scholarly journals Insulation Monitoring of Dynamic Wireless Charging Network Based on BP Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 129
Author(s):  
Feng Wen ◽  
Wenjie Pei ◽  
Qiang Li ◽  
Zhoujian Chu ◽  
Wenhan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Power Conversion ◽  
Experimental Results ◽  
Calculation Error ◽  
Insulation Resistance ◽  
Wireless Charging ◽  
Monitoring Method ◽  
Insulation Monitoring ◽  
On Line

The transmission cable and power conversion device need to be buried underground for dynamic wireless charging of an expressway, so cable insulation deterioration caused by aging and corrosion may occur. This paper presents an on-line insulation monitoring method based on BP neural network for dynamic wireless charging network. The sampling signal expression of the injection signal is derived, and the feasibility of this method is verified by experiments, which effectively overcomes the problem of large calculation error of insulation resistance when the cable capacitance to ground is large. The experimental results indicate that the error of the proposed method is less than 9%, which can meet the needs of insulation monitoring.

The Design of Insulation Monitoring Device for Electric Vehicle's Battery Pack

2013 ◽  
Vol 718-720 ◽  
pp. 1422-1428 ◽  
Author(s):  
Min Xin Zheng ◽  
Qing Sen Yang ◽  
Shuo Ying Chen
Keyword(s):  
Software Design ◽  
Negative Electrode ◽  
Experimental Results ◽  
Battery Pack ◽  
Monitoring Device ◽  
Insulation Resistance ◽  
Monitoring Method ◽  
Insulation Monitoring

A novel insulation monitoring method which can monitor the insulation resistance between both the positive and the negative electrode of the electric vehicle's battery pack and the vehicle's body is proposed. The insulation monitoring method proposed in this paper is deduced theoretically, and the hardware and software design of an insulation monitoring device based on this method is given. The experimental results prove the correctness of the insulation monitoring method and the practicability of the insulation monitoring device.

scholarly journals Calculation for Primary Combustion Characteristics of Boron-Based Fuel-Rich Propellant Based on BP Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Wu Wan'e ◽  
Zhu Zuoming
Keyword(s):  
Neural Network ◽  
Burning Rate ◽  
Ammonium Perchlorate ◽  
Bp Neural Network ◽  
Calculation Model ◽  
Combustion Characteristics ◽  
Experimental Results ◽  
Calculation Error ◽  
Backpropagation Neural Network ◽  
Practical Scheme

A practical scheme for selecting characterization parameters of boron-based fuel-rich propellant formulation was put forward; a calculation model for primary combustion characteristics of boron-based fuel-rich propellant based on backpropagation neural network was established, validated, and then was used to predict primary combustion characteristics of boron-based fuel-rich propellant. The results show that the calculation error of burning rate is less than %; in the formulation range (hydroxyl-terminated polybutadiene 28%–32%, ammonium perchlorate 30%–35%, magnalium alloy 4%–8%, catocene 0%–5%, and boron 30%), the variation of the calculation data is consistent with the experimental results.

scholarly journals Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1385
Author(s):  
Sheng Wu ◽  
Kwok L. Lo
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Bp Neural Network ◽  
Distribution Curve ◽  
Electrical Characteristics ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Current Frequency ◽  
Operation Pattern ◽  
Load Monitoring

Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.

Study on Method of Recognizing Characteristics of Pipeline Leakage Acoustic Signals

2006 ◽  
Author(s):  
Likun Wang ◽  
Dongjie Tan ◽  
Yongjun Cai ◽  
SongGuang Fu ◽  
Jian Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Field Experiment ◽  
Frequency Band ◽  
Bp Neural Network ◽  
Acoustic Signals ◽  
Pressure Variation ◽  
Experimental Results ◽  
Acoustic Sensors ◽  
Experiment Data

Wavelet package and neural network are used to recognize the characteristics of pipeline leakage acoustic signals. Acoustic signals produced by pressure variation of pipelines can be detected by the acoustic sensors installed on the pipelines. The detecting accuracy can be increased with recognizing the acoustic signals correctly. The method to detect acoustic signals by combining the wavelet package and neural network is introduced in this paper. The signal is decomposed with wavelet package firstly, then the decomposed coefficients in each frequency band are obtained through reconstruction. As a result, the parameters of the new sequences reconstructed on every decomposed node are acquired, and then these parameters are input to BP neural network to recognize the fault reason intelligently. At the end of the paper, field experiment data and their analyzed results are studied. The experimental results are provided to show that the proposed method can increase the accuracy efficiently.

On-Line Calculation Method of Exhaust Steam Humidity Based on BP Neural Network for Steam Turbine

2013 ◽  
Author(s):  
Likun Zheng ◽  
Chang Chen ◽  
Danmei Xie ◽  
Hengliang Zhang ◽  
Yanzhi Yu
Keyword(s):  
Neural Network ◽  
Steam Turbine ◽  
Bp Neural Network ◽  
Calculation Method ◽  
Thermal Power ◽  
Engineering Application ◽  
Measuring Method ◽  
Factors Affecting ◽  
On Line ◽  
Turbine Exhaust

For condensing turbine, steam exhaust point is in wet steam area. The exhaust steam humidity of steam turbine is difficult to get due to lacking of effective measuring method. Calculation of exhaust steam humidity has always been one of the key parts of the analysis of thermal power units. The main factors affecting exhaust steam humidity are turbine load and turbine exhaust pressure etc, and they are of non-linearity. This paper develops a calculation method to calculate exhaust steam humidity based on BP neural network. Taking a N1000-25/600/600 ultra-supercritical (USC) steam turbine as an example, the exhaust steam humidity is calculated and the results show that the method has a good accuracy to meet the needs of the engineering application.

Creep Behavior Prediction Research of T92 Steel Based on BP Neural Network

2013 ◽  
Vol 467 ◽  
pp. 203-207
Author(s):  
Jian Liu
Keyword(s):  
Neural Network ◽  
Creep Behavior ◽  
Bp Neural Network ◽  
Network Theory ◽  
Experimental Results ◽  
Behavior Prediction ◽  
Forecast Precision ◽  
Network Method ◽  
Simulation Results ◽  
Neural Network Theory

Based on the BP neural network theory, the creep rate prediction model of T92 steel was established under multiple stress levels. Obtained the experimental results and using the model, the experimental results were trained. The results show that the simulation results match the measured results well with a high forecast precision. The BP neural network method can serve as research on T92 steel creep behavior.

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

On-Line Thickness Measurement of Thin Film Based on Neural Network

2014 ◽  
Vol 484-485 ◽  
pp. 307-310
Author(s):  
Li Cai ◽  
Yue Gang Tan ◽  
Qin Wei
Keyword(s):  
Neural Network ◽  
Thin Film ◽  
Film Thickness ◽  
Production Cost ◽  
Rbf Neural Network ◽  
Thickness Measurement ◽  
Monitoring Method ◽  
Line Thickness ◽  
On Line ◽  
Thickness Deviation

This paper proposes a on-line thickness measurement scheme of thin film based on the capacitance thickness sensor and introduces the composition and principle of thickness measurement system. Then it further states the principle and simulation of the RBF neural network, which can effectively predict the thickness deviation of thin film by setting the appropriate parameters. The monitoring method based on the RBF neural network will reduce production cost and make the film thickness uniformity better, combining the traditional film production line with an new idea of controlling the opening degree of wind ring.

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