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Volume 49,Issue 9,2022 Table of Contents
2022, 49(9):1-7.
DOI: 10.12177/emca.2022.087
Abstract:
The residual vibration of the single-link flexible manipulator (SLFM) occurs during the point-to-point motion driven by the permanent magnet linear motor (PMLM)which can affect the rapid and precise location. To suppress the residual vibrationthe model identification of translational flexible manipulator system driven by PMLM is studied. The transfer function from position command to strain vibration at the root of the flexible manipulator is establishedand then the model is identified by step and frequency sweep input methodwhich lay the foundation for the control method of flexible manipulator system driven by PMLM to suppress the residual vibration. The experimental results show that the fitting ratios between the simulation output value of the identification model and the actual dynamic response of the PMLM SLFM system are both high. The results verify the model identification and also reflect the dynamic characteristics of the flexible manipulator system based on PMLM.
The residual vibration of the single-link flexible manipulator (SLFM) occurs during the point-to-point motion driven by the permanent magnet linear motor (PMLM)which can affect the rapid and precise location. To suppress the residual vibrationthe model identification of translational flexible manipulator system driven by PMLM is studied. The transfer function from position command to strain vibration at the root of the flexible manipulator is establishedand then the model is identified by step and frequency sweep input methodwhich lay the foundation for the control method of flexible manipulator system driven by PMLM to suppress the residual vibration. The experimental results show that the fitting ratios between the simulation output value of the identification model and the actual dynamic response of the PMLM SLFM system are both high. The results verify the model identification and also reflect the dynamic characteristics of the flexible manipulator system based on PMLM.
2022, 49(9):8-16.
DOI: 10.12177/emca.2022.091
Abstract:
Model predictive control (MPC) has excellent dynamic performance and strong disturbance rejection capabilitybut zero voltage vector can cause large common mode voltageresulting in axis currentelectromagnetic interference and other problems. Although the existed multi-vector MPC based on the equivalent of zero vector can suppress common-mode voltageit needs to use multiple effective vectors in a periodresulting in high switching frequency and low system efficiency. A hybrid model predictive torque control (MPTC) strategy for permanent magnet synchronous motor (PMSM) is proposedwhich uses two waysthe virtual vector or multi-vector synthesis wayto generate the voltage vector for different amplitudes of the reference voltage. This method can achieve common mode voltage suppression with low average switching frequency and ensure good performance of dynamic and static torque control. In additionflexible compromise between switching frequency and torque ripple can be achieved by changing the switching condition of control mode. Finallythe effectiveness of the proposed method is verified by simulation.
Model predictive control (MPC) has excellent dynamic performance and strong disturbance rejection capabilitybut zero voltage vector can cause large common mode voltageresulting in axis currentelectromagnetic interference and other problems. Although the existed multi-vector MPC based on the equivalent of zero vector can suppress common-mode voltageit needs to use multiple effective vectors in a periodresulting in high switching frequency and low system efficiency. A hybrid model predictive torque control (MPTC) strategy for permanent magnet synchronous motor (PMSM) is proposedwhich uses two waysthe virtual vector or multi-vector synthesis wayto generate the voltage vector for different amplitudes of the reference voltage. This method can achieve common mode voltage suppression with low average switching frequency and ensure good performance of dynamic and static torque control. In additionflexible compromise between switching frequency and torque ripple can be achieved by changing the switching condition of control mode. Finallythe effectiveness of the proposed method is verified by simulation.
LI Yaohua
,
SU Jinshi
,
WU Tingxu
,
ZHANG Xinquan
,
CUI Kangjian
,
CHEN Xin
,
XU Zhixiong
,
LAN Fenlong
2022, 49(9):17-21.
DOI: 10.12177/emca.2022.089
Abstract:
Multi-step model prediction control (MPC) of permanent magnet synchronous motor (PMSM) has a large computation. In order to solve the problemthe event trigger mechanism is introduced into PMSM multi-step model predictive torque control (MPTC). The event trigger mechanism is based on torque errorstator flux error and times of continuous event triggering. If the event is triggeredthe MPTC uses the voltage vector of control sequence in the most recent multi-step prediction directly to calculate the sampling period and does not carry out the multi-step prediction. Simulation results show that the PMSM MPTC based on event trigger can work properly. Compared with conventional MPTCthe torque and stator flux ripples of PMSM MPTC increase mildly as rolling optimization is sacrificed when the event is triggeredbut average switching frequency and computational burden decrease.
