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Volume 51,Issue 11,2024 Table of Contents
2024, 51(11):1-10.
DOI: 10.12177/emca.2024.117
Abstract:
[Objective] This article focuses on the challenges of stator magnetic field orientation in mover current vector control for the quasi-synchronous operation of a doubly-fed linear motor (DFLM). In high-speed maglev applications, the mover magnetomotive force significantly exceeds that of the stator, making it difficult to obtain accurate stator magnetic field information. Additionally, harmonic injection in the mover current during levitation control presents further challenges. To address these issues, this paper introduced an innovative stator magnetic field orientation method based on a mover-fixed α-β coordinate system, along with a corresponding parameter correction method. [Method] By conducting error analysis and parameter correction, the proposed method determined the mover-side parameters with sufficient precision. This enabled the direct and precise calculation of the relatively small stator back electromotive force in the mover windings, facilitating magnetic field orientation. [Results] The proposed method was validated on a DFLM test platform. The results demonstrated that, with sufficiently precise mover-side parameters, the flux observation algorithm successfully converged the T^-axis component of the stator current to zero, thereby achieving effective magnetic field orientation. [Conclusion] The proposed method accurately determined mover parameters and achieved reliable magnetic field orientation. It was unaffected by harmonic injection in the mover current, maintaining good dynamic performance and ensuring stable magnetic field orientation.
[Objective] This article focuses on the challenges of stator magnetic field orientation in mover current vector control for the quasi-synchronous operation of a doubly-fed linear motor (DFLM). In high-speed maglev applications, the mover magnetomotive force significantly exceeds that of the stator, making it difficult to obtain accurate stator magnetic field information. Additionally, harmonic injection in the mover current during levitation control presents further challenges. To address these issues, this paper introduced an innovative stator magnetic field orientation method based on a mover-fixed α-β coordinate system, along with a corresponding parameter correction method. [Method] By conducting error analysis and parameter correction, the proposed method determined the mover-side parameters with sufficient precision. This enabled the direct and precise calculation of the relatively small stator back electromotive force in the mover windings, facilitating magnetic field orientation. [Results] The proposed method was validated on a DFLM test platform. The results demonstrated that, with sufficiently precise mover-side parameters, the flux observation algorithm successfully converged the T^-axis component of the stator current to zero, thereby achieving effective magnetic field orientation. [Conclusion] The proposed method accurately determined mover parameters and achieved reliable magnetic field orientation. It was unaffected by harmonic injection in the mover current, maintaining good dynamic performance and ensuring stable magnetic field orientation.
2024, 51(11):11-20.
DOI: 10.12177/emca.2024.122
Abstract:
[Objective] The DC bus sampling method introduces additional harmonics into the phase current due to the use of pulse width modulation (PWM) phase shifting to compensate for the current reconstruction dead zone. To solve this problem, a phase current reconstruction scheme based on the multiple-branch sampling method was proposed. [Method] A theoretical analysis of the DC bus sampling method and the multiple-branch sampling method was conducted, focusing on the causes of the current reconstruction dead zone. Matlab/Simulink simulations were used for a comparative analysis of both methods. [Results] Simulation results indicated that the multiple-branch sampling method effectively avoided the dead zones in the low modulation index region and sector boundaries present in the DC current sampling method, reduced the harmonic content in the current, and enhanced the accuracy of current reconstruction. Experiments conducted at a motor speed of 60 r/min demonstrated that the maximum error in the reconstructed current was less than 0.5 A. [Conclusion] The experimental results verified the effectiveness and feasibility of the proposed multiple-branch sampling method for current reconstruction.
[Objective] The DC bus sampling method introduces additional harmonics into the phase current due to the use of pulse width modulation (PWM) phase shifting to compensate for the current reconstruction dead zone. To solve this problem, a phase current reconstruction scheme based on the multiple-branch sampling method was proposed. [Method] A theoretical analysis of the DC bus sampling method and the multiple-branch sampling method was conducted, focusing on the causes of the current reconstruction dead zone. Matlab/Simulink simulations were used for a comparative analysis of both methods. [Results] Simulation results indicated that the multiple-branch sampling method effectively avoided the dead zones in the low modulation index region and sector boundaries present in the DC current sampling method, reduced the harmonic content in the current, and enhanced the accuracy of current reconstruction. Experiments conducted at a motor speed of 60 r/min demonstrated that the maximum error in the reconstructed current was less than 0.5 A. [Conclusion] The experimental results verified the effectiveness and feasibility of the proposed multiple-branch sampling method for current reconstruction.
