Maximizing the carbon reduction in substations with minimum cost investments can be achieved by taking advantage of the potential of substations in terms of the envelope and renewable energy, which is significant in promoting carbon reduction in substations. Therefore, firstly, the relationship between building cost–energy consumption–carbon emissions is explored, and then the global optimal calculation model of substation envelope–renewable energy is established, with the lowest life-cycle carbon emission of the substation as the optimization goal. Finally, the validity of the model is verified based on a case study of a typical 110 kV outdoor substation. The model calculation results show that, without considering the cost constraint, Harbin has the highest maximum carbon reduction of 180,350 kg, which is 25.15% and 13.74% higher than the maximum carbon reduction in Shanghai and Haikou, respectively. Furthermore, based on the comparison of the cost and benefits of each carbon reduction technology, a prioritization of various carbon reduction technologies is given for each climate zone. The model established in this paper can provide the optimal configuration of substation carbon reduction technologies with different incremental cost constraints, and provide a reference for the low-carbon design of substations.
Since AlphaGo beat the world Go champion in 2016, which attracted wide attention, the neural network has become more and more popular in recent years, and people’s research on it has gradually improved and been applied in different fields. Today, artificial intelligence and machine learning have become an essential part of modern society and intelligent systems. We do image recognition, speech recognition, and visual learning, closely related to machine learning. However, in machine learning, unsatisfactory training results or even training failure is always encountered. Therefore, in machine learning, it is imperative to improve the accuracy of neural network training results. In this paper, speech recognition, image processing, MNIST, and other classical neural network models will be used to set the training parameters of the neural network better and improve the accuracy of training through voting, quantization, restart, and other methods. The part of research is aiming to find the relationship between restart numbers on the training process and the total extent of learning improvement. At the same time, several algorithms on utilizing these restart numbers are to be compared and selected. Finally, the conclusion is drawn that the more restart made in the training process with a convolutional neural network, the less profit on accuracy improvement we gain from restarting the process.
The correct installation of spacer bars is an important part of ensuring people’s daily life. This paper studies and implements a spacer bar installation confirmation system based on a deep learning object detection algorithm, which makes up for the deficiencies in the installation. Among the object detection algorithms, Faster-RCNN has higher detection accuracy, is more accurate than one-stage and can detect multi-scale and small objects. Through historical use, it is found that Faster-RCNN is excellent in detecting multi-class image sets tasks. For personal data sets, only fine-tuning is needed to achieve better results, and it also has a fast detection rate. Therefore, this paper is based on the Faster-RCNN algorithm to realize the detection and confirmation of the plug and rubber during the installation of the spacer bar.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.