Multi-step model prediction control (MPC) of permanent magnet synchronous motor (PMSM) has a large computation. In order to solve the problemthe event trigger mechanism is introduced into PMSM multi-step model predictive torque control (MPTC). The event trigger mechanism is based on torque errorstator flux error and times of continuous event triggering. If the event is triggeredthe MPTC uses the voltage vector of control sequence in the most recent multi-step prediction directly to calculate the sampling period and does not carry out the multi-step prediction. Simulation results show that the PMSM MPTC based on event trigger can work properly. Compared with conventional MPTCthe torque and stator flux ripples of PMSM MPTC increase mildly as rolling optimization is sacrificed when the event is triggeredbut average switching frequency and computational burden decrease.
2022, 49(9):22-28.
DOI: 10.12177/emca.2022.077
Abstract:
To improve the stability of permanent magnet synchronous motor (PMSM) with traditional integral sliding mode control (SMC) structure and improve the stability of the systema new integral weakening SMC structure is designed. Based on the traditional integral SMC structurethe boundary layer concept is introduced to weaken the integral outside the layer and soften the approaching motion of the motor. The advantages of this method are verified by Lyapunov function. Based on the integral weakening structurethe PMSM vector control models of the traditional and novel controllers are respectively built in MATLAB/Simulink for comparison and verification. The simulation analysis results show thatcompared with the traditional integral sliding mode controlthe designed SMC structure can effectively improve the steady-state error and stability of the system.
To improve the stability of permanent magnet synchronous motor (PMSM) with traditional integral sliding mode control (SMC) structure and improve the stability of the systema new integral weakening SMC structure is designed. Based on the traditional integral SMC structurethe boundary layer concept is introduced to weaken the integral outside the layer and soften the approaching motion of the motor. The advantages of this method are verified by Lyapunov function. Based on the integral weakening structurethe PMSM vector control models of the traditional and novel controllers are respectively built in MATLAB/Simulink for comparison and verification. The simulation analysis results show thatcompared with the traditional integral sliding mode controlthe designed SMC structure can effectively improve the steady-state error and stability of the system.
2022, 49(9):29-36.
DOI: 10.12177/emca.2022.095
Abstract:
In order to weaken the normal force ripple existing between the mover and the stator in the movement of permanent magnet linear motor (PMLM)on the basis of deducing and analyzing the generation principle of normal force ripplethe optimal pole-arc coefficient and magnetic pole chamfer method are used to weaken the cogging normal forceand the optimization of core length and the end tooth shape are used to weaken the end normal forceso as to comprehensively weaken the normal force ripple of the PMLM. The effectiveness of the method is proved by finite element simulationwhich can greatly weaken the normal force ripple of the motor when the thrust requirements are met. The obtained conclusions can provide some theoretical guidance for the design of permanent magnet linear motors.
In order to weaken the normal force ripple existing between the mover and the stator in the movement of permanent magnet linear motor (PMLM)on the basis of deducing and analyzing the generation principle of normal force ripplethe optimal pole-arc coefficient and magnetic pole chamfer method are used to weaken the cogging normal forceand the optimization of core length and the end tooth shape are used to weaken the end normal forceso as to comprehensively weaken the normal force ripple of the PMLM. The effectiveness of the method is proved by finite element simulationwhich can greatly weaken the normal force ripple of the motor when the thrust requirements are met. The obtained conclusions can provide some theoretical guidance for the design of permanent magnet linear motors.
2022, 49(9):37-43.
