A chaos control strategy for chaotic current-mode boost converter is presented by using inductor current sampled feedback control technique.The quantitative analysis of control mechanism is performed by establishing a...A chaos control strategy for chaotic current-mode boost converter is presented by using inductor current sampled feedback control technique.The quantitative analysis of control mechanism is performed by establishing a discrete alterative map of the controlled system.The stability criterion,feedback gain,and corresponding critical duty ratio are obtained from the eigenvalue of the map.The simulation results verify the t heoretical analysis results of the control strategy.展开更多
A current identification method based on optimized variational mode decomposition(VMD)and sample entropy(SampEn)is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder ca...A current identification method based on optimized variational mode decomposition(VMD)and sample entropy(SampEn)is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder cannot distinguish between train charging current and remote short circuit current.This method uses the principle of energy difference to optimize the optimal mode decomposition number k of VMD;the optimal VMD for DC feeder current is decomposed into the intrinsic modal function(IMF)of different frequency bands.The sample entropy algorithm is used to perform feature extraction of each IMF,and then the eigenvalues of the intrinsic modal function of each frequency band of the current signal can be obtained.The recognition feature vector is input into the support vector machine model based on Bayesian hyperparameter optimization for training.After a large number of experimental data are verified,it is found that the optimal VMD_SampEn algorithm to identify the train charging current and remote short circuit current is more accurate than other algorithms.Thus,the algorithm based on optimized VMD_SampEn has certain engineering application value in the fault current identification of the DC traction feeder.展开更多
The design of iterative learning controller(ILC) requires to store the system input, output or control parameters of previous trials for generating the input of the current trial. In order to apply the iterative learn...The design of iterative learning controller(ILC) requires to store the system input, output or control parameters of previous trials for generating the input of the current trial. In order to apply the iterative learning controller for a real application and reduce the memory size for implementation, a current error based sampled-data proportional-derivative(PD) type iterative learning controller is proposed for control systems with initial resetting error, input disturbance and output measurement noise in this paper.The proposed iterative learning controller is simple and effective. The first contribution in this paper is to prove the learning error convergence via a rigorous technical analysis. It is shown that the learning error will converge to a residual set if a forgetting factor is introduced in the controller. All the theoretical results are also shown by computer simulations. The second main contribution is to realize the iterative learning controller by a digital circuit using a field programmable gate array(FPGA) chip applied to repetitive position tracking control of direct current(DC) motors. The feasibility and effectiveness of the proposed current error based sampleddata iterative learning controller are demonstrated by the experiment results. Finally, the relationship between learning performance and design parameters are also discussed extensively.展开更多
This paper introduces the Particle SwarmOptimization(PSO)algorithmto enhance the LatinHypercube Sampling(LHS)process.The key objective is to mitigate the issues of lengthy computation times and low computational accur...This paper introduces the Particle SwarmOptimization(PSO)algorithmto enhance the LatinHypercube Sampling(LHS)process.The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation(MCS)to LHS for probabilistic trend calculations.The PSOmethod optimizes sample distribution,enhances global search capabilities,and significantly boosts computational efficiency.To validate its effectiveness,the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power.The performance was then compared with Latin Hypercubic Important Sampling(LHIS),which integrates significant sampling with theMonte Carlomethod.The comparison results indicate that the PSO-enhanced method significantly improves the uniformity and representativeness of the sampling.This enhancement leads to a reduction in data errors and an improvement in both computational accuracy and convergence speed.展开更多
Aiming at piezoresistive pressure sensors, this paper studies simulation of standard pressure by using benchmark current source and self-calibration of the sampling data characteristics. A data fusion algorithm for sa...Aiming at piezoresistive pressure sensors, this paper studies simulation of standard pressure by using benchmark current source and self-calibration of the sampling data characteristics. A data fusion algorithm for sample set is presented which transforms a surface problem into a curve fitting and interpolation problem. The simulation result shows that benchmark current source simulating pressure is successful and data fusion algorithm is effective. The maximum measurement error is only 0.098 kPa and maximum relative error is 0.92% at 0-45 kPa and -10-45~C.展开更多
The torque output in a permanent magnet brushless DC motor (BLDCM) is usually controlled by regulating the motor phase currents. In this paper, three kinds of PWM strategies together with some critical review on trad...The torque output in a permanent magnet brushless DC motor (BLDCM) is usually controlled by regulating the motor phase currents. In this paper, three kinds of PWM strategies together with some critical review on traditional current measurements in a BLDCM drive system are discussed. A novel method for assessing the PWM information and measuring the motor phase currents by a dc link current sensor is proposed. An attractive feature of the proposed method is the simplicity with the current sample processing because there is no need to incorporate the conduction information of the power switches or diodes. Only the single sided PWM or the double sided complementary PWM is needed with the proposed technique.展开更多
静止变频器(static frequency converter,SFC)是抽水蓄能电站大型同步机组启动过程的重要设备。在抽蓄机组启动的过程中,由于SFC机、网两侧电流频率不一致,使得基于工频相量的常规差动保护配置困难,且现有保护方案在速动性和可靠性上存...静止变频器(static frequency converter,SFC)是抽水蓄能电站大型同步机组启动过程的重要设备。在抽蓄机组启动的过程中,由于SFC机、网两侧电流频率不一致,使得基于工频相量的常规差动保护配置困难,且现有保护方案在速动性和可靠性上存在不足。为解决上述问题,提出了一种基于模型校核的SFC采样值差动保护原理。首先充分研究SFC的工作原理和控制策略,并基于电流采样值在时域内构建符合SFC工作原理的数学模型,然后采用模型校核的方法寻找故障前后差异并提出相应的保护判据。最后,在PSCAD/EMTDC中搭建抽蓄机组SFC启动的电磁暂态仿真模型,验证所提保护原理的有效性。仿真结果表明,所提保护原理不受SFC两侧不同频率的干扰,具有良好的选择性、速动性和可靠性。展开更多
文摘A chaos control strategy for chaotic current-mode boost converter is presented by using inductor current sampled feedback control technique.The quantitative analysis of control mechanism is performed by establishing a discrete alterative map of the controlled system.The stability criterion,feedback gain,and corresponding critical duty ratio are obtained from the eigenvalue of the map.The simulation results verify the t heoretical analysis results of the control strategy.
