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Issue 7,2026 Table of Contents
2026, 53(7):644-654.
DOI: 10.12177/emca.2026.187
Abstract:
[Objective] With the continuous increase in the penetration of renewable energy generation, the grid-forming (GFM) converter, owing to their capability to actively support voltage and frequency, are regarded as a key technology for enabling the reliable integration and stable operation of power systems with high shares of renewable energy. However, due to the limited overcurrent withstand capability of power semiconductor devices, when voltage sags or other faults occur in the power grid, the GFM converter prone to large power angle deviations, loss of synchronization, and transient overcurrent, thereby posing significant challenges to stable operation. [Methods] Aiming at the above problems, based on the equivalent impedance model of the GFM converter and combined with the vector diagrams of system voltage and output current before and after the fault, this paper systematically analyzed the generation mechanism of overcurrent during the fault process and the influence of voltage amplitude and power angle variations on the fault current amplitude. On the basis of the above analysis, a comprehensive control strategy that took into account both transient synchronization stability and fault overcurrent suppression was proposed. The proposed strategy adaptively reduced the active power reference value to enhance the power angle stability of the system. Meanwhile, to achieve overcurrent mitigation throughout the entire fault process, the strategy implemented fault current magnitude constraints along with a combined control approach. This approach synergized internal voltage reference regulation for steady state fault current management and virtual impedance for surge current suppression. [Results] Finally, the rationality and effectiveness of the proposed strategy were verified through comparative tests on an experimental platform. The experimental results indicated that, compared with conventional methods, the proposed strategy had significant advantages in enhancing the transient performance of the grid-forming converter. It was able to maintain the power angle stability of the system under various grid fault conditions, effectively suppress the fault current, and ensure that both the surge current and the steady-state fault overcurrent magnitude were below the preset thresholds. [Conclusion] The comprehensive fault ride-through strategy for the GFM converters considering current constraints proposed in this paper takes into account both power angle stability and overcurrent suppression, and can significantly improve the operational reliability of grid-forming converters under complex grid fault conditions.
[Objective] With the continuous increase in the penetration of renewable energy generation, the grid-forming (GFM) converter, owing to their capability to actively support voltage and frequency, are regarded as a key technology for enabling the reliable integration and stable operation of power systems with high shares of renewable energy. However, due to the limited overcurrent withstand capability of power semiconductor devices, when voltage sags or other faults occur in the power grid, the GFM converter prone to large power angle deviations, loss of synchronization, and transient overcurrent, thereby posing significant challenges to stable operation. [Methods] Aiming at the above problems, based on the equivalent impedance model of the GFM converter and combined with the vector diagrams of system voltage and output current before and after the fault, this paper systematically analyzed the generation mechanism of overcurrent during the fault process and the influence of voltage amplitude and power angle variations on the fault current amplitude. On the basis of the above analysis, a comprehensive control strategy that took into account both transient synchronization stability and fault overcurrent suppression was proposed. The proposed strategy adaptively reduced the active power reference value to enhance the power angle stability of the system. Meanwhile, to achieve overcurrent mitigation throughout the entire fault process, the strategy implemented fault current magnitude constraints along with a combined control approach. This approach synergized internal voltage reference regulation for steady state fault current management and virtual impedance for surge current suppression. [Results] Finally, the rationality and effectiveness of the proposed strategy were verified through comparative tests on an experimental platform. The experimental results indicated that, compared with conventional methods, the proposed strategy had significant advantages in enhancing the transient performance of the grid-forming converter. It was able to maintain the power angle stability of the system under various grid fault conditions, effectively suppress the fault current, and ensure that both the surge current and the steady-state fault overcurrent magnitude were below the preset thresholds. [Conclusion] The comprehensive fault ride-through strategy for the GFM converters considering current constraints proposed in this paper takes into account both power angle stability and overcurrent suppression, and can significantly improve the operational reliability of grid-forming converters under complex grid fault conditions.
2026, 53(7):655-664.
