Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.

Smart Monitoring of Solar Photovoltaic Panels by the Approach of Machine Learning

The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependable smart asset management techniques within the solar power industry.

Ghost-Retina Net:Fast Shadow Detection Method for Photovoltaic Panels Based on Improved Retina Net

Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.

Comparison of Different Models for the Simulation of Photovoltaic Panels

This article contains the description of a circuital model, which was developed to represent the energy production of a photovoltaic panel in a more accurate way, taking into consideration the decrease of its operational time. Furthermore, a comparison among the experimental, the posed simulated model in PSIM and the results obtained by a piece of software developed by some students of the Universidad Distrital is performed in order to verify the values provided by the software and demonstrate the optimal operation of the developed model.

Acquisition System for Photovoltaic Panels with Data Storage in Remote Sewer Platforms Using Open Source Platforms Raspberry Pi and Arduino

This paper consists of a prototype for a data acquisition system connected via wireless network for data storage on a remote server. This study presents the acquisition board and the operating principle of the whole system developed starting at the measurement of data up to its storage on a remote server. Using a remote server connected to the Internet implies the possibility of analysis, manipulation and control of such data from anywhere in the world.

Bionic Photovoltaic Panels Bio-Inspired by Green Leaves

In strong solar light, silicon solar panels can heat up by 70℃ and, thereby, loose approximately one third of their efficiencyfor electricity generation. Leaf structures of plants on the other hand, have developed a series of technological adaptations,which allow them to limit their temperature to 40-45℃ in full sunlight, even if water evaporation is suppressed. This is accomplishedby several strategies such as limitation of leaf size, optimization of aerodynamics in wind, limitation of absorbedsolar energy only to the useful fraction of radiation and by efficient thermal emission. Optical and infrared thermographicmeasurements under a solar simulator and in a streaming channel were used to investigate the corresponding properties of leavesand to identify suitable bionic model systems. Experiments started with the serrated structure of ordinary green leaves distributedover typical twig structures and finally identified the Australian palm tree Licuala ramsayi as a more useful bionic model. Itcombines a large area for solar energy harvesting with optimized aerodynamic properties for cooling and is able to restructureitself as a protection against strong winds. The bionic models, which were constructed and built, are analyzed and discussed.

Advancements in Photovoltaic Panel Fault Detection Techniques

This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.

Experimental Investigation of the Stability of the Performance Characteristics of a Photovoltaic Module in the Face of Environmental and Meteorological Factors

The explosive technological improvement of photovoltaic systems as well as the necessity of populations to come to less expensive energy sources, that have led to an implosion at the level of solar panel manufacturers. This causes a large flow of these equipments to developing countries where the need is high, without any quality control. That conducted an experimental investigation on the performance characteristics of a 250 wp monocrystalline silicon photovoltaic module in other to check the verification and quality control. Most of these PV panels which often have missing informations are manufactured and tested in places that are inadequate for our environmental and meteorological conditions. Also, their influences on the stability of internal parameters were evaluated in order to optimize their performance. The results obtained at maximum illumination (1000 w/m2) confirmed those produced by the manufacturer. The analysis of these characteristics showed that the illumination and the temperature (meteorological factors) influenced at most the stability of the internal characteristics of the module in the sense that the maximum power increased very rapidly beyond 750 w/m2 but a degradation of performance was accentuated for a temperature of the solar cells exceeding 50°C. The degradation coefficients were evaluated at -0.0864 V/°C for the voltage and at -1.6248 w/°C for the power. The 10° inclination angle of the solar panel proved to be ideal for optimizing overall efficiency in practical situations.

Variability of Performance Indices of Photovoltaic Solar Panels in Operating Conditions in the Littoral Zone of Cameroon

Photovoltaic cells are generally manufactured under standard test conditions. The operating conditions, very often induce performance losses different from those initially given by the manufacturer. This article presents an experimental acquisition and analysis system that integrates the synthetic efficiency ratio (SER) as a hybrid analysis tool to evaluate the performance of a monocrystalline photovoltaic solar panel, in this case the LW-MS90 panel in the city of Douala. The meteorological data obtained experimentally was used to evaluate these performances according to the manufacturer’s model in MATLAB/Simulink. By comparison with the experimental performances, the results quantify through a certain number of indices, a minimal power drop according to the acquired irradiance estimated at 3.45%. The interest of this approach is to contribute to the prediction of the operating performance of PV panels in the installation phase in non-standard areas.