2024, 51(11):21-31.
DOI: 10.12177/emca.2024.114
Abstract:
[Objective] In order to improve the control performance of model predictive current control (MPCC) for permanent magnet synchronous motor (PMSM), an LC filter was added between the PMSM and the inverter. [Method] A third-order multi-step MPCC system with LC filter was established. Predictive control was applied to the inverter current, capacitor voltage, and motor current, with simulation comparisons made against a first-order multi-step MPCC and field orientation control (FOC). [Results] Comparison results showed that under the same switching frequency and number of prediction steps, the third-order MPCC significantly reduced torque ripple and current ripple in the PMSM compared to the first-order MPCC, while also achieving lower total harmonic distortion (THD) in the current. Additionally, as the number of prediction steps increased in the third-order MPCC, both current THD, current ripple, and torque ripple progressively decreased. [Conclusion] As the number of prediction steps increases, the control performance of the third-order MPCC system gradually improves. The third-order MPCC outperforms the first-order MPCC. At higher switching frequencies, the third-order MPCC with 4-5 prediction steps delivers better control performance than FOC.
[Objective] In order to improve the control performance of model predictive current control (MPCC) for permanent magnet synchronous motor (PMSM), an LC filter was added between the PMSM and the inverter. [Method] A third-order multi-step MPCC system with LC filter was established. Predictive control was applied to the inverter current, capacitor voltage, and motor current, with simulation comparisons made against a first-order multi-step MPCC and field orientation control (FOC). [Results] Comparison results showed that under the same switching frequency and number of prediction steps, the third-order MPCC significantly reduced torque ripple and current ripple in the PMSM compared to the first-order MPCC, while also achieving lower total harmonic distortion (THD) in the current. Additionally, as the number of prediction steps increased in the third-order MPCC, both current THD, current ripple, and torque ripple progressively decreased. [Conclusion] As the number of prediction steps increases, the control performance of the third-order MPCC system gradually improves. The third-order MPCC outperforms the first-order MPCC. At higher switching frequencies, the third-order MPCC with 4-5 prediction steps delivers better control performance than FOC.
2024, 51(11):32-43.
DOI: 10.12177/emca.2024.123
Abstract:
[Objective] With the rapid development of modern power grids, power quality issues have become significant concerns in power systems. These issues, including voltage fluctuations, harmonics, transient pulses, and voltage interruptions, not only affect the safe operation of power systems but can also lead to equipment damage and energy waste. To address the limitations of traditional power quality classification methods, which suffer from poor recognition performance, high computational complexity, and data privacy concerns, this paper proposed a federated weighted resampling and hybrid optimization (FedWRHO) algorithm, which leverages federated learning and edge computing. [Method] First, a hybrid model integrating convolutional neural network (CNN) and long short-term memory (LSTM) network was designed to efficiently extract spatiotemporal features of power quality signals, with local training performed on edge devices. Subsequently, a distributed federated learning framework was established by integrating edge computing with federated learning, allowing edge nodes to utilize local data for training while aggregating models in the cloud to improve overall model performance. Finally, detailed experimental validation was conducted to assess the effectiveness of the proposed FedWRHO algorithm and its hybrid CNN-LSTM model. [Results] The findings indicated that the model achieved high classification accuracy for 14 types of power signals, with most categories reaching or exceeding 95% accuracy. Additionally, the data transmission volume and storage requirements for edge training were significantly lower than those for centralized training. [Conclusion] The method proposed in this paper not only demonstrates superior classification performance and data privacy protection but also effectively addresses issues related to computational resource allocation and adaptability, indicating broad application potential.