DOI: 10.12177/emca.2026.181
Abstract:
[Objective] The multi-objective optimization of brushless direct current (BLDC) motors frequently relies on extensive finite element analysis, which is computationally expensive and time-consuming. While surrogate models accelerate the design process, traditional data-driven models often suffer from prediction distortion near physical boundaries, resulting in non-physical output values. To address this issue, this paper proposes a novel robust optimization methodology integrating a physically constrained gaussian process regression (GPR) model with a lower confidence bound (LCB) strategy. [Methods] Firstly, an improved Morris trajectory sampling method based on the Campolongo strategy was adopted to perform global sensitivity analysis on the key structural parameters of the motor, and achieved dimensionality reduction and screening of design variables. Secondly, considering the physical characteristics of motor efficiency and torque ripple, logarithmic (Log) and logistic (Logit) transformations were introduced into the GPR modeling to construct a high-accuracy surrogate model that satisfied physical boundary constraints. Finally, uncertainty quantification was performed using the predictive variance information provided by GPR, and a robust optimization strategy based on LCB was proposed. Through a two-stage search, the comprehensive performance of the motor was improved, while the engineering robustness of the design scheme was effectively enhanced. [Results] The proposed framework demonstrated excellent performance during validation. The improved GPR model mathematically eliminated non-physical predictions at the boundaries of motor efficiency and torque ripple, and it achieved significantly higher generalization accuracy on the test set compared with conventional surrogate models. When applied to the design of the BLDC motor, the final optimized design exhibited substantial improvements. Specifically, the output torque was increased by 9.56%, and the torque ripple was dramatically reduced by 47.18%. Meanwhile, the overall motor efficiency was reliably maintained above 90%. [Conclusion] This method effectively addresses the computational bottlenecks and model prediction distortions present in BLDC motor optimization. By structurally integrating physical constraints within the modeling architecture and utilizing the uncertainty-aware LCB strategy, the proposed method comprehensively improves electromagnetic performance while effectively enhancing the engineering robustness and practical viability of the design scheme.
[Objective] The multi-objective optimization of brushless direct current (BLDC) motors frequently relies on extensive finite element analysis, which is computationally expensive and time-consuming. While surrogate models accelerate the design process, traditional data-driven models often suffer from prediction distortion near physical boundaries, resulting in non-physical output values. To address this issue, this paper proposes a novel robust optimization methodology integrating a physically constrained gaussian process regression (GPR) model with a lower confidence bound (LCB) strategy. [Methods] Firstly, an improved Morris trajectory sampling method based on the Campolongo strategy was adopted to perform global sensitivity analysis on the key structural parameters of the motor, and achieved dimensionality reduction and screening of design variables. Secondly, considering the physical characteristics of motor efficiency and torque ripple, logarithmic (Log) and logistic (Logit) transformations were introduced into the GPR modeling to construct a high-accuracy surrogate model that satisfied physical boundary constraints. Finally, uncertainty quantification was performed using the predictive variance information provided by GPR, and a robust optimization strategy based on LCB was proposed. Through a two-stage search, the comprehensive performance of the motor was improved, while the engineering robustness of the design scheme was effectively enhanced. [Results] The proposed framework demonstrated excellent performance during validation. The improved GPR model mathematically eliminated non-physical predictions at the boundaries of motor efficiency and torque ripple, and it achieved significantly higher generalization accuracy on the test set compared with conventional surrogate models. When applied to the design of the BLDC motor, the final optimized design exhibited substantial improvements. Specifically, the output torque was increased by 9.56%, and the torque ripple was dramatically reduced by 47.18%. Meanwhile, the overall motor efficiency was reliably maintained above 90%. [Conclusion] This method effectively addresses the computational bottlenecks and model prediction distortions present in BLDC motor optimization. By structurally integrating physical constraints within the modeling architecture and utilizing the uncertainty-aware LCB strategy, the proposed method comprehensively improves electromagnetic performance while effectively enhancing the engineering robustness and practical viability of the design scheme.
2026, 53(7):665-675.
