AC contactors are used frequently in various low-voltage control lines, so remaining-life prediction for them can significantly improve the operational reliability of power control systems. To address the problem that the existing AC contactor remaining-life prediction methods do not make full use of the correlation between previous and later states in the degradation process, a CNN-GRU (convolutional neural network-gated recurrent unit) method for AC-contactor remaining-life prediction is proposed. Firstly, the entire cycle of an AC contactor’s degradation data is obtained through a whole-life test, from which the characteristic parameters that effectively reflect the operating states of the contactor are extracted; secondly, neighborhood component analysis (NCA) and maximal information coefficient (MIC) are used to eliminate the redundant information of multidimensional parameters in order to select the optimal feature subset; and then, CNN is used to compress the feature dimension and mine the regular information between the features, so as to extract the effective feature vectors; finally, taking the AC contactor remaining electrical life as a long time sequence issue, time-series accurate prediction is performed using GRU. It is verified that this model is better than RNN (recurrent neural network), LSTM (long short-term memory) and GRU models in prediction, with an effective accuracy of 96.63%, which effectively supports the feasibility of time-series prediction in the field of the remaining-life prediction of electrical devices.
Self-excited DC air circuit breaker(SE-DCCB) has been widely used in urban rail transit for its excellent stability. It can realize forward and reverse interrupting, but has difficulty in interrupting small current due to the phenomenon of arc root sticking at the entrance of the arc chamber in the splitting process which is known as arc root stagnation. A coupling model of self-excited magnetic field and magnetohydrodynamics (MHD) is established for the SE-DCCB with the traditional structure. The magnetic field, temperature and airflow distribution in the arc chamber are investigated with interrupting current 150 A. The simulation results show that the direction and magnitude of magnetic blowout force are the dominant factors of the arc root stagnation. The local high temperature of the arc chamber due to arc root stagnation increases the obstruction effect of airflow vortex on the arc root movement which increases the arc duration time of small current interrupting seriously. Based on the research, the structure of magnetic conductance plate of the actual product is improved which can improve the direction and magnitude of the magnetic blowout force at the arc root, so as to restrain the development of airflow vortex effectively, and solve the problem of the arc root stagnation when small current is interrupted. The simulation results show that the circuit breaker with improved structure has a better performance for small current interrupting range from 100 A to 350 A.
Calculus equation is an important tool for mathematical research and plays an important role in most natural science research. Since the beginning of the 18th century, people have gradually used differential and integral equations to solve physical problems. In general, several different aspects of differential equations in the field of mathematics are concerned and studied by most scholars. However, this paper studies and establishes the optimal model for numerical solution of differential equations through deep learning and genetic algorithm. In this paper, the solution of ordinary differential equations is solved through the use of polynomial function space, while the linear combination of simple function x and its product nx can obtain multinomial function space. The space function form of polynomial is very simple, and the operation ability is very strong. Almost all functions can be approximated, and the function space can be transformed by a simple function. Through data simulation test results, it can be found that the oscillation of neural network output is stronger and stronger with the increase of depth, that is to say, the deeper depth endows the neural network with stronger oscillation properties, so for the oscillation function, the depth neural network fitting effect is better than the shallow neural network. Therefore, by combining deep learning and genetic algorithm, this paper studies and establishes the optimal model for numerical solution of differential equations, and finds that the deep neural network can largely complete data simulation.
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