the load will use power from the grid. A photovoltaic system is a power system designed to supply usable solar power by means of photovoltaic panels. It consists of an arrangement of several components, including solar panels to absorb and convert sunlight into electricity, a solar inverter to change the electric current from DC to AC, as well as mounting, cabling, and other electrical accessories to set up a working system.

Powering the Future: Advancements and Applications of Photovoltaic Systems with Political, Economic, and Environmental Considerations

The paper explores the significant role of photovoltaic (PV) technology in the global energy mix and its diverse range of applications, including power generation for residential and commercial buildings, transportation, and industry. Building-integrated photovoltaic systems offer numerous benefits over traditional solar panels, such as enhanced energy efficiency, reduced visual impact, and simplified installation. The critical role of grid integration in photovoltaic systems, the utilization of photovoltaic systems for powering electric vehicles, and the application of floating solar farms are also emphasized. Additionally, the article discusses the influence of various factors, including solar irradiance, temperature, shading, and system design and installation, on the performance of photovoltaic systems during their operation. The article also sheds light on emerging technologies, such as software tools, numerical models, and experimental methods, used for performance modeling and analysis. Despite challenges associated with the photovoltaic industry, such as initial investment costs and intermittency issues, supportive policies from governments and ongoing technological advancements are contributing to the accelerated growth of the photovoltaic industry and paving the way for a more sustainable future for future generations.

An accurate real time neural network based irradiance and temperature sensor for photovoltaic applications

The functioning of photovoltaic (PV) systems mainly depends on the climatic conditions, including incident solar irradiance intensity and module temperature. Thus, irradiance and operating temperature are key factors for assessing and monitoring the performance of PV systems. This work develops a PV solar cells-based sensor for accurate measurement of both incident irradiance and the PV operating temperature. The proposed sensor relies on a Neural Network (NN) soft sensor, trained to estimate PV modules incident solar irradiance and operating temperature without direct measurements. The trained NN model uses the short circuit current (ISC) and open-circuit voltage (VOC) of a reference PV module as inputs of the estimation. The accuracy of the proposed sensor is validated through indoor and outdoor experiments using a PV emulator and a real panel, respectively. Indoor results affirmed the accurate tracking capability of the proposed estimator for both irradiance and module temperature, in constant and rapidly changing conditions, including sudden irradiance changes. The estimator achieved an exceptional level of performance with a Mean Absolute Percentage Error (MAPE) of 1.45% for irradiance and 1.64% for temperature. Similarly, the outdoor experiments using real-time data further validate the obtained results with a Normalized Root Mean Square Error (NRMSE) of 1.22% for irradiance and 3.50% for temperature, outperforming other methods published in the literature.

Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm

Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system’s performance, influencing both current and energy production. While traditional methods have provided reasonable results for PV model variables, they often require extensive computational resources, which impacts precision and robustness and results in many fitness evaluations. To address this problem, this paper presents an improved algorithm for PV parameter extraction, leveraging the opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with opposition-based learning, provides an enhanced exploration capability and efficient exploitation of the search space, helping to mitigate the risk of entrapment in local optima. The proposed OBEDO algorithm is rigorously verified against state-of-the-art algorithms across various PV models, including single-diode, double-diode, three-diode, and photovoltaic module models. Practical and statistical results reveal that the OBEDO performs better than other algorithms in estimating parameters, demonstrating superior convergence speed, reliability, and accuracy. Moreover, the performance of the proposed algorithm is assessed using several case studies, further reinforcing its effectiveness. Therefore, the OBEDO, with its advantages in terms of computational efficiency and robustness, emerges as a promising solution for photovoltaic model parameter identification, making a significant contribution to enhancing the performance of PV systems.

Disaggregating Longer-Term Trends from Seasonal Variations in Measured PV System Performance

Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into its constituent components, allowing us to find out the impact of the system degradation, maintenance, and weather variations from the inherent seasonal patterns. Our results reveal significant trends in PV system performance, indicating the need for proactive maintenance strategies to mitigate degradation effects. Moreover, we quantify the impact of changing weather patterns and provide recommendations for optimising the system’s efficiency based on seasonally varying conditions. Hence, this study not only advances our understanding of the intricate variations within PV system performance but also provides practical guidance for enhancing the sustainability and effectiveness of solar energy utilisation in both residential and commercial settings.

Metal–organic framework-based atmospheric water harvesting for enhanced photovoltaic efficiency and sustainability

The global demand for photovoltaic (PV) cooling is projected to increase over the coming years, driven by the growing adoption of solar energy and the need to improve the efficiency and performance of PV systems.