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Solar energy is one of the most important resources that can be a clean and renewable alternative to traditional fuels. The collection process of solar energy mainly rely on the photovoltaic solar cells. The defects, such as microcracks and finger interruption on the photovoltaic solar cells can reduce its efficiency a lot. To solve this problem, defects detection of solar cells have attracted attention from many researchers. In this paper, we propose a transformer based network to detect defects on solar cells efficiently and effectively. First, we introduce convolutions into the transformer to enable the input embeddings of the transformer, positional information of patches and spatial context more accurate and precise. Secondly, cross window based multi-head self-attention (CW-MSA) is proposed to enlarge the window relation modeling capacity via the strong attention mechanism and can be an effective alternative to the original counterpart. Finally, we propose a multi-scale aggregation block to merge the low-level features into deep semantically strong features by attention to obtain accurate geometry information. Extensive experiments on the elpv dataset demonstrate DPiT can consistently bring significant improvements over its strong baseline Swin Transformer with subtle extra computational overhead. The visualization results show that the proposed DPiT is able to detect various complex defects correctly. In particular, DPiT can achieve impressive 91.7 top-1 accuracy and greatly outperforms other competitive counterparts.

Photovoltaic (PV) output power is significantly random and fluctuating due to its sensitivity to meteorological factors, making PV power forecasting a big challenge. Accurate short-term PV power forecasting plays a crucial role for the stable operation and maintenance management of PV systems. To achieve this target, the paper proposes a novel Spatial-Temporal Genetic-based Attention Networks (STGANet), which consists of a spatial-temporal module (STM) and a genetic-based attention module (GAM). STM serves to predict the missing solar irradiance to support the generation forecast, and contains a graph convolutional neural network to learn the spatial and temporal dependencies between historical meteorological data, while using dilated convolution as the non-linear part to simplify the network structure. The GAM efficiently explores for potential relationships in input features with attentional mechanism and uses genetic-based operation and LSTM which takes forecasting error as reference to find global optimal solutions and to avoid getting trapped in local optimal solutions. The model is verified through comparative experiment with several benchmark models using a real-world historical meteorological dataset and a power generation dataset of PV plants in southeastern China. The results have illustrated that the proposed model can provide better prediction performance in PV systems.

With the gradual consumption of fossil energy, the development and use of renewable energy become more and more important. Distributed photovoltaic power stations have become an important means to efficiently use solar energy for power generation. Under the influence of complex environmental factors, with the continuous expansion of photovoltaic power stations, photovoltaic system accidents occur frequently, and problems in operation and maintenance gradually emerge. In order to improve the informatization level of photovoltaic system and reduce operation and maintenance costs, this paper designs an ensemble model based on cross-validation, which integrates five classical classification algorithms. Through analyzing and processing multidimensional data of photovoltaic system, it realizes the function of fault diagnosis and fault classification of photovoltaic system. By comparing the performance of single model and ensemble model in photovoltaic fault diagnosis, it is found that the ensemble model has better diagnostic performance and can be used for photovoltaic system operation and maintenance and fault diagnosis.

SGCC invented State Grid Distributed Photovoltaic Cloud Platform (SG-DPCP) in 2018, which is the largest distributed photovoltaic public service cloud platform in China. Based on the power generation data of SG-DPCP, in use of Big Data analysis technology, this paper proposes a method to accurately diagnose the fault types of photovoltaic buildings. The effectiveness of this method is verified by a specific application study.

With the development of new energy and the increase in demand, photovoltaic power generation has received more and more attention. However, due to the susceptibility of photovoltaic power generation to the influence of weather factors, especially the influence of solar irradiation, it is intermittent and fluctuating. Understanding the power generation data of a PV plant has a great effect on the subsequent photovoltaic power generation planning and photovoltaic power plant situation assessment. However, these data are often incompletely recorded due to frequent communication problems or equipment failures. Most current studies focus on photovoltaic power generation predictions, it is still a challenge to speculate the missing data around photovoltaic power plants. In this paper, a context-sensitive tensor decomposition model is proposed to intelligently fill the missing power generation entries. The experimental results show that our model can effectively fill missing data and obtain good results.

The existing operation and maintenance system of photovoltaic power station can analyze partial equipment problems, but due to the data storage, processing and transmission capacity can hardly meet the requirements of a large number of operation data acquisitions and processing of photovoltaic power station, the operation status of photovoltaic power station cannot be comprehensively evaluated. This paper proposes to analyze the overall efficiency and key link efficiency of the photovoltaic system through long-term monitoring, extract the efficiency model parameters of the key components and important links of the photovoltaic power station, and establish the performance evaluation implementation process. At the same time, based on different actual detection conditions, photovoltaic fault diagnosis is carried out by time domain reflectometry, prediction theory comparison and sensor optimization.