DOI: 10.12177/emca.2022.088
Abstract:
Based on the traditional flux switched linear motora transverse magnetic field flux switched electric excited magnetic suspension linear motor (TMFFSEEMSLM) for rail transit is proposed. The structure and the operation principle of TMFFSEEMSLM are studied. The stator of the motor is U-shaped magnetic polethe mover is double H-shaped and connected through a beam to generate transverse magnetic flux for guidance. The armature winding and excitation winding locate on the short primary of the motor to realize magnetic flux switchingand the secondary is only composed of magnetic core. Electrical excitation realizes air gap magnetic field regulationgreatly reduce the manufacturing cost of long-distance rail transit. Magnetic suspension enables frictionless motion at the primary and secondary levels of the motors. The mathematical model of magnetic suspension linear motor is establishedand the voltageflux linkage equations and the analytical expressions of suspension forceelectromagnetic thrust and guidance force are derived. The three-dimensional finite element model of the motor is establishedthe magnetic field distribution and electromagnetic parameters of the motor are analyzedand the suspension forceelectromagnetic thrust and guidance force of the motor are simulated. The research results verify the practicability of the motor structure. The integration of tractionsuspension and guidance is realized.
Based on the traditional flux switched linear motora transverse magnetic field flux switched electric excited magnetic suspension linear motor (TMFFSEEMSLM) for rail transit is proposed. The structure and the operation principle of TMFFSEEMSLM are studied. The stator of the motor is U-shaped magnetic polethe mover is double H-shaped and connected through a beam to generate transverse magnetic flux for guidance. The armature winding and excitation winding locate on the short primary of the motor to realize magnetic flux switchingand the secondary is only composed of magnetic core. Electrical excitation realizes air gap magnetic field regulationgreatly reduce the manufacturing cost of long-distance rail transit. Magnetic suspension enables frictionless motion at the primary and secondary levels of the motors. The mathematical model of magnetic suspension linear motor is establishedand the voltageflux linkage equations and the analytical expressions of suspension forceelectromagnetic thrust and guidance force are derived. The three-dimensional finite element model of the motor is establishedthe magnetic field distribution and electromagnetic parameters of the motor are analyzedand the suspension forceelectromagnetic thrust and guidance force of the motor are simulated. The research results verify the practicability of the motor structure. The integration of tractionsuspension and guidance is realized.
2022, 49(9):44-49.
DOI: 10.12177/emca.2022.047
Abstract:
Taking the high precision linear motor as the research objecta method for thrust ripple compensation of linear motor is proposed. The general law of detent force of linear motor is analyzed and summarized by analytical methodwhich provides a theoretical basis for rationally setting compensator parameters and suppressing linear motor thrust ripple. Finallythe finite element analysis and experiment of 6-slot 8-pole motor is carried out. The results show that the compensation effect of the compensator on the detent force is over 60%.
Taking the high precision linear motor as the research objecta method for thrust ripple compensation of linear motor is proposed. The general law of detent force of linear motor is analyzed and summarized by analytical methodwhich provides a theoretical basis for rationally setting compensator parameters and suppressing linear motor thrust ripple. Finallythe finite element analysis and experiment of 6-slot 8-pole motor is carried out. The results show that the compensation effect of the compensator on the detent force is over 60%.
2022, 49(9):50-56.
DOI: 10.12177/emca.2022.092
Abstract:
The main circuit of the suspension controller for low and medium speed maglev trains is studied and a mathematical model of the charging circuit and chopper circuit is developed. A simulation model of the suspension controller main circuit is established using the dSPACE real-time simulation software and hardware platformand the charging circuit and chopper circuit as well as the current tracking situation are verified and compared. The simulation results show that the simulation model of the suspension controller main circuit established is more accurate. A theoretical basis is provided for the subsequent replacement of the real suspension controller main circuit by the simulation modeland the foundation is also provided for the development and debugging of the suspension control strategy.
The main circuit of the suspension controller for low and medium speed maglev trains is studied and a mathematical model of the charging circuit and chopper circuit is developed. A simulation model of the suspension controller main circuit is established using the dSPACE real-time simulation software and hardware platformand the charging circuit and chopper circuit as well as the current tracking situation are verified and compared. The simulation results show that the simulation model of the suspension controller main circuit established is more accurate. A theoretical basis is provided for the subsequent replacement of the real suspension controller main circuit by the simulation modeland the foundation is also provided for the development and debugging of the suspension control strategy.
2022, 49(9):57-65.
DOI: 10.12177/emca.2022.099
Abstract:
Transformer voiceprint signal contains a lot of effective information reflecting the internal mechanical state. In order to realize uninterrupted detection of internal mechanical state of transformera voiceprint recognition method of transformer mechanical state based on feature screening and improved deep forest is proposed. Firstlythe intrinsic mode function(IFM) is obtained by decomposing the voiceprint signal with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)and the IMF component containing the fault information is obtained by filtering the IMF components through spectrum analysis and Pearson correlation coefficient. Secondlythe distribution of each IMF component in the frequency band is used to divide the high and low frequency bands. According to the difference of the IMF components in the high and low frequency bandsthe time-frequency energy of the IMF component in the high frequency band and the amplitude characteristic of the IMF component in the low frequency band are used as characteristic indicators to form a feature vectorwhich is input into the improved deep forest modeland the voiceprint recognition results of 10 mechanical loose states are obtained. Finallythe effectiveness of the method is verified by field experiments. The research results show that the average recognition accuracy of the proposed method is 99.2% for 10 mechanical loose states. Compared with the traditional transformer voiceprint featurethe proposed voiceprint feature has higher discrimination; Compared with the traditional recognition modelthe proposed improved deep forest recognition model has lower complexityfaster training speed and higher recognition accuracy.
Transformer voiceprint signal contains a lot of effective information reflecting the internal mechanical state. In order to realize uninterrupted detection of internal mechanical state of transformera voiceprint recognition method of transformer mechanical state based on feature screening and improved deep forest is proposed. Firstlythe intrinsic mode function(IFM) is obtained by decomposing the voiceprint signal with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)and the IMF component containing the fault information is obtained by filtering the IMF components through spectrum analysis and Pearson correlation coefficient. Secondlythe distribution of each IMF component in the frequency band is used to divide the high and low frequency bands. According to the difference of the IMF components in the high and low frequency bandsthe time-frequency energy of the IMF component in the high frequency band and the amplitude characteristic of the IMF component in the low frequency band are used as characteristic indicators to form a feature vectorwhich is input into the improved deep forest modeland the voiceprint recognition results of 10 mechanical loose states are obtained. Finallythe effectiveness of the method is verified by field experiments. The research results show that the average recognition accuracy of the proposed method is 99.2% for 10 mechanical loose states. Compared with the traditional transformer voiceprint featurethe proposed voiceprint feature has higher discrimination; Compared with the traditional recognition modelthe proposed improved deep forest recognition model has lower complexityfaster training speed and higher recognition accuracy.
2022, 49(9):66-74.
DOI: 10.12177/emca.2022.085
Abstract:
In the incipient stage inter-turn short circuit fault detectionthe fault signal cannot be detected because the amplitude of fault characteristic frequency is very small and it is easy to be interfered by noise signal. To solve the above problemsa method based on variational mode decomposition (VMD) and double logarithmic spectrum analysis is proposed for the incipient stage inter-turn short circuit fault detection of permanent magnet synchronous motor (PMSM). The VMD is applied to remove the noise and related harmonic components in zero sequence voltage component (ZSVC) for highlighting the fault characteristic component. Thenthe double logarithmic spectrum analysis method is used to analyze the obtained signal for inter-turn faultand the fault of inter-turn short circuit can be judged by detecting the fault characteristic frequency in the spectrum diagram. The effectiveness of the proposed fault detection method is validated by the simulation and physical test.
In the incipient stage inter-turn short circuit fault detectionthe fault signal cannot be detected because the amplitude of fault characteristic frequency is very small and it is easy to be interfered by noise signal. To solve the above problemsa method based on variational mode decomposition (VMD) and double logarithmic spectrum analysis is proposed for the incipient stage inter-turn short circuit fault detection of permanent magnet synchronous motor (PMSM). The VMD is applied to remove the noise and related harmonic components in zero sequence voltage component (ZSVC) for highlighting the fault characteristic component. Thenthe double logarithmic spectrum analysis method is used to analyze the obtained signal for inter-turn faultand the fault of inter-turn short circuit can be judged by detecting the fault characteristic frequency in the spectrum diagram. The effectiveness of the proposed fault detection method is validated by the simulation and physical test.