DOI: 10.12177/emca.2026.184
Abstract:
[Objective] Aiming at the problems that the grounding shield layer of three-phase insulated tube-type busbars is prone to floating or degradation faults during operation, which further threatens equipment insulation and personal safety, this paper intends to deeply reveal the potential evolution mechanism under such faults and provide theoretical support for the analysis of electric field characteristics under fault conditions. [Methods] First, a field-circuit coupled finite element model of 35 kV three-phase insulated tube-type busbar was established,and the electric field distortion characteristics and potential distribution under the shield floating state were analyzed. Meanwhile, a three-phase coupled equivalent circuit model considering distributed parameters was constructed to calculate the variation law of the floating potential of the shielding layer with respect to the stray capacitance to ground and the grounding resistance. Furthermore, combined with a 1 km line model in practical engineering, the influence of distributed parameters on the induced voltage was analyzed. [Results] The results show that, compared with normal operating conditions, the surface potential of the faulty phase rises with the conductor under the floating shield layer, and electric field distortion occurred in the interphase air domain. The main insulation capacitance was significantly larger than the stray capacitance to ground, causing the shield layer to lose its potential clamping function. Under the resistance grounding condition where the shield layer is grounded through a resistor, the accumulation of displacement current caused by distributed parameters made long-distance busbar more sensitive to changes in grounding resistance. [Conclusion] Floating of the grounding shield layer results in a sustained and stable high-voltage state, and the associated electric field distortion severely damages the full shielding characteristic of insulated tube-type busbars. To ensure personal safety and equipment insulation safety, based on the parametric analytical model derived in this paper for estimating the safety threshold of grounding resistance, it is recommended that the grounding resistance of 1 km long insulated tube-type busbars at the 35 kV voltage level be controlled within 12.3 Ω to maintain sufficient safety margin.
[Objective] Aiming at the problems that the grounding shield layer of three-phase insulated tube-type busbars is prone to floating or degradation faults during operation, which further threatens equipment insulation and personal safety, this paper intends to deeply reveal the potential evolution mechanism under such faults and provide theoretical support for the analysis of electric field characteristics under fault conditions. [Methods] First, a field-circuit coupled finite element model of 35 kV three-phase insulated tube-type busbar was established,and the electric field distortion characteristics and potential distribution under the shield floating state were analyzed. Meanwhile, a three-phase coupled equivalent circuit model considering distributed parameters was constructed to calculate the variation law of the floating potential of the shielding layer with respect to the stray capacitance to ground and the grounding resistance. Furthermore, combined with a 1 km line model in practical engineering, the influence of distributed parameters on the induced voltage was analyzed. [Results] The results show that, compared with normal operating conditions, the surface potential of the faulty phase rises with the conductor under the floating shield layer, and electric field distortion occurred in the interphase air domain. The main insulation capacitance was significantly larger than the stray capacitance to ground, causing the shield layer to lose its potential clamping function. Under the resistance grounding condition where the shield layer is grounded through a resistor, the accumulation of displacement current caused by distributed parameters made long-distance busbar more sensitive to changes in grounding resistance. [Conclusion] Floating of the grounding shield layer results in a sustained and stable high-voltage state, and the associated electric field distortion severely damages the full shielding characteristic of insulated tube-type busbars. To ensure personal safety and equipment insulation safety, based on the parametric analytical model derived in this paper for estimating the safety threshold of grounding resistance, it is recommended that the grounding resistance of 1 km long insulated tube-type busbars at the 35 kV voltage level be controlled within 12.3 Ω to maintain sufficient safety margin.
2026, 53(7):676-687.