Design of a Photovoltaic Panel Orientation System

This work highlights the design and the realization of an automatic solar-panel orientation system in order to achieve high-performances. The solar panel sensor constitutes the main part of the system, since it ensures the pursuit of the sunbeam. The management of the system, depending on the movements, the presence of sun, and the regular checkup of the system evolution, is ensured by an electronic unit executed around a microcontroller.

Promoting Energy Efficiency in the Palestinian Municipalities: A Case Study of Al-Dahriya Municipality

Energy demand overall the world increases rapidly in various sectors, one of the highest energy consumption sector is the building sector. Installation of PV systems is one of the solutions to cover this demand and will serve in promoting energy efficiency in the Palestinian municipalities in decreasing the electricity bill, and using the saved money in constructing new projects and improving the level of services provided to citizens. In this work, Al-Dahriya municipality has been taken as a case study. The municipality installed 20 KW of photovoltaic panels on the roof of the main building in 2015. The cumulative values for one year after installation the PV system represent a total consumed electricity by the main building was 71,506 kw, while the total generated power by the PV system that transferred to building was 32,664 kw, and 5323 kw exported to the grid with total generated power by PV system was 37,987 kw. The participation of energy that produced by the photovoltaic system is 53.12% of the total power demand of the building. The value of generated power varies between the summer months and winter months through the difference of the solar radiation intensity and the number of shinning hours, the largest reading of solar radiation intensity is in the summer months. The study ensures the importance of applying selected thermal insulation materials in order to decrease the heat transfer through the boundary wall of the building. Furthermore, this study covers the other buildings and utilities of municipality and recommended with certain issues in order to promote energy efficiency.

Dynamic Optimization of a 2-DOF Pseudo-Equatorial Tracking System

The key word for the design process of the photovoltaic tracking systems is the energetic efficiency. Using the tracking system, the photovoltaic panel follows the sun and increases the collected energy, but the driving motors consume a part of this energy. In these terms, the optimization of the tracking systems became an important challenge in the modem research and technology. In this paper, a strategy for the dynamic optimization of the photovoltaic tracking systems is presented. The main task in optimization is to maximize the energetic gain by increasing the incoming solar radiation and minimizing the energy consumption for tracking. This strategy is possible by developing the virtual prototype of the tracking system, which is a control loop composed by the multi-body mechanical model connected with the dynamic model of the actuators and with the controller model. In this way, it is possible to optimize the tracking mechanism, choose the appropriate actuators, and design the optimal controller.

Experimental investigation of photovoltaic systems for performance improvement using water cooling

This research aims to analyse the comparative performance of two identical photovoltaic(PV)panels with load variations and integrating an automated water-cooling process under the climatic conditions of the United Arab Emirates.The work also presents the steps of system design,implementation and performance evaluation of the proposed PV system,and all electrical,control and mechanical components along with how they were integrated within a 100-W PV system.MATLAB/Simulink?was used only to simulate the behaviours of the PV panel under wide ranges of incident sunlight and ambient temperature.The tests were performed for a day-long operation during a clear summer day.The experimental results demonstrate an improvement in the PV system performance compared with the uncooled system by~1.6%in terms of total harvested energy using the proposed water-cooling process with a frequency of 2 minutes of cooling operation every 30 minutes during day hours.

Design and performance testing of a novel building integrated photovoltaic thermal roofing panel

A novel building integrated photovoltaic thermal(BIPVT)roofing panel has been designed considering both solar energy harvesting efficiency and thermal performance.The thermal system reduces the operating temperature of the cells by means of a hydronic loop integrated into the backside of the panel,thus resulting in maintaining the efficiency of the solar panels at their feasible peak while also harvesting the generated heat for use in the building.The performance of the proposed system has been evaluated using physical experiments by conducting case studies to investigate the energy harvesting efficiency,thermal performance of the panel,and temperature differences of inlet/outlet working liquid with various liquid flow rates.The physical experiments have been simulated by coupling the finite element method(FEM)and finite volume method(FVM)for heat and mass transfer in the operation.Results show that the thermal system successfully reduced the surface temperature of the solar module from 88℃to as low as 55℃.Accordingly,the output power that has been decreased from 14.89 W to 10.69 W can be restored by 30.2%to achieve 13.92 W.On the other hand,the outlet water from this hydronic system reaches 45.4℃which can be used to partially heat domestic water use.Overall,this system provides a versatile framework for the design and optimization of the BIPVT systems.

