The power sector is one of the key areas to achieve the carbon peaking and carbon neutrality goals. Therefore, it is of great theoretical and practical significance to study the driving factors of carbon emission reduction in the power industry. Based on the data of Jilin Province from 2011 to 2021, this study adopts LMDI decomposition method to decompose ten driving factors affecting carbon emissions year by year and explores the main reasons for the change of carbon emissions in Jilin Province. The results show that per capita GDP and the ratio of electricity generation to consumption are driving factors of electric carbon emissions in Jilin Province, while the main inhibiting factors are industrial power intensity and coal consumption. In order to further promote the emission reduction of electric power carbon in Jilin Province, corresponding measures should be taken from the aspects of demand side management, clean energy development, industrial green upgrading and fuel conversion technology.
The engineering application number of fiber optic current sensor (FOCS) is decreasing year by year since 2012 in China due to its reliability problems. However, the researchers and related enterprises have also made some constructive attempts on the study of fault diagnosis of FOCS. In this paper, the application status and the common fault modes of FOCS are analyzed. Three ways to diagnosing the soft and hard fault of FOCS are reviewed, including based on analytical model, on signal processing and on knowledge. Finally, the research direction of FOCS fault diagnosing is prospected. It is concluded that the diversified and intelligent fault diagnosis method based on knowledge has more advantages compared with the other two methods. In addition, the development of FOCS for integrated optical path is of great help in improving its reliability and will be a research hotspot in the future.
The fiber optic current sensor (FOCS) is susceptible to external temperature in actual operation, which will lead to its accuracy deviation, even malfunction. In order to improve the temperature stability of FOCS’s ratio error, a temperature compensation method based on RBF neural network is established by taking the temperature as input and the ratio error as output to the network. Compared with BP neural network, the simulation results show that the temperature compensation model based on RBF neural network has better accuracy whose prediction error is less than 3%. At the same time, the experimental results show that the drift deviation of ratio error can remain as low as ±0.1% in the range of -40 °C to 70°C, and the 0.2S-level accuracy of GBT20840.8 standard can be achieved.
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