DOI: 10.12177/emca.2026.185
Abstract:
[Objective] The magnetic circuit of dual-stator axial-flux permanent magnet motors is distributed along both the axial and circumferential directions, requiring three-dimensional finite element method for electromagnetic performance analysis. Although the three-dimensional finite element method offers high computational accuracy, it also presents challenges such as high computational resource demands and long solution times. This paper proposes an equivalent magnetic network method that accurately accounts for nonlinear iterations, enabling fast and precise calculation of the electromagnetic performance of such motors. [Methods] First, considering the three-dimensional magnetic circuit characteristics of this motor, the motor was divided radially into multiple independent slices, with each slice equivalently modeled as a two-dimensional linear motor, thereby a quasi-three-dimensional layered magnetic network model that accounts for iron core saturation was established. Second, to overcome the convergence difficulties and poor robustness of traditional magnetic network models in nonlinear solutions, an iterative strategy with a dynamic relaxation factor based on the Sigmoid function was proposed. This strategy utilized the continuous and smooth characteristics of the Sigmoid function to dynamically adjust the relaxation factor, achieving fast convergence in the initial iteration stages and high-precision stable solutions in the final stages. Finally, taking a dual-stator axial-flux permanent magnet motor as an example, the calculated results were compared and analyzed with three-dimensional finite element results , demonstrating the correctness and feasibility of the proposed method. [Results] The established quasi-three-dimensional layered magnetic network model was able to accurately reflect the magnetic field distribution characteristics of this motor. The calculated results of air-gap flux density, no-load back electromotive force, and electromagnetic torque agreed well with the finite element simulation results, and the errors were within the engineering allowable range. In terms of nonlinear iteration performance, the proposed Sigmoid function dynamic relaxation factor strategy significantly improved the iterative convergence characteristics. Compared with the traditional fixed relaxation factor method, this strategy exhibited stronger robustness, the convergence process was more stable, it maintained stable convergence performance under different saturation levels, and it effectively avoided the iterative divergence problem that might occur in the deep saturation region. [Conclusion] The electromagnetic performance analysis method proposed in this paper, which is based on the quasi-three-dimensional layered magnetic network model and the Sigmoid function dynamic relaxation factor, balances computational accuracy and efficiency. This method possesses good generality and can be extended to the electromagnetic performance analysis of other types of axial-flux motors with complex magnetic circuit structures.
[Objective] The magnetic circuit of dual-stator axial-flux permanent magnet motors is distributed along both the axial and circumferential directions, requiring three-dimensional finite element method for electromagnetic performance analysis. Although the three-dimensional finite element method offers high computational accuracy, it also presents challenges such as high computational resource demands and long solution times. This paper proposes an equivalent magnetic network method that accurately accounts for nonlinear iterations, enabling fast and precise calculation of the electromagnetic performance of such motors. [Methods] First, considering the three-dimensional magnetic circuit characteristics of this motor, the motor was divided radially into multiple independent slices, with each slice equivalently modeled as a two-dimensional linear motor, thereby a quasi-three-dimensional layered magnetic network model that accounts for iron core saturation was established. Second, to overcome the convergence difficulties and poor robustness of traditional magnetic network models in nonlinear solutions, an iterative strategy with a dynamic relaxation factor based on the Sigmoid function was proposed. This strategy utilized the continuous and smooth characteristics of the Sigmoid function to dynamically adjust the relaxation factor, achieving fast convergence in the initial iteration stages and high-precision stable solutions in the final stages. Finally, taking a dual-stator axial-flux permanent magnet motor as an example, the calculated results were compared and analyzed with three-dimensional finite element results , demonstrating the correctness and feasibility of the proposed method. [Results] The established quasi-three-dimensional layered magnetic network model was able to accurately reflect the magnetic field distribution characteristics of this motor. The calculated results of air-gap flux density, no-load back electromotive force, and electromagnetic torque agreed well with the finite element simulation results, and the errors were within the engineering allowable range. In terms of nonlinear iteration performance, the proposed Sigmoid function dynamic relaxation factor strategy significantly improved the iterative convergence characteristics. Compared with the traditional fixed relaxation factor method, this strategy exhibited stronger robustness, the convergence process was more stable, it maintained stable convergence performance under different saturation levels, and it effectively avoided the iterative divergence problem that might occur in the deep saturation region. [Conclusion] The electromagnetic performance analysis method proposed in this paper, which is based on the quasi-three-dimensional layered magnetic network model and the Sigmoid function dynamic relaxation factor, balances computational accuracy and efficiency. This method possesses good generality and can be extended to the electromagnetic performance analysis of other types of axial-flux motors with complex magnetic circuit structures.
2026, 53(7):688-699.
