An ultra-short-term power prediction model based on machine vision for distributed photovoltaic system

Bao, Guanjun; Liubin, Tian; Cai, Shibo; Tong, Jianjun; Zhang, Linwei; Fang, Xu

doi:10.1109/icinfa.2015.7279459

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…In order to ensure the normal operation of photovoltaic panels, dust cleaning has been widely concerned. The cleaning methods of photovoltaic panels [9][10] mainly include fluid washing, mechanical wiping, compressed air dust removal and vibration dust removal. Xu et al [11] found that the optimal cleaning period of a 20 MW (p) photovoltaic electric field in Turpan, Xinjiang, under mechanical cleaning is 3.4 days, based on the linear theory of condenser fouling cleaning in thermal power plants.…”

Section: Introductionmentioning

confidence: 99%

Experimental study on cleaning efficiency of independent traveling solar panel cleaning machine

Zhu,

Chen,

et al. 2023

J. Phys.: Conf. Ser.

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As a kind of clean energy, solar energy plays a great role in helping China achieve the “double carbon” goal. However, dust cover will greatly affect the power generation efficiency of solar panels. In order to solve this problem, based on the self-designed independent traveling solar panel cleaning machine, orthogonal experiments were carried out, to study the influence of factors such as the no-load rotating speed of the roller brush, the flow rate of the front water spray component, the flow rate of the rear water spray component and the nozzle type on the improved value of the cleanliness after cleaning. The experimental verification of the optimal parameters of the power generation was also conducted. The results show that the nozzle type has the greatest impact on the cleaning effect, followed by the spray flow of the spray components on both sides of the roller brush. Taking the cleaning effect as the optimization index, the water mist nozzle should adopt the wide-angle small-aperture fan-shaped nozzle (11004 type), the cleaning water flow on both sides of the roller brush should be controlled at 500 L/h, and the rotational speed of the roller brush off the plate should be controlled at 200 rpm. If it is necessary to pay attention to water conservation, the water spray flow of the two ways can be moderately reduced. The optimized cleaning efficiency parameter configuration scheme has an obvious cleaning effect on the photovoltaic array, which can greatly improve the power generation of the array.

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Section: Introductionmentioning

confidence: 99%

Experimental study on cleaning efficiency of independent traveling solar panel cleaning machine

Zhu,

Chen,

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…A mathematical model of irradiance conversion and the photovoltaic module model were verified by experiments [19][20][21][22]. Open operation, quadratic polynomial fitting, gray-level segmentation analysis, centroid angle function, and cluster analysis are used to establish the cloud feature model [23][24][25], and then to carry out ultra-short-term photovoltaic power prediction. Although scholars have carried out a lot of work in cloud analysis and feature modeling and ultra-short-term prediction of photovoltaic power based on cloud analysis and modeling in recent years, there are still problems of difficult modeling [26], and the cost of deploying and maintaining ground cameras in power grids with a large number of photovoltaic power stations is high [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)

et al. 2023

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The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In order to improve the accuracy of distributed photovoltaic power generation prediction, this paper proposes a new distributed photovoltaic power generation prediction model: ROLL-GNN, which is defined as a prediction model based on rolling prediction of the graph neural network. The ROLL-GNN uses the perspective of graph signal processing to model distributed generation production timeseries data as signals on graphs. In the model, the similarity of data is used to capture their spatio-temporal dependencies to achieve improved prediction accuracy.

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Fluctuation-sensitive model-predictive communication for distributed energy resources

Kleinschmidt

Wietfeld

2016

2016 IEEE International Conference on Communications Workshops (ICC)

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An ultra-short-term power prediction model based on machine vision for distributed photovoltaic system

Cited by 3 publications

References 3 publications

Experimental study on cleaning efficiency of independent traveling solar panel cleaning machine

Experimental study on cleaning efficiency of independent traveling solar panel cleaning machine

Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)

Fluctuation-sensitive model-predictive communication for distributed energy resources

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