[Objective] With the rapid development of modern power grids, power quality issues have become significant concerns in power systems. These issues, including voltage fluctuations, harmonics, transient pulses, and voltage interruptions, not only affect the safe operation of power systems but can also lead to equipment damage and energy waste. To address the limitations of traditional power quality classification methods, which suffer from poor recognition performance, high computational complexity, and data privacy concerns, this paper proposed a federated weighted resampling and hybrid optimization (FedWRHO) algorithm, which leverages federated learning and edge computing. [Method] First, a hybrid model integrating convolutional neural network (CNN) and long short-term memory (LSTM) network was designed to efficiently extract spatiotemporal features of power quality signals, with local training performed on edge devices. Subsequently, a distributed federated learning framework was established by integrating edge computing with federated learning, allowing edge nodes to utilize local data for training while aggregating models in the cloud to improve overall model performance. Finally, detailed experimental validation was conducted to assess the effectiveness of the proposed FedWRHO algorithm and its hybrid CNN-LSTM model. [Results] The findings indicated that the model achieved high classification accuracy for 14 types of power signals, with most categories reaching or exceeding 95% accuracy. Additionally, the data transmission volume and storage requirements for edge training were significantly lower than those for centralized training. [Conclusion] The method proposed in this paper not only demonstrates superior classification performance and data privacy protection but also effectively addresses issues related to computational resource allocation and adaptability, indicating broad application potential.
2024, 51(11):44-53.
DOI: 10.12177/emca.2024.126
Abstract:
[Objective] To solve the problem of poor robustness in the model predictive torque control (MPTC) algorithm caused by parameter mismatch in the control model of permanent magnet synchronous motors, an MPTC algorithm based on an unweighted factor proportional integral derivative (PID) cost function was proposed. [Methods] The PID cost function eliminated static error by constructing an integral error value function and suppressed the oscillation of torque and flux linkage errors by constructing a differential error value function. However, since torque and flux linkage had different dimensions, the PID cost function still included weight coefficients. To solve this problem, an unweighted dual-cost function parallel strategy was proposed, which transformed the multi-objective cost function into a single-objective cost function to obtain a set of voltage vectors. The current-type cost function was used as the final criterion to evaluate the optimal voltage vector, thus eliminating the need for weight coefficient. Finally, the proposed unweighted factor PID cost function MPTC was compared with the traditional cost function MPTC through simulations. [Results] The results show that MPTC with unweighted factor PID cost function can suppress torque and flux ripple, reduce the dependence of MPTC on model parameters, and solve the difficulty in adapting to different operating conditions because of the existence of weight coefficient, while maintaining the advantage of MPTC’s fast dynamic response. [Conclusion] The proposed cost function demonstrates good feasibility in MPTC for permanent magnet synchronous motors.
[Objective] To solve the problem of poor robustness in the model predictive torque control (MPTC) algorithm caused by parameter mismatch in the control model of permanent magnet synchronous motors, an MPTC algorithm based on an unweighted factor proportional integral derivative (PID) cost function was proposed. [Methods] The PID cost function eliminated static error by constructing an integral error value function and suppressed the oscillation of torque and flux linkage errors by constructing a differential error value function. However, since torque and flux linkage had different dimensions, the PID cost function still included weight coefficients. To solve this problem, an unweighted dual-cost function parallel strategy was proposed, which transformed the multi-objective cost function into a single-objective cost function to obtain a set of voltage vectors. The current-type cost function was used as the final criterion to evaluate the optimal voltage vector, thus eliminating the need for weight coefficient. Finally, the proposed unweighted factor PID cost function MPTC was compared with the traditional cost function MPTC through simulations. [Results] The results show that MPTC with unweighted factor PID cost function can suppress torque and flux ripple, reduce the dependence of MPTC on model parameters, and solve the difficulty in adapting to different operating conditions because of the existence of weight coefficient, while maintaining the advantage of MPTC’s fast dynamic response. [Conclusion] The proposed cost function demonstrates good feasibility in MPTC for permanent magnet synchronous motors.
2024, 51(11):54-63.