Wide spectrum solar energy harvesting through an integrated photovoltaic and thermoelectric system

This paper proposes a power system concept that integrates photovoltaic （PV） and thermoelectric （TE） technologies to harvest solar energy from a wide spectral range. By introduction of the ＇spectrum beam splitting＇ technique, short wavelength solar radiation is converted directly into electricity in the PV cells, while the long wavelength segment of the spectrum is used to produce moderate to high temperature thermal energy, which then generates electricity in the TE device. To overcome the intermittent nature of solar radiation, the system is also coupled to a thermal energy storage unit. A systematic analysis of the integrated system is carried out, encompassing the system configuration, material properties, thermal management, and energy storage aspects. We have also attempted to optimize the integrated system. The results indicate that the system configuration and optimization are the most important factors for high overall efficiency.

Complete Parasitic Capacitance Model of Photovoltaic Panel Considering the Rain Water

Common mode current suppression is important to grid-connected photovoltaic(PV)systems and depends strongly on the value of the parasitic capacitance between the PV panel and the ground.Some parasitic capacitance models have been proposed to evaluate the magnitude of the effective parasitic capacitance.However,the proposed model is only for the PV panels under dry and clean environmental conditions.The dependence of rain water on the capacitance is simply described rather than analyzing in detail.Furthermore,the effects of water are addressed quite differently in papers.Thus,this paper gives complete parasitic capacitance model of the PV panel considering the rain water.The effect of the water on the capacitance is systematically investigated through 3D finite element(FE)electromagnetic(EM)simulations and experiments.Accordingly,it is clarified how the water affects the parasitic capacitance and methods of minimization of the capacitance are proposed.

Feasibility of Grid-connected Solar-wind Hybrid System with Electric Vehicle Charging Station

Recently,renewable power generation and electric vehicles(EVs)have been attracting more and more attention in smart grid.This paper presents a grid-connected solar-wind hybrid system to supply the electrical load demand of a small shopping complex located in a university campus in India.Further,an EV charging station is incorporated in the system.Economic analysis is performed for the proposed setup to satisfy the charging demand of EVs as well as the electrical load demand of the shopping complex.The proposed system is designed by considering the cost of the purchased energy,which is sold to the utility grid,while the power exchange is ensured between the utility grid and other components of the system.The sizing of the component is performed to obtain the least levelized cost of electricity(LCOE)while minimizing the loss of power supply probability(LPSP)by using recent optimization techniques.The results demonstrate that the LCOE and LPSP for the proposed system are measured at 0.038$/k Wh and0.19%with a renewable fraction of 0.87,respectively.It is determined that a cost-effective and reliable system can be designed by the proper management of renewable power generation and load demands.The proposed system may be helpful in reducing the reliance on the over-burdened grid,particularly in developing countries.

Efficient maximum power point tracker based on neural network and sliding-mode control for buck converters

This paper presents detailed design steps of an effective control system aiming to increase the solar energy harvested via photovoltaic power-generation systems.The design of an intelligent maximum power point tracker(MPPT)supported by a robust sliding-mode(SM)controller is discussed in this study.The proposed control scheme is designed to track the MPP and provide a smooth system response by removing the overshoot in the load current during any variation in the connected load.Such a system is suitable for DC-DC buck converter applications.The study begins with modelling the buck converter for a continuous current mode operation.The reference voltage of the tracking system is produced by the proposed neural network(NN)algorithm.The proposed intelligent MPPT integrated with an SM controller is simulated in a MATLAB®/Simulink®platform.The simulation results are analysed to investigate and confirm the satisfaction level of the adopted four-serially connected PV-modules system.The system performance is evaluated at a light intensity of 500 W/m^(2) and an ambient temperature of 25°C.Applying only the proposed NN algorithm guarantees the MPP tracking response by delivering 100 W at a resistive load of 13Ω,and 200 W at a load of 6.5Ω,respectively,with 99.77%system efficiency.However,this simultaneously demonstrates a current spike of~0.5 A when the load is varied from 50%to 100%.The integrated SM controller demonstrates a robust and smooth response,eliminating the existing current spike.