基金This project supported by The National Natural Science Foundation of China(No.11872253).
文摘A current identification method based on optimized variational mode decomposition(VMD)and sample entropy(SampEn)is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder cannot distinguish between train charging current and remote short circuit current.This method uses the principle of energy difference to optimize the optimal mode decomposition number k of VMD;the optimal VMD for DC feeder current is decomposed into the intrinsic modal function(IMF)of different frequency bands.The sample entropy algorithm is used to perform feature extraction of each IMF,and then the eigenvalues of the intrinsic modal function of each frequency band of the current signal can be obtained.The recognition feature vector is input into the support vector machine model based on Bayesian hyperparameter optimization for training.After a large number of experimental data are verified,it is found that the optimal VMD_SampEn algorithm to identify the train charging current and remote short circuit current is more accurate than other algorithms.Thus,the algorithm based on optimized VMD_SampEn has certain engineering application value in the fault current identification of the DC traction feeder.
基金supported by National Science Council,Taiwan,China(No.NSC102-2221-E-211-011)National Nature Science Foundation of China(No.61374102)
文摘The design of iterative learning controller(ILC) requires to store the system input, output or control parameters of previous trials for generating the input of the current trial. In order to apply the iterative learning controller for a real application and reduce the memory size for implementation, a current error based sampled-data proportional-derivative(PD) type iterative learning controller is proposed for control systems with initial resetting error, input disturbance and output measurement noise in this paper.The proposed iterative learning controller is simple and effective. The first contribution in this paper is to prove the learning error convergence via a rigorous technical analysis. It is shown that the learning error will converge to a residual set if a forgetting factor is introduced in the controller. All the theoretical results are also shown by computer simulations. The second main contribution is to realize the iterative learning controller by a digital circuit using a field programmable gate array(FPGA) chip applied to repetitive position tracking control of direct current(DC) motors. The feasibility and effectiveness of the proposed current error based sampleddata iterative learning controller are demonstrated by the experiment results. Finally, the relationship between learning performance and design parameters are also discussed extensively.
文摘This paper introduces the Particle SwarmOptimization(PSO)algorithmto enhance the LatinHypercube Sampling(LHS)process.The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation(MCS)to LHS for probabilistic trend calculations.The PSOmethod optimizes sample distribution,enhances global search capabilities,and significantly boosts computational efficiency.To validate its effectiveness,the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power.The performance was then compared with Latin Hypercubic Important Sampling(LHIS),which integrates significant sampling with theMonte Carlomethod.The comparison results indicate that the PSO-enhanced method significantly improves the uniformity and representativeness of the sampling.This enhancement leads to a reduction in data errors and an improvement in both computational accuracy and convergence speed.
基金Project supported by the National Natural Science Foundation of China （Grant No.40265001）, and the Science Foundation of Yunnan Province （Grant No.2002C0038M）
文摘Aiming at piezoresistive pressure sensors, this paper studies simulation of standard pressure by using benchmark current source and self-calibration of the sampling data characteristics. A data fusion algorithm for sample set is presented which transforms a surface problem into a curve fitting and interpolation problem. The simulation result shows that benchmark current source simulating pressure is successful and data fusion algorithm is effective. The maximum measurement error is only 0.098 kPa and maximum relative error is 0.92% at 0-45 kPa and -10-45~C.
文摘The torque output in a permanent magnet brushless DC motor (BLDCM) is usually controlled by regulating the motor phase currents. In this paper, three kinds of PWM strategies together with some critical review on traditional current measurements in a BLDCM drive system are discussed. A novel method for assessing the PWM information and measuring the motor phase currents by a dc link current sensor is proposed. An attractive feature of the proposed method is the simplicity with the current sample processing because there is no need to incorporate the conduction information of the power switches or diodes. Only the single sided PWM or the double sided complementary PWM is needed with the proposed technique.
文摘静止变频器(static frequency converter,SFC)是抽水蓄能电站大型同步机组启动过程的重要设备。在抽蓄机组启动的过程中,由于SFC机、网两侧电流频率不一致,使得基于工频相量的常规差动保护配置困难,且现有保护方案在速动性和可靠性上存在不足。为解决上述问题,提出了一种基于模型校核的SFC采样值差动保护原理。首先充分研究SFC的工作原理和控制策略,并基于电流采样值在时域内构建符合SFC工作原理的数学模型,然后采用模型校核的方法寻找故障前后差异并提出相应的保护判据。最后,在PSCAD/EMTDC中搭建抽蓄机组SFC启动的电磁暂态仿真模型,验证所提保护原理的有效性。仿真结果表明,所提保护原理不受SFC两侧不同频率的干扰,具有良好的选择性、速动性和可靠性。