DOI: 10.12177/emca.2024.121
Abstract:
[Objective] This paper combined linearized feedback control with adaptive global integral sliding mode control (AGISMC) to optimize the control performance and the control accuracy of the system based on the nonlinear characteristics, uncertainty and susceptibility to external perturbations of the magnetic suspension ball system. [Methods] First, a mathematical model was established using linearized feedback, and adaptive control was employed to achieve real-time estimation of the system parameters to reduce the uncertainty. Second, a global function was introduced to improve the transient response of the system, and the sign function in the sliding mode function was replaced with a saturation function to reduce system chattering. Third, for uncertain disturbances, an extended state observer (ESO) was utilized to estimate unknown disturbance signals in real time. Finally, simulations were conducted in Matlab/Simulink, and an experimental platform was built for validation. [Results] Simulation and experimental
[Objective] This paper combined linearized feedback control with adaptive global integral sliding mode control (AGISMC) to optimize the control performance and the control accuracy of the system based on the nonlinear characteristics, uncertainty and susceptibility to external perturbations of the magnetic suspension ball system. [Methods] First, a mathematical model was established using linearized feedback, and adaptive control was employed to achieve real-time estimation of the system parameters to reduce the uncertainty. Second, a global function was introduced to improve the transient response of the system, and the sign function in the sliding mode function was replaced with a saturation function to reduce system chattering. Third, for uncertain disturbances, an extended state observer (ESO) was utilized to estimate unknown disturbance signals in real time. Finally, simulations were conducted in Matlab/Simulink, and an experimental platform was built for validation. [Results] Simulation and experimental
2024, 51(11):64-74.
DOI: 10.12177/emca.2024.127
Abstract:
[Objective] In order to improve the dynamic response and robustness of permanent magnet linear synchronous motor (PMLSM) servo systems, a dual-closed-loop cascade composite control method was proposed, combining robust velocity control with delay compensation and plug-in repetitive proportional differential(PD) position control. [Methods] Initially, a robust velocity controller was designed based on the reference model robust compensation principle, which effectively addressed the modeling errors between the reference model and the actual system model, using an inverse system delay model to mitigate the effects of system transmission delays. In addition, to effectively suppress periodic external disturbances, a plug-in repetitive controller was integrated with the PD position controller, forming a plug-in repetitive PD position controller. [Results] Experimental results demonstrate that this dual closed-loop cascade control structure significantly reduces position errors, addresses the degradation of system performance and overshoot caused by transmission delays, and effectively mitigates the impact of external periodic disturbances. [Conclusion] The proposed composite control method enables the PMLSM position servo system to achieve high-precision tracking of periodic signals, thereby improving the system’s dynamic response and robustness.
[Objective] In order to improve the dynamic response and robustness of permanent magnet linear synchronous motor (PMLSM) servo systems, a dual-closed-loop cascade composite control method was proposed, combining robust velocity control with delay compensation and plug-in repetitive proportional differential(PD) position control. [Methods] Initially, a robust velocity controller was designed based on the reference model robust compensation principle, which effectively addressed the modeling errors between the reference model and the actual system model, using an inverse system delay model to mitigate the effects of system transmission delays. In addition, to effectively suppress periodic external disturbances, a plug-in repetitive controller was integrated with the PD position controller, forming a plug-in repetitive PD position controller. [Results] Experimental results demonstrate that this dual closed-loop cascade control structure significantly reduces position errors, addresses the degradation of system performance and overshoot caused by transmission delays, and effectively mitigates the impact of external periodic disturbances. [Conclusion] The proposed composite control method enables the PMLSM position servo system to achieve high-precision tracking of periodic signals, thereby improving the system’s dynamic response and robustness.
2024, 51(11):75-84.
DOI: 10.12177/emca.2024.115
Abstract:
[Objective] The introduction of virtual synchronous control complicates the sub-synchronous oscillation between double-fed induction generator (DFIG) and line compensation devices. To address this issue, a sub-synchronous oscillation suppression strategy based on model predictive control (MPC) for a virtual synchronous DFIG grid-connected system is proposed. [Method] First, the second-order expressions of virtual inertia and damping were derived from the virtual synchronous generator (VSG) impedance model, and the impact of parameter variations on sub-synchronous oscillations in the grid-connected system was investigated from the perspective of impedance characteristics. Second, using a three-phase two-level voltage equation with switching functions, the prediction functions of active and reactive power output from the converter were derived. A direct power predictive inner-loop control based on MPC was established to achieve optimal control with minimum power fluctuation. Finally, the MPC_VSG control strategy was analyzed using the frequency sweep method. [Results] The proposed MPC_VSG control strategy was verified by hardware-in-loop experiment based on RT-LAB .The results demonstrated that, under different series compensation levels and wind speeds, the MPC_VSG control strategy can suppress sub-synchronous oscillations within 0.5 seconds, and exhibits strong robustness. [Conclusion] The MPC_VSG control strategy designed in this paper selects the optimal switching state by targeting minimal power fluctuations, achieving the subsynchronous oscillation effective suppression.
[Objective] The introduction of virtual synchronous control complicates the sub-synchronous oscillation between double-fed induction generator (DFIG) and line compensation devices. To address this issue, a sub-synchronous oscillation suppression strategy based on model predictive control (MPC) for a virtual synchronous DFIG grid-connected system is proposed. [Method] First, the second-order expressions of virtual inertia and damping were derived from the virtual synchronous generator (VSG) impedance model, and the impact of parameter variations on sub-synchronous oscillations in the grid-connected system was investigated from the perspective of impedance characteristics. Second, using a three-phase two-level voltage equation with switching functions, the prediction functions of active and reactive power output from the converter were derived. A direct power predictive inner-loop control based on MPC was established to achieve optimal control with minimum power fluctuation. Finally, the MPC_VSG control strategy was analyzed using the frequency sweep method. [Results] The proposed MPC_VSG control strategy was verified by hardware-in-loop experiment based on RT-LAB .The results demonstrated that, under different series compensation levels and wind speeds, the MPC_VSG control strategy can suppress sub-synchronous oscillations within 0.5 seconds, and exhibits strong robustness. [Conclusion] The MPC_VSG control strategy designed in this paper selects the optimal switching state by targeting minimal power fluctuations, achieving the subsynchronous oscillation effective suppression.
2024, 51(11):85-96.
DOI: 10.12177/emca.2024.119
Abstract:
[Objective] To address the limitations of traditional large-scale permanent magnet synchronous wind generators, which cannot regulate the excitation magnetic field, and traditional double-fed generators, which are not suitable for direct drive and require post-installation maintenance, a modular dual-rotor synchronous wind generator is proposed. [Method] The stator winding of the generator was designed using fractional slot concentrated winding (FSCW), while both rotor windings were concentrated and excited by direct current. The stator excitation generated two dominant poles, which magnetically coupled with the dominant poles of the two rotors, forming a flux linkage. First, the basic structure and operating principle of the generator were introduced. Following this, a mathematical model was developed and the electromagnetic characteristics, including flux linkage and induced electromotive force, were analyzed based on FSCW theory. Finally, a finite element simulation model was created, and the simulation results were compared with the theoretical predictions. [Results] The results indicated that flux linkage calculations were accurate when the magnetic surfaces of the stator and rotor teeth were aligned. However, when the teeth were misaligned, the presence of air-gap leakage inductance introduced a certain degree of error in the calculations. [Conclusion] The simulation results are generally consistent with the theoretical calculations, validating the accuracy of the proposed modular dual-rotor synchronous wind generator design.
[Objective] To address the limitations of traditional large-scale permanent magnet synchronous wind generators, which cannot regulate the excitation magnetic field, and traditional double-fed generators, which are not suitable for direct drive and require post-installation maintenance, a modular dual-rotor synchronous wind generator is proposed. [Method] The stator winding of the generator was designed using fractional slot concentrated winding (FSCW), while both rotor windings were concentrated and excited by direct current. The stator excitation generated two dominant poles, which magnetically coupled with the dominant poles of the two rotors, forming a flux linkage. First, the basic structure and operating principle of the generator were introduced. Following this, a mathematical model was developed and the electromagnetic characteristics, including flux linkage and induced electromotive force, were analyzed based on FSCW theory. Finally, a finite element simulation model was created, and the simulation results were compared with the theoretical predictions. [Results] The results indicated that flux linkage calculations were accurate when the magnetic surfaces of the stator and rotor teeth were aligned. However, when the teeth were misaligned, the presence of air-gap leakage inductance introduced a certain degree of error in the calculations. [Conclusion] The simulation results are generally consistent with the theoretical calculations, validating the accuracy of the proposed modular dual-rotor synchronous wind generator design.
2024, 51(11):97-109.
DOI: 10.12177/emca.2024.125
Abstract:
[Objective] To effectively solve the environmental economic dispatch problem in power systems, this paper proposed an improved particle swarm optimization (PSO) algorithm based on grey wolf optimization (GWO) to optimize both the fuel costs and pollutant emissions. [Methods]First, the refracted opposition-based learning of refraction was applied to the initial particle swarm to generate inverse solutions, hereby enhancing population diversity. During the algorithm’s iteration process, the GWO algorithm was combined with PSO to guide the elite individuals in the particle swarm to conduct optimal searches, improving PSO’s optimization capability and convergence accuracy. In the later stages of the algorithm, to address the drawback where the particle swarm easily fell into local optima, Tent chaotic mapping was used to perturb the optimal particles. The individual best and global best positions of the particle swarm were then updated based on the fitness values. [Results] The improved algorithm was applied to 6-unit and 40-unit generator systems with different load demands. The convergence curves of the proposed algorithm, PSO algorithm. And GWO algorithm were compared for solving the power system, and the results showed that the proposed improved algorithm converged to the optimal value more quickly and resulted in the lowest fuel cost. [Conclusion] The improved algorithm proposed in this paper effectively solves complex constrained optimization problems and performs well in optimization accuracy and stability.
[Objective] To effectively solve the environmental economic dispatch problem in power systems, this paper proposed an improved particle swarm optimization (PSO) algorithm based on grey wolf optimization (GWO) to optimize both the fuel costs and pollutant emissions. [Methods]First, the refracted opposition-based learning of refraction was applied to the initial particle swarm to generate inverse solutions, hereby enhancing population diversity. During the algorithm’s iteration process, the GWO algorithm was combined with PSO to guide the elite individuals in the particle swarm to conduct optimal searches, improving PSO’s optimization capability and convergence accuracy. In the later stages of the algorithm, to address the drawback where the particle swarm easily fell into local optima, Tent chaotic mapping was used to perturb the optimal particles. The individual best and global best positions of the particle swarm were then updated based on the fitness values. [Results] The improved algorithm was applied to 6-unit and 40-unit generator systems with different load demands. The convergence curves of the proposed algorithm, PSO algorithm. And GWO algorithm were compared for solving the power system, and the results showed that the proposed improved algorithm converged to the optimal value more quickly and resulted in the lowest fuel cost. [Conclusion] The improved algorithm proposed in this paper effectively solves complex constrained optimization problems and performs well in optimization accuracy and stability.
11 Comparison Study on Pole Pair Harmonic Suppression Schemes in Fractional Slot Concentrated Windings
2024, 51(11):110-122.
DOI: 10.12177/emca.2024.118
Abstract:
[Objective] To address the issue of high-amplitude and low-order harmonics in the magnetomotive force of fractional slot concentrated winding (FSCW) induction motors, this paper proposes three different harmonic suppression schemes: dual Y-shaped winding, star-delta winding, and specific subharmonic suppression winding, aiming to suppress non-dominant pole harmonics. [Method] First, the basic principles of the three harmonic suppression schemes and the distribution patterns of magnetomotive force harmonics under different pole-slot combinations were analyzed. Second, the relationship between the winding distribution factor for each harmonic and the number of turns in different schemes was derived. Then, the theoretical calculation of the suppression effects on the magnetomotive force of various harmonics under different harmonic suppression schemes was carried out. Finally, a 2D finite element model of the FSCW induction motor was established, and the electromagnetic characteristics of the motor under differe
[Objective] To address the issue of high-amplitude and low-order harmonics in the magnetomotive force of fractional slot concentrated winding (FSCW) induction motors, this paper proposes three different harmonic suppression schemes: dual Y-shaped winding, star-delta winding, and specific subharmonic suppression winding, aiming to suppress non-dominant pole harmonics. [Method] First, the basic principles of the three harmonic suppression schemes and the distribution patterns of magnetomotive force harmonics under different pole-slot combinations were analyzed. Second, the relationship between the winding distribution factor for each harmonic and the number of turns in different schemes was derived. Then, the theoretical calculation of the suppression effects on the magnetomotive force of various harmonics under different harmonic suppression schemes was carried out. Finally, a 2D finite element model of the FSCW induction motor was established, and the electromagnetic characteristics of the motor under differe
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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.
