Electron multiplier tube can be used in the field of electron beam measurement, which can be used to measure the small current electron beam after amplification. In the measurement, in addition to the internal error of the multiplier tube, different incident electron beams will also affect the magnification of the multiplier tube. In order to analyze the error of different incident electron beam parameters on the electron multiplier tube, this paper uses CST to establish the electron multiplier tube model. After verifying the correctness of the simulation model, the energy, incident angle and radius of the incident electron beam are simulated and analyzed. The simulation results show that The parameters of the incident electron beam mainly affect the first multiplication electrode, including its collection efficiency and the number of secondary electrons generated, and ultimately affect the magnification of the electron multiplier tube.
Fiber-optic current transformer (FOCT) is widely used in DC power transmission and transformation system of power grid. Power grid users pay attention not only to its measured output value, but also to its state quantity. They expect to find equipment failure in advance by monitoring the internal state parameters of optical path and reduce the risk of abnormal shutdown. This work mainly analyzes the modulation and demodulation process of FOCT based on PZT modulation, and extracts the condition monitoring parameters for fault diagnosis. And the influence of typical low-temperature fault process on outdoor modulation circuit devices is analyzed, and the data of performance deterioration process of modulation tank is collected through accelerated aging test, which explains the influence of performance deterioration of low-temperature devices on state parameters.
KEYWORDS: Data modeling, Neural networks, Principal component analysis, Error analysis, Data mining, Transformers, Data processing, Statistical analysis, Performance modeling, Binary data
With the continuous updating of communication network technology and the influence of different factors (such as humidity, specific gravity, temperature, etc.), the monitoring data acquired by the grid equipment is exponentially increasing and the complexity of the data is also continuously improving. Taking full advantages of these big data, studying the measurement characteristics of electronic transformers in operation and discovering the relationship of environment, load and other factors will help optimize the performance of electronic transformers, give users a better experience and improve the benefits of the companies. However, the emergence of massive data makes traditional data analysis methods unable to meet the accuracy and real-time performance of data processing. Therefore, how to effectively and accurately solve the big data analysis and processing problems is particularly urgent. To effectively process this data, we have chosen the popular data mining method. Compared to traditional machine learning, we choose a relatively simple deep learning network for data mining. A feed forward neural network is used for classification. On the basis of classification, a new network is established to perform nonlinear regression prediction on the data, then an error transfer model is established. In the regression prediction problem, due to the high dimensionality and high computational complexity of the original data, we use the PCA method to reduce the feature dimension, which is also helpful to establish a nonlinear relationship between the learning characteristics of the deep neural network and the predicted values. Compared with the traditional feed forward neural network, the accuracy of our network has been significantly improved.
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