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.

A new dust detection method for photovoltaic panel surface based on Pytorch and its economic benefit analysis

Dust accumulation on the surface of solar photovoltaic panels diminishes their power generation efficiency,leading to reduced energy generation.Regular monitoring and cleaning of solar photovoltaic panels is essential.Thus,developing optimal procedures for their upkeep is crucial for improving component efficiency,reducing maintenance costs,and conserving resources.This study introduces an improved Adam optimization algorithm designed specifically for detecting dust on the surface of solar photovoltaic panels.Although the traditional Adam algorithm is the preferred choice for optimizing neural network models,it occasionally encounters problems such as local optima,overfitting,and not convergence due to inconsistent learning rates during the optimization process.To mitigate these issues,the improved algorithm incorporates Warmup technology and cosine annealing strategies with traditional Adam algorithm,that allows for a gradual increase in the learning rate,ensuring stability in the preliminary phases of training.Concurrently,the improved algorithm employs a cosine annealing strategy to dynamically tweak the learning rate.This not only counters the local optimization issues to some degree but also bolsters the generalization ability of the model.When applied on the dust detection on the surface of solar photovoltaic panels,this improved algorithm exhibited superior convergence and training accuracy on the surface dust detection dataset of solar photovoltaic panels in comparison to the standard Adam method.Remarkably,it displayed noteworthy improvements within three distinct neural network frameworks:ResNet-18,VGG-16,and MobileNetV2,thereby attesting to the effectiveness of the novel algorithm.These findings hold significant promise and potential applications in the field of surface dust detection of solar photovoltaic panels.These research results will create economic benefits for enterprises and individuals,and are an important strategic development direction for the country.

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.

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.