DOI: 10.12177/emca.2026.183
Abstract:
[Objective] In order to reduce the dependence of maximum torque per ampere (MTPA) control of permanent magnet synchronous motor (PMSM) on motor parameters, the incremental model predictive current control (MPCC) MTPA for PMSM based on virtual signal injection is established in this paper. And model reference adaptive system (MRAS) is used to identify parameters to improve parameters robustness. [Methods] The traditional formula method was adopted to achieve MTPA, which required three parameters—stator dq-axis inductance and rotor flux. However, mismatches in these three parameters all had a large impact on the MTPA calculation results. An incremental MPCC for PMSM was established, which required three parameters—stator dq-axis inductance and stator resistance. Among them, the mismatch in stator resistance had a relatively small impact. The virtual signal injection method was adopted to achieve MTPA, which required two parameters—stator d-axis inductance and stator resistance. Among them, the mismatch in stator d-axis inductance had a large impact, while the mismatch in stator resistance had a small impact. In summary, an MTPA based on virtual signal injection combined with incremental MPCC for PMSM was established, and the MRAS was adopted to identify the stator dq-axis inductances. The identified parameters were used in both the incremental model predictive current control and the virtual signal injection MTPA to improve parameter robustness. A simulation model was built based on Matlab/Simulink, and the above methods were simulated and analyzed. [Results] The incremental MPCC for PMSM based on the virtual signal injection MTPA adopted MRAS to identify dq-axis stator inductance. Under both matched and mismatched parameter conditions, the error of the current vector angle βMTPA calculated by the virtual signal injection MTPA was small, and the impact of stator resistance mismatch was small. [Conclusion] The virtual signal injection MTPA only requires d-axis stator inductance and stator resistance, has low parameter dependency, and the impact of stator resistance mismatch is small. The incremental MPCC MTPA for PMSM based on virtual signal injection requires only the identification of the dq-axis inductances. The virtual signal injection MTPA using MRAS for parameter identification has a small error, which improves parameter robustness.
[Objective] In order to reduce the dependence of maximum torque per ampere (MTPA) control of permanent magnet synchronous motor (PMSM) on motor parameters, the incremental model predictive current control (MPCC) MTPA for PMSM based on virtual signal injection is established in this paper. And model reference adaptive system (MRAS) is used to identify parameters to improve parameters robustness. [Methods] The traditional formula method was adopted to achieve MTPA, which required three parameters—stator dq-axis inductance and rotor flux. However, mismatches in these three parameters all had a large impact on the MTPA calculation results. An incremental MPCC for PMSM was established, which required three parameters—stator dq-axis inductance and stator resistance. Among them, the mismatch in stator resistance had a relatively small impact. The virtual signal injection method was adopted to achieve MTPA, which required two parameters—stator d-axis inductance and stator resistance. Among them, the mismatch in stator d-axis inductance had a large impact, while the mismatch in stator resistance had a small impact. In summary, an MTPA based on virtual signal injection combined with incremental MPCC for PMSM was established, and the MRAS was adopted to identify the stator dq-axis inductances. The identified parameters were used in both the incremental model predictive current control and the virtual signal injection MTPA to improve parameter robustness. A simulation model was built based on Matlab/Simulink, and the above methods were simulated and analyzed. [Results] The incremental MPCC for PMSM based on the virtual signal injection MTPA adopted MRAS to identify dq-axis stator inductance. Under both matched and mismatched parameter conditions, the error of the current vector angle βMTPA calculated by the virtual signal injection MTPA was small, and the impact of stator resistance mismatch was small. [Conclusion] The virtual signal injection MTPA only requires d-axis stator inductance and stator resistance, has low parameter dependency, and the impact of stator resistance mismatch is small. The incremental MPCC MTPA for PMSM based on virtual signal injection requires only the identification of the dq-axis inductances. The virtual signal injection MTPA using MRAS for parameter identification has a small error, which improves parameter robustness.
2026, 53(7):700-710.
