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Volume 49,Issue 12,2022 Table of Contents
2022, 49(12):1-6.
DOI: 10.12177/emca.2022.128
Abstract:
A new sliding mode control method based on feedback linearization decoupling is introduced to solve the problem that the traditional control strategy cannot completely decouple permanent magnet synchronous motor (PMSM). Firstly, the PMSM control system is divided into two independent linear subsystems, namely the speed subsystem and the current subsystem, by using differential operation and feedback linearization principle. Secondly, considering the poor robustness of the system, a new sliding mode controller with terminal attractor model is proposed. Finally, the simulation results show that the control strategy combined with the above two methods can improve the control precision, rapidity and robustness of the system.
A new sliding mode control method based on feedback linearization decoupling is introduced to solve the problem that the traditional control strategy cannot completely decouple permanent magnet synchronous motor (PMSM). Firstly, the PMSM control system is divided into two independent linear subsystems, namely the speed subsystem and the current subsystem, by using differential operation and feedback linearization principle. Secondly, considering the poor robustness of the system, a new sliding mode controller with terminal attractor model is proposed. Finally, the simulation results show that the control strategy combined with the above two methods can improve the control precision, rapidity and robustness of the system.
2022, 49(12):7-12.
DOI: 10.12177/emca.2022.127
Abstract:
Aiming at the problem of weighting factor design and adjustment of model predictive torque control (MPTC) of permanent magnet synchronous motor (PMSM), the pre-unit cost function is used to transfer the control objects into dimensionless variables, so as to eliminate the weighting factor, and its effectiveness is verified by simulation. Studies show that the pre-unit cost function unifies the range of control objects, and it can only be used in the area where the importance of all control objects is almost the same, and its computational load is large, and its real-time performance of the algorithm is reduced.
Aiming at the problem of weighting factor design and adjustment of model predictive torque control (MPTC) of permanent magnet synchronous motor (PMSM), the pre-unit cost function is used to transfer the control objects into dimensionless variables, so as to eliminate the weighting factor, and its effectiveness is verified by simulation. Studies show that the pre-unit cost function unifies the range of control objects, and it can only be used in the area where the importance of all control objects is almost the same, and its computational load is large, and its real-time performance of the algorithm is reduced.
2022, 49(12):13-20.
DOI: 10.12177/emca.2022.133
Abstract:
The traditional model predictive current control (MPCC) of permanent magnet synchronous motor (PMSM) has a contradiction between the steady-state control performance and the switching frequency. Therefore, an improved method is proposed based on the traditional MPCC. By extending the current prediction trajectory of one voltage vector to two control cycles, and using the current prediction values at different times to construct a new cost function, the optimal voltage vectors in two cycles are evaluated, so that the optimal voltage vectors in adjacent cycles are as same as possible, thus reducing the switching frequency of the inverter. Simulation results show that compared with traditional MPCC, the proposed method can effectively reduce the switching frequency at the same control frequency. In addition, when the switching frequency is approximately equal, the proposed method can effectively improve the current control performance.
The traditional model predictive current control (MPCC) of permanent magnet synchronous motor (PMSM) has a contradiction between the steady-state control performance and the switching frequency. Therefore, an improved method is proposed based on the traditional MPCC. By extending the current prediction trajectory of one voltage vector to two control cycles, and using the current prediction values at different times to construct a new cost function, the optimal voltage vectors in two cycles are evaluated, so that the optimal voltage vectors in adjacent cycles are as same as possible, thus reducing the switching frequency of the inverter. Simulation results show that compared with traditional MPCC, the proposed method can effectively reduce the switching frequency at the same control frequency. In addition, when the switching frequency is approximately equal, the proposed method can effectively improve the current control performance.
2022, 49(12):21-27.
DOI: 10.12177/emca.2022.129
Abstract:
Aiming at the problems of long-distance belt conveyor transportation system in the state of “big horse-drawn trolley” for a long time in constant speed operation mode, complex control structure of multi-drive motors, and poor speed synchronization performance, the motor cluster unitized control and variable speed energy-saving control strategy based on active disturbance rejection control are proposed. The performance comparison test of parallel and deviation coupling control is carried out under the motor cluster unitized control structure. The power consumption model of the long-distance belt conveyor is established. According to the proposed variable speed energy-saving control strategy, the simulation model is built based on PI and active disturbance rejection control, and the performance is compared and analyzed. The results show that in the variable speed energy-saving control strategy, active disturbance rejection control has higher precision and better anti-interference than PI control.
Aiming at the problems of long-distance belt conveyor transportation system in the state of “big horse-drawn trolley” for a long time in constant speed operation mode, complex control structure of multi-drive motors, and poor speed synchronization performance, the motor cluster unitized control and variable speed energy-saving control strategy based on active disturbance rejection control are proposed. The performance comparison test of parallel and deviation coupling control is carried out under the motor cluster unitized control structure. The power consumption model of the long-distance belt conveyor is established. According to the proposed variable speed energy-saving control strategy, the simulation model is built based on PI and active disturbance rejection control, and the performance is compared and analyzed. The results show that in the variable speed energy-saving control strategy, active disturbance rejection control has higher precision and better anti-interference than PI control.
2022, 49(12):28-33.
DOI: 10.12177/emca.2022.131
Abstract:
Aiming at the problem that traditional winter heating system in northern China is a nonlinear system with heating feedback lag, a new control system is introduced. Combined with the current situation of household heating, a heating valve DC motor control system based on improved PID is designed. The PID parameters of the motor control system are adjusted by genetic algorithm, and the optimal control curves at different angles are obtained and applied to the actual system. Meanwhile, long range radio (LoRa) is used to establish the information transmission between the user socket and the heating valve, and the temperature, angle and other information are sent to the heating company through the 485 bus to realize monitoring and control. Compared with the traditional control method, the proposed control method can improve the working performance and efficiency of the system.
Aiming at the problem that traditional winter heating system in northern China is a nonlinear system with heating feedback lag, a new control system is introduced. Combined with the current situation of household heating, a heating valve DC motor control system based on improved PID is designed. The PID parameters of the motor control system are adjusted by genetic algorithm, and the optimal control curves at different angles are obtained and applied to the actual system. Meanwhile, long range radio (LoRa) is used to establish the information transmission between the user socket and the heating valve, and the temperature, angle and other information are sent to the heating company through the 485 bus to realize monitoring and control. Compared with the traditional control method, the proposed control method can improve the working performance and efficiency of the system.
2022, 49(12):34-40,59.
DOI: 10.12177/emca.2022.108
Abstract:
The large-scale connection of electric vehicles and photovoltaic power generation system to the distribution network increases the single-phase power supply and load, which aggravates the single-phase operation problem. In order to ensure the power supply quality of the power grid, a large number of adjustable single-phase and three-phase compensation regulation devices are used in the power grid, so the optimal power flow of three-phase unbalanced distribution network is studied. The optimal power flow of distribution network under the condition of three-phase unbalance is solved and analyzed, and the optimal power flow model of multi-period distribution network is established with the optimization objective of minimizing the active power loss. The non-convex nonlinear optimal power flow model is transformed into a linear model by second-order cone programming, which reduces the difficulty of solving and improves the efficiency of solving without loss of accuracy. Pyomo modeling tool and Gurobi algorithm package are used to solve the model, and the power flow error is analyzed, which verifies the feasibility and effectiveness of the method.
The large-scale connection of electric vehicles and photovoltaic power generation system to the distribution network increases the single-phase power supply and load, which aggravates the single-phase operation problem. In order to ensure the power supply quality of the power grid, a large number of adjustable single-phase and three-phase compensation regulation devices are used in the power grid, so the optimal power flow of three-phase unbalanced distribution network is studied. The optimal power flow of distribution network under the condition of three-phase unbalance is solved and analyzed, and the optimal power flow model of multi-period distribution network is established with the optimization objective of minimizing the active power loss. The non-convex nonlinear optimal power flow model is transformed into a linear model by second-order cone programming, which reduces the difficulty of solving and improves the efficiency of solving without loss of accuracy. Pyomo modeling tool and Gurobi algorithm package are used to solve the model, and the power flow error is analyzed, which verifies the feasibility and effectiveness of the method.
