Parameters Design and Braking Efficiency Analysis of a Hydraulic Self-Energizing Wedge Disc Brake

Junnian Wang,Yao Zhang,Nannan Yang,Dafeng Song,Qingnian Wang 한국정밀공학회 2017 International Journal of Precision Engineering and Vol.18 No.10

Pursuing more efficient running and operation has been the orientation of today's automotive powertrain and chassis technologies development. As for the brake system, increasing the braking efficiency or reducing the power consumption of actuation is an effective method to improve the energy-saving performance of the car. In this paper, a self-energizing wedge disc brake actuated by hydraulic power, that could be used to reduce both the actuation force and the energy required for braking is presented and compared in terms of the practical issues on braking efficiency, heat recession and self-locking. The selected critical parameters and their optimum value range are determined based on the static force analysis. Then the simulations based on AMESim/Simulink are implemented to compare the proposed wedge brake with traditional disc brake, and to investigate the influence of the wedge angle and the actuating angle on the braking efficiency performance. The simulation results validate the self-reinforcement and energy saving performance of this hydraulic wedge disc brake well.

Power Load Disaggregation of Households with Solar Panels Based on an Improved Long Short-term Memory Network

JiaXuan Sun,JunNian Wang,Wenxin Yu,ZhenHeng Wang,YangHua Wang 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5

With the increasing application of small distributed renewable energy systems in household power supplies, when a large number of distributed renewable energy power generation systems are connected to the power grid, the time-varying output power of small solar energy, wind turbines, etc. Disaggregation and analysis of regional household electricity and renewable energy power supply systems connected to household electricity will help grid companies to conduct power dispatch management. This paper employed a two-way two-layer Long Short-term Memory deep learning network with improved input form to perform non-intrusive load disaggregation on household power with solar panels, which can monitor the load status of household electrical appliances and the output power of solar power generation system in real time. The power situation provides a decision basis for optimizing the response value of household energy demand and improving the demand of the power system from the response management level. The combined dataset from UK-DALE and kaggle’solar panel power generation data is adopted to train and test the proposed improved Long Short-term Memory network. The test results show that the proposed algorithm is applied to the household electric load disaggregation with solar panels, with high accuracy and reliability.

Fault Diagnosis of a Nonlinear Dynamic System Based on Sliding Mode

Wenxin Yu,Junnian Wang,Dan Jiang 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.6

Actuator failures and the failures of controlled objects are often considered together. To overcome this limitation, a class of sliding mode observers for the fault diagnosis of nonlinear systems is designed in this paper. Due to the influence of the sliding mode function, the control strategy and the residual change of the observer exhibit certain trends governed by specific relations. Therefore, according to the changes in the control strategy and the observer residuals, the sensor and actuator faults in nonlinear systems can be determined. Finally, the effectiveness of the proposed method is verified based on simulations of a DC motor system.

Fault Diagnosis of a Nonlinear Dynamic System Based on Sliding Mode

Yu, Wenxin,Wang, Junnian,Jiang, Dan The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.6

Actuator failures and the failures of controlled objects are often considered together. To overcome this limitation, a class of sliding mode observers for the fault diagnosis of nonlinear systems is designed in this paper. Due to the influence of the sliding mode function, the control strategy and the residual change of the observer exhibit certain trends governed by specific relations. Therefore, according to the changes in the control strategy and the observer residuals, the sensor and actuator faults in nonlinear systems can be determined. Finally, the effectiveness of the proposed method is verified based on simulations of a DC motor system.

Fault Detection Based on a Combined Approach of FA-CP-ELM with Application to Wind Turbine System

Wenxin Yu,Shoudao Huang,Junnian Wang 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.1

In this paper, a novel wind turbine (WT) fault detection method, based on the Partial Least Squares (PLS), Firefly Algorithm (FA), Chaos Map (CP) and Extreme Learning Machine (ELM), which is proposed and explained in detail. The proposed method includes two procedures: a WT mathematical model with PLS and a prediction model with FA-CP-ELM. Since the WT system is modeled as a system using PLS, the ELM has been optimized by the FA and CP to improve the predictive performance. Then, it’s calculated the residual between the mathematical model and the predicted model. If a fault occurs, the residual will increase accordingly and exceed the tolerance range. Hence, a fault can be detected quickly. To demonstrate the feasibility and effectiveness of the proposed approach, the wind turbine system is tested with a fault point set in this system. According to the results of the example, this proposed method is found to achieve better performance.

Design of PID Controller Based on ELM and Its Implementation for Buck Converters

Yang Lu,WeiXin Yu,JunNian Wang,Dan Jiang,RuiQi Li 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.7

Buck converter is a kind of converter device with high efficiency, wide adjustable output voltage, small loss, small size and light weight. Its circuit has nonlinearity and will exhibit abundant nonlinear phenomena with the change of circuit parameters, resulting in unstable output voltage and being susceptible to load and external disturbances. Therefore, a control method based on Extreme Learning Machine (ELM) combined with a proportionalintegral-derivative (PID) controller is proposed in this paper and used for output voltage control of buck converter. Firstly, the mathematical model of buck converter in Continuous Conduction Mode (CCM) is established by the state space averaging method. Then the PID tuning algorithm is designed in combination with ELM, and the stability analysis of the model is carried out. Finally, the simulation experiment is carried out under different disturbances. By comparing with the open-loop control strategy, the effectiveness of the proposed ELM-PID control strategy is verified, indicating that the proposed method can achieve the stability of output voltage and good dynamic response.

A STATE DETECTION METHOD OF INDUCTION MOTOR BASED ON PSO-BS-SMO

Guanglin Zhong,Wenxin Yu,JunNian Wang 한국자동차공학회 2024 International journal of automotive technology Vol.25 No.2

In order to improve the performance of sliding mode observer in detecting the state of induction motor, a state detectionmethod based on particle swarm optimization (PSO)-backstepping (BS)-sliding mode observer (SMO) is proposed in thispaper. In this method, the controller is constructed and the parameters of the control rate are optimized, so that the trackingaccuracy and robustness of the new observer are improved relative to conventional observer, exponential observer, PI andPID. Firstly, the state equation of the induction motor under stator and rotor winding fault and stator current sensor fault isestablished. Secondly, the new sliding mode observer is designed using the backstepping method based on the new reachinglaw. Then, the new fi tness function and PSO is used to optimize the parameters of the new sliding mode observer. Finally,the simulation comparison experiment of stator current state detection is carried out under the simulated fault condition ofinduction motor. The feasibility of the method is verifi ed by comparing the state tracking situation and the state detectionerror. The comparative experimental results show that the method has less jitter, stronger robustness, and higher state trackingaccuracy when detecting stator current states under diff erent faults.