A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation

Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is difficult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation. To auto-design an effective lightweight model, we introduce neural architecture search to the field of PV cell defect classification for the first time. Since the defect can be any size, we design a proper search structure of network to better exploit the multi-scale characteristic. To improve the overall performance of the searched lightweight model, we further transfer the knowledge learned by the existing pre-trained large-scale model based on knowledge distillation. Different kinds of knowledge are exploited and transferred, including attention information, feature information, logit information and task-oriented information. Experiments have demonstrated that the proposed model achieves the state-of-the-art performance on the public PV cell dataset of EL images under online data augmentation with accuracy of 91.74% and the parameters of 1.85M. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.Comment: 12 pages, 7 figure

Challenges faced by China compared with the US in developing wind power

In the 21st Conference of the Parties held in Paris in December 2015, China pledged to peak its carbon emissions and increase non-fossil energy to 20% by 2030 or earlier. Expanding renewable capacity, especially wind power, is a central strategy to achieve these climate goals. Despite greater capacity for wind installation in China compared to the US (114.7 vs. 65.9 GW), less wind electricity is generated in China (153.4 vs. 181.8 TWh). Here, we quantify the relative importance of the key factors accounting for the unsatisfactory performance of Chinese wind farms. Different from qualitative studies, we find that the difference in wind resources explains only a small fraction of the current US-China difference in wind power output (-17.9% in 2012); the curtailment of wind power, differences in turbine quality and delayed connection to the grid are identified as the three primary factors (respectively -49.3%, -50.2%, and -50.3% in 2012). Improvements in both technology choices and the policy environment are critical in addressing these challenges.Engineering and Applied Science

Tropical Cyclones and Climate Change

Trabajo presentado en: 10th International Worskshop Cyclones Tropicales, celebrado del 5 al 9 de diciembre de 2022 en Bali, Indonesia.A substantial number of studies have been published since the IWTC-9 in 2018, improving our understanding of the effect of climate change on tropical cyclones (TCs) and associated hazards and risks. They reinforced the robustness of increases in TC intensity and associated TC hazards and risks due to anthropogenic climate change. New modeling and observational studies suggested the potential influence of anthropogenic climate forcings, including greenhouse gases and aerosols, on global and regional TC activity at the decadal and century time scale. However, there is still substantial uncertainty owing to model uncertainty in simulating historical TC decadal variability in the Atlantic and owing to limitations of observed TC records. The projected future change in the global number of TCs has become more uncertain since IWTC-9 due to projected increases in TC frequency by a few climate models. A new paradigm, TC seeds, has been proposed, and there is currently a debate on whether seeds can help explain the physical mechanism behind the projected changes in global TC frequency. New studies also highlighted the importance of large-scale environmental fields on TC activity, such as snow cover and air-sea interactions. Future projections on TC translation speed and Medicanes are new additional focus topics in our report. Recommendations and future research are proposed relevant to the remaining scientific questions and assisting policymakers

Existence and Global Stability of a Periodic Solution for Discrete-Time Cellular Neural Networks

A novel sufficient condition is developed to obtain the discrete-time analogues of cellular neural network (CNN) with periodic coefficients in the three-dimensional space. Existence and global stability of a periodic solution for the discrete-time cellular neural network (DT-CNN) are analysed by utilizing continuation theorem of coincidence degree theory and Lyapunov stability theory, respectively. In addition, an illustrative numerical example is presented to verify the effectiveness of the proposed results

A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy

Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction

Wind speed prediction is the key to wind power prediction, which is very important to guarantee the security and stability of the power system. Due to dramatic changes in wind speed, it needs high-frequency sampling to describe the wind. A large number of samples are generated and affect modeling time and accuracy. Therefore, two novel active learning methods with sample selection are proposed for short-term wind speed prediction. The main objective of active learning is to minimize the number of training samples and ensure the prediction accuracy. In order to verify the validity of the proposed methods, the results of support vector regression (SVR) and artificial neural network (ANN) models with different training sets are compared. The experimental data are from a wind farm in Jiangsu Province. The simulation results show that the two novel active learning methods can effectively select typical samples. While reducing the number of training samples, the prediction performance remains almost the same or slightly improved

Space electromagnetic interference analysis of secondary equipment signal cable

In the field of high-voltage transmission and distribution, the secondary equipments are influenced by the electromagnetic coupling effect generated by the primary circuit, which causes strong interference to the signal cable. In this paper, an equivalent circuit model is established for the secondary cables in different forms, namely conventional cable, coaxial cable and twisted pair cable, based on transmission line theory. Based on the model, the responses of these three kinds of signal cables under the impact of spatial plane electromagnetic wave are analysed in frequency domain. Different impedance characteristics of the cables at the near-end and far-end, and different cable layouts, and different injection directions of the electromagnetic wave are considered in the analysis. The response characteristics of different kinds of signal cables subjected to electromagnetic wave are obtained in frequency domain. By comparing the response characteristics of different kinds of signal cables in different conditions, measures to supress the interference from the coupling of electromagnetic wave are obtained. It can be used to guide the selection and cabling of the signal cables for the secondary equipment in power system