DOI: 10.12177/emca.2026.189
Abstract:
[Objective] To address the challenges of insufficient feature extraction, inadequate capture of temporal dependencies, and low diagnostic accuracy under noise interference in motor fault diagnosis under complex working conditions, a fault diagnosis method based on a multi-scale convolutional neural network, long short-term memory, and attention mechanism (MCNN-LSTM-Attention) is proposed. [Methods] First, through the MCNN module, the parallel convolutional layers were used to extract multi-scale local features from the vibration signals, addressing the problem of difficulty in comprehensively characterizing fault features. Then, the LSTM module was utilized to perform temporal modeling on the multi-scale feature sequences, deeply mining the temporal dependencies of the fault features. On this basis, the Attention mechanism was introduced to adaptively weight the temporal features, so as to focus on the key fault information and suppress irrelevant noise interference. Finally, the multiple modules worked collaboratively to solve the problems of incomplete feature representation and weak robustness of the diagnostic model. [Results] To comprehensively evaluate the robustness and adaptability of the proposed motor fault diagnosis method in practical applications, typical non-ideal factors such as load fluctuations and speed disturbances were simulated and introduced by emulating complex on-site operating conditions, and noise interferences of different intensities were superimposed during the signal acquisition process, so as to construct a test scenario close to the real industrial environment. On this basis, the diagnostic model was verified using actual collected motor operating data. The experimental results showed that the method maintained high diagnostic accuracy under various complex operating conditions and different noise levels, and at the same time, it exhibited good generalization capability in tests across different operating conditions and fault types. [Conclusion] The proposed method achieves accurate fault diagnosis of motors under complex operating conditions. This result provides reliable technical support for practical engineering applications, and also offers useful reference and new insights for preventive maintenance measures in subsequent equipment operation.
[Objective] To address the challenges of insufficient feature extraction, inadequate capture of temporal dependencies, and low diagnostic accuracy under noise interference in motor fault diagnosis under complex working conditions, a fault diagnosis method based on a multi-scale convolutional neural network, long short-term memory, and attention mechanism (MCNN-LSTM-Attention) is proposed. [Methods] First, through the MCNN module, the parallel convolutional layers were used to extract multi-scale local features from the vibration signals, addressing the problem of difficulty in comprehensively characterizing fault features. Then, the LSTM module was utilized to perform temporal modeling on the multi-scale feature sequences, deeply mining the temporal dependencies of the fault features. On this basis, the Attention mechanism was introduced to adaptively weight the temporal features, so as to focus on the key fault information and suppress irrelevant noise interference. Finally, the multiple modules worked collaboratively to solve the problems of incomplete feature representation and weak robustness of the diagnostic model. [Results] To comprehensively evaluate the robustness and adaptability of the proposed motor fault diagnosis method in practical applications, typical non-ideal factors such as load fluctuations and speed disturbances were simulated and introduced by emulating complex on-site operating conditions, and noise interferences of different intensities were superimposed during the signal acquisition process, so as to construct a test scenario close to the real industrial environment. On this basis, the diagnostic model was verified using actual collected motor operating data. The experimental results showed that the method maintained high diagnostic accuracy under various complex operating conditions and different noise levels, and at the same time, it exhibited good generalization capability in tests across different operating conditions and fault types. [Conclusion] The proposed method achieves accurate fault diagnosis of motors under complex operating conditions. This result provides reliable technical support for practical engineering applications, and also offers useful reference and new insights for preventive maintenance measures in subsequent equipment operation.
2026, 53(7):711-723.
DOI: 10.12177/emca.2026.188
Abstract:
[Objective] To address the limited excitation efficiency of single-harmonic excitation in multiphase self-excited synchronous machines under medium- and high-speed operating conditions, a multi-harmonic cooperative excitation strategy based on an improved genetic algorithm is proposed. This strategy fully utilizes the high control degrees of freedom of the multiphase machines and increases the rotor excitation current under the condition that the total stator harmonic current is limited. [Methods] First, the harmonic excitation mechanism of an eleven-phase self-excited synchronous machine was analyzed, and a mathematical model describing the relationship between stator harmonic currents and rotor excitation current was established. On this basis, the conventional single-harmonic excitation scheme was extended to a multi-harmonic cooperative excitation strategy, in which the third-, fifth-, seventh-, and ninth-order harmonics were introduced as the main excitation components. Subsequently, with the objective of maximizing the excitation current, a multi-harmonic amplitude allocation optimization model was constructed under the constraint of a constant total harmonic current effective value. An improved genetic algorithm with adaptive mechanisms was adopted to globally optimize the amplitude ratios of different harmonic currents. Finally, