In order to improve the accuracy of predicting the remaining electrical life of AC circuit breakers, ensure the safe operation of electrical equipment, and reduce economic losses caused by equipment failures, this paper studies a method based on the Savitzky–Golay convolution smoothing long short-term memory neural network for predicting the electrical life of AC circuit breakers. First, a full lifespan test is conducted to obtain degradation data throughout the entire life cycle of the AC circuit breaker, from which feature parameters that effectively reflect its operational state are extracted. Next, principal component analysis and the maximum information coefficient are used to remove redundancy in the feature parameters and choose the best subset of features. Subsequently, the Savitzky–Golay convolutional smoothing algorithm is employed to smooth the feature sequence, reducing the impact of noise and outliers on the feature sequence while preserving its main trends. Then, a secondary feature extraction is performed on the smoothed feature subset to obtain the optimal secondary feature subset. Finally, the remaining electrical lifespan of the AC circuit breaker is treated as a long-term sequence problem and the long short-term memory neural network method is used for precise time-series forecasting. The proposed model outperforms backpropagation neural networks and the gate recurrent unit in terms of prediction precision, achieving an impressive 97.4% accuracy. This demonstrates the feasibility of using time-series forecasting for predicting the residual electrical lifespan of electrical equipment and provides a reference for optimizing the method of predicting remaining electrical life.
In order to suppress the busbar voltage fluctuations in the DC microgrid, this paper establishes an optical storage DC microgrid system with a hybrid energy storage system to achieve the purpose of stabilizing the DC bus voltage. This system focuses on the component hybrid energy storage unit, and uses the structure of three batteries and supercapacitors (SC) in parallel to improve the stability of the system, while ensuring the frequency division distribution between devices.
The reliability analysis of low-voltage switchgear is of great importance to the safety of power system. Bayes theory is a widely used reliability evaluation method because it can make full use of historical data. Therefore, the reliable prior information is the key factor to ensure the correctness of the reliability analysis results when the Bayes method is used to evaluate the reliability of the low-voltage switchgear. In order to obtain the priori information of low-voltage switchgear with high reliability, a feasible prior information fusion method was proposed and a reliable prior distribution function was obtained. First, kinds of historical data and effective information were collected from manufacturers and users. Then, to get more accurate prior distribution, the parametric and nonparametric test method was used to test the compatibility of the collected historical data, the effective information and the field information. Finally, the prior distribution and the expert experience were fused, and the conjugate prior distribution of the reliable low-voltage switchgear was constructed, which provided the theoretical basis for improving the accuracy of reliability evaluation of the low-voltage switchgear.
Abstract. This paper research on sensorless control of brushless dc motor control for electric bicycle. Based on the work requirements of electric bicycles, this paper introduces some method to improve the start of the motor and the detection of the motor's rotor position, expand the speed range of electric bicycle without hall and solve some problem in starting process. Finally, this paper verifies the feasibility of the scheme on the electric bicycle. IntroductionNow most of the control strategy of electric bicycle give priority to sensor control strategy. Sensorless control strategy is complementary. When the hall sensors can work normally in the electric motor, the sensor control strategy will be adopted. When the hall sensors are broken, the controller will switch into the sensorless control strategy, in order to ensure the electric bicycle can still run without hall sensor [1][2]. Although sensorless control strategy can ensure electric bicycle working, it can't satisfy customers' demand for high quality of running on electric bicycle [3][4]. Compared with the sensor control strategy, the sensorless control strategy of electric bicycle are still many deficiencies.Those are: 1) The motor speed, can be adjusted, is low.2) There are the dithering phenomenon in starting process. The sensorless control methodThe rotor detected method of sensorless control. Based on the electric bicycle's operation environment and operation requirements of the motor, this paper improves the back EMF ZCP(electromagnetic force zero-crossing point), optimizes the voltage comparison circuit, and enlarges the speed range of the method. In the process of normal operation of the simplified equation of brushless dc motor can be written as follows [5-6]: (1) (In the Eq. 1, 、 、 are the three-phase voltages of brushless dc motor, 、 、 are the three phase currents, 、 、 are the three phase opposite potentials, R is the resistance of three-phase, M is the mutual inductance between the phase winding, L is the self-inductance of the three-phase winding, and P is the differential operator )We can see that[4]:3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015)
This paper presents an optimization method for the dynamic characteristics of permanent magnet (PM) contactor based on fuzzy control. Firstly, The Dynamic characterization of PM contactor is analyzed. Secondly, fuzzy control model is established based on MATLAB. Finally, it is proved that this control method is feasible and effective. The simulation results show that the fuzzy control method can effectively optimize the dynamic characteristics of PM contactor.
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