11 Fault Diagnosis of Iron Core Looseness Based on Mel Spectrogram-ResNet with Transformer Voiceprint
2022, 49(9):75-80.
DOI: 10.12177/emca.2022.084
Abstract:
In order to realize the identification of transformer core looseness faultResNet convolution neural network (CNN) based on transformer voiceprint is proposed as the identification of core looseness fault. The different performance effects of the same ResNet-CNN in the cross entropy loss function (SE-ResNet-Dense) and the additive angular margin loss function (SE-ResNet-ArcLoss) which maximizes the classification boundary in the angle space of feature expression are comparedrespectively. The noise signals of the transformer core at the rated preload20% looseness and 40% looseness are collected through the transformer no-load experiment. The collected voiceprint signal is generated into the time-frequency matrix through the discrete Fourier transformand the Mel filter is used to reduce the dimension of the time-frequency matrix to generate the Mel spectrogram with greatly reduced size. The collected noise signal is made into a data set and input into the two models for training. The prediction result of the final test set on the model SE-ResNet-Dense is 90.753%and that on the model SE-ResNet-ArcLoss is 97.541%. The results show that SE-ResNet-ArcLoss are most suitable for transformer core looseness identification.
In order to realize the identification of transformer core looseness faultResNet convolution neural network (CNN) based on transformer voiceprint is proposed as the identification of core looseness fault. The different performance effects of the same ResNet-CNN in the cross entropy loss function (SE-ResNet-Dense) and the additive angular margin loss function (SE-ResNet-ArcLoss) which maximizes the classification boundary in the angle space of feature expression are comparedrespectively. The noise signals of the transformer core at the rated preload20% looseness and 40% looseness are collected through the transformer no-load experiment. The collected voiceprint signal is generated into the time-frequency matrix through the discrete Fourier transformand the Mel filter is used to reduce the dimension of the time-frequency matrix to generate the Mel spectrogram with greatly reduced size. The collected noise signal is made into a data set and input into the two models for training. The prediction result of the final test set on the model SE-ResNet-Dense is 90.753%and that on the model SE-ResNet-ArcLoss is 97.541%. The results show that SE-ResNet-ArcLoss are most suitable for transformer core looseness identification.
2022, 49(9):81-87.
DOI: 10.12177/emca.2022.096
Abstract:
The connection of distributed photovoltaic power to distribution network may cause the decrease of the reliability and sensitivity of traditional distribution network relay protection. To solve this problema distributed fault location method based on skewness coefficient is proposed to measure the polarity of transient zero-sequence current considering the multi-branch bus. The distribution characteristics of zero-sequence current when single-phase grounding fault occurs in the distribution network connected with distributed generation are analyzed. Thusthe characteristics of the same polarity of transient zero-sequence current at both ends of the fault section at fault time and the opposite polarity of transient zero-sequence current at both ends of the non-fault section are further obtained. The skewness coefficient is introduced to measure the polarity of transient zero-sequence current and realize the distributed fault location. The simulation results show that the method can be applied to the fault location of distribution network with distributed photovoltaic power supplyand has strong applicability under different fault conditions.
The connection of distributed photovoltaic power to distribution network may cause the decrease of the reliability and sensitivity of traditional distribution network relay protection. To solve this problema distributed fault location method based on skewness coefficient is proposed to measure the polarity of transient zero-sequence current considering the multi-branch bus. The distribution characteristics of zero-sequence current when single-phase grounding fault occurs in the distribution network connected with distributed generation are analyzed. Thusthe characteristics of the same polarity of transient zero-sequence current at both ends of the fault section at fault time and the opposite polarity of transient zero-sequence current at both ends of the non-fault section are further obtained. The skewness coefficient is introduced to measure the polarity of transient zero-sequence current and realize the distributed fault location. The simulation results show that the method can be applied to the fault location of distribution network with distributed photovoltaic power supplyand has strong applicability under different fault conditions.
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Electric Machines & Control Application ® 2025
Supported by:Beijing E-Tiller Technology Development Co., Ltd.
沪ICP备16038578号-3
Electric Machines & Control Application ® 2025
Supported by:Beijing E-Tiller Technology Development Co., Ltd.