2022, 49(12):41-46,80.
DOI: 10.12177/emca.2022.137
Abstract:
The tasks of high voltage and large capacity power transmission undertaken by high voltage cross-linked cable line are complex. Aiming at the problems that the detection accuracy of line oscillating wave partial discharge is poor and it is difficult to locate defects, a high voltage cross-linked cable line oscillating wave partial discharge detection method based on long short-term memory (LSTM) network algorithm is proposed. The oscillating wave voltage method is applied to establish the framework of oscillating wave partial discharge detection of high voltage cross-linked cable line. Based on the wavelet packet decomposition algorithm, the characteristics of typical partial discharge signals are extracted. The oscillating wave partial discharge signals are identified and detected by LSTM network algorithm to eliminate the noise in the partial discharge signals. According to the time difference between the original oscillating wave and the reflected oscillating wave reaching the test end, combined with the propagation speed of oscillating wave, the defect location of high voltage cross-linked cable line is determined, and the partial discharge detection of cable line oscillating wave is realized. The test results show that the partial discharge signal recognition accuracy obtained by the proposed method is higher, and the cable line defect location is more accurate, and the practical application performance is better.
The tasks of high voltage and large capacity power transmission undertaken by high voltage cross-linked cable line are complex. Aiming at the problems that the detection accuracy of line oscillating wave partial discharge is poor and it is difficult to locate defects, a high voltage cross-linked cable line oscillating wave partial discharge detection method based on long short-term memory (LSTM) network algorithm is proposed. The oscillating wave voltage method is applied to establish the framework of oscillating wave partial discharge detection of high voltage cross-linked cable line. Based on the wavelet packet decomposition algorithm, the characteristics of typical partial discharge signals are extracted. The oscillating wave partial discharge signals are identified and detected by LSTM network algorithm to eliminate the noise in the partial discharge signals. According to the time difference between the original oscillating wave and the reflected oscillating wave reaching the test end, combined with the propagation speed of oscillating wave, the defect location of high voltage cross-linked cable line is determined, and the partial discharge detection of cable line oscillating wave is realized. The test results show that the partial discharge signal recognition accuracy obtained by the proposed method is higher, and the cable line defect location is more accurate, and the practical application performance is better.
2022, 49(12):47-52,80.
DOI: 10.12177/emca.2022.139
Abstract:
In order to ensure the stable operation of the distributed photovoltaic station area and accurately and effectively divide the line loss data of the station area, a distributed photovoltaic line loss anomaly sensing algorithm based on K-Medoids clustering is proposed to accurately judge the degree of line loss anomaly in the distributed station area. The local anomaly factor (LOF) algorithm is used to judge the degree of local anomaly in the distributed photovoltaic station area data, and the anomalous line loss data generated by the influence of isolated points is filtered and removed. The distributed photovoltaic station area data after the filtering is clustered and analyzed by the K-Medoids clustering method. The anomalous line loss rate interval is combined with the clustering center and Euclidean distance of the anomalous line loss data, and the line loss anomaly sensing of the station area is completed, and the granularity calculation is innovatively introduced to optimize the K-Medoids clustering algorithm clustering center and to improve the sensing of anomalous data. The test results show that the proposed algorithm can effectively avoid the influence of isolated points on the anomalous sensing effect, accurately and effectively perceive the line loss anomalies in the distributed photovoltaic station area, and clearly divide the line loss data categories in the station area.
In order to ensure the stable operation of the distributed photovoltaic station area and accurately and effectively divide the line loss data of the station area, a distributed photovoltaic line loss anomaly sensing algorithm based on K-Medoids clustering is proposed to accurately judge the degree of line loss anomaly in the distributed station area. The local anomaly factor (LOF) algorithm is used to judge the degree of local anomaly in the distributed photovoltaic station area data, and the anomalous line loss data generated by the influence of isolated points is filtered and removed. The distributed photovoltaic station area data after the filtering is clustered and analyzed by the K-Medoids clustering method. The anomalous line loss rate interval is combined with the clustering center and Euclidean distance of the anomalous line loss data, and the line loss anomaly sensing of the station area is completed, and the granularity calculation is innovatively introduced to optimize the K-Medoids clustering algorithm clustering center and to improve the sensing of anomalous data. The test results show that the proposed algorithm can effectively avoid the influence of isolated points on the anomalous sensing effect, accurately and effectively perceive the line loss anomalies in the distributed photovoltaic station area, and clearly divide the line loss data categories in the station area.
2022, 49(12):53-59.
DOI: 10.12177/emca.2022.138
Abstract:
There are both permanent magnet torque and reluctance torque in permanent magnet-assisted synchronous reluctance motor (PMaSRM). The proportion of different torque components and torque ripple are important indexs to evaluate the rationality of electromagnetic structure design of motor. Taking a 3 kW PMaSRM as the research object, considering the influence of magnetic circuit saturation and d/q-axis cross coupling factors, the torque separation and torque ripple separation of the motor is carried out by the frozen permeability method. Based on the separation result of torque components and torque ripple components, with the goal of reducing the cogging torque under load, the optimization design of motor rotor structure is carried out, which provides reference value for the design of PMaSRM.
There are both permanent magnet torque and reluctance torque in permanent magnet-assisted synchronous reluctance motor (PMaSRM). The proportion of different torque components and torque ripple are important indexs to evaluate the rationality of electromagnetic structure design of motor. Taking a 3 kW PMaSRM as the research object, considering the influence of magnetic circuit saturation and d/q-axis cross coupling factors, the torque separation and torque ripple separation of the motor is carried out by the frozen permeability method. Based on the separation result of torque components and torque ripple components, with the goal of reducing the cogging torque under load, the optimization design of motor rotor structure is carried out, which provides reference value for the design of PMaSRM.
2022, 49(12):60-66，73.
DOI: 10.12177/emca.2022.135
Abstract:
Aiming at the special structure of hybrid excitation flux switching linear magnetic suspension motor (HEFSLMSM), the temperature rise characteristics under different working conditions are analyzed deeply. The structure and operating principle of HEFSLMSM are studied. Three-dimensional steady-state temperature field mathematical model and boundary condition equation are established. Considering the heat exchange of each part of the motor, the thermal conductivity of each material and the convective heat transfer coefficient of different parts of the motor are determined. The copper loss and iron loss of the motor are calculated by using the finite element method, and the corresponding heat of formation is obtained. The temperature variation under the condition of no load and load with natural heat dissipation is calculated in the finite element software. The cooling system is designed on the basis of the original model, and the temperature field distribution under different working conditions after the installation of the cooling device is calculated respectively. The rationality and feasibility of the cooling device are proved by comparing the temperature of natural air cooling with forced water cooling.
Aiming at the special structure of hybrid excitation flux switching linear magnetic suspension motor (HEFSLMSM), the temperature rise characteristics under different working conditions are analyzed deeply. The structure and operating principle of HEFSLMSM are studied. Three-dimensional steady-state temperature field mathematical model and boundary condition equation are established. Considering the heat exchange of each part of the motor, the thermal conductivity of each material and the convective heat transfer coefficient of different parts of the motor are determined. The copper loss and iron loss of the motor are calculated by using the finite element method, and the corresponding heat of formation is obtained. The temperature variation under the condition of no load and load with natural heat dissipation is calculated in the finite element software. The cooling system is designed on the basis of the original model, and the temperature field distribution under different working conditions after the installation of the cooling device is calculated respectively. The rationality and feasibility of the cooling device are proved by comparing the temperature of natural air cooling with forced water cooling.
2022, 49(12):67-73.
DOI: 10.12177/emca.2022.136
Abstract:
The permanent magnet traction system suitable for serialized Chinese standard metro trains is the development direction of the new generation of rail transit traction systems. The rotor initial position of the permanent magnet synchronous motor (PMSM) determines the size of the starting torque, and the polarity of the magnetic pole determines the direction of the starting torque. So the accuracy of rotor initial position identification is related to whether the motor can start stably and accelerate smoothly. Based on the analysis of the mathematical model of PMSM, the initial estimated position of the rotor is obtained by using the high frequency square wave voltage injection algorithm. Then a narrow positive and reverse voltage pulse is injected into the estimated d-axis, and the magnitude of the positive and reverse response current on the d-axis is compared to determine whether the positioned magnetic pole is the N pole or the S pole. If the magnetic pole is the S pole, the initial estimated position needs to be compensated to be positioned at the N pole. Simulink simulation and test results show that when the actual initial position of the motor is different, the algorithm can identify the correct rotor position. The maximum identification error is 0.052 4 rad, and the average error is 0.016 1 rad.
The permanent magnet traction system suitable for serialized Chinese standard metro trains is the development direction of the new generation of rail transit traction systems. The rotor initial position of the permanent magnet synchronous motor (PMSM) determines the size of the starting torque, and the polarity of the magnetic pole determines the direction of the starting torque. So the accuracy of rotor initial position identification is related to whether the motor can start stably and accelerate smoothly. Based on the analysis of the mathematical model of PMSM, the initial estimated position of the rotor is obtained by using the high frequency square wave voltage injection algorithm. Then a narrow positive and reverse voltage pulse is injected into the estimated d-axis, and the magnitude of the positive and reverse response current on the d-axis is compared to determine whether the positioned magnetic pole is the N pole or the S pole. If the magnetic pole is the S pole, the initial estimated position needs to be compensated to be positioned at the N pole. Simulink simulation and test results show that when the actual initial position of the motor is different, the algorithm can identify the correct rotor position. The maximum identification error is 0.052 4 rad, and the average error is 0.016 1 rad.
2022, 49(12):74-80.
DOI: 10.12177/emca.2022.132
Abstract:
The large-scale development of electric vehicle and the continuous construction of charging facilities seriously threaten the stability of the power system. However, there is still a lack of simple and effective methods for electric vehicle ownership and load prediction. Therefore, the prediction model of electric vehicle ownership based on the comprehensive prediction is established. Three prediction models of grey prediction, back propagation (BP) neural network and long-short term memory (LSTM) network are used to predict the electric vehicle ownership, and the prediction results of the single prediction model are obtained. The entropy weight method is used to assign weight to the prediction results of the single prediction model, and the comprehensive prediction results are calculated. The electric vehicle load prediction model based on Monte Carlo algorithm is established. On the basis of ownership prediction, the characteristic parameters of electric vehicle batteries and user travel habits are simulated to predict the disordered charging behavior of electric vehicle and to form the daily load curve of electric vehicle. Finally, the effectiveness of the proposed model is verified by the data of electric vehicle ownership and charging load in a city. The example analysis shows that the proposed comprehensive prediction model has higher prediction accuracy than the single prediction model, and the load prediction results show that the grid connection of large-scale electric vehicle will bring new challenges to the power system.
The large-scale development of electric vehicle and the continuous construction of charging facilities seriously threaten the stability of the power system. However, there is still a lack of simple and effective methods for electric vehicle ownership and load prediction. Therefore, the prediction model of electric vehicle ownership based on the comprehensive prediction is established. Three prediction models of grey prediction, back propagation (BP) neural network and long-short term memory (LSTM) network are used to predict the electric vehicle ownership, and the prediction results of the single prediction model are obtained. The entropy weight method is used to assign weight to the prediction results of the single prediction model, and the comprehensive prediction results are calculated. The electric vehicle load prediction model based on Monte Carlo algorithm is established. On the basis of ownership prediction, the characteristic parameters of electric vehicle batteries and user travel habits are simulated to predict the disordered charging behavior of electric vehicle and to form the daily load curve of electric vehicle. Finally, the effectiveness of the proposed model is verified by the data of electric vehicle ownership and charging load in a city. The example analysis shows that the proposed comprehensive prediction model has higher prediction accuracy than the single prediction model, and the load prediction results show that the grid connection of large-scale electric vehicle will bring new challenges to the power system.
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沪ICP备16038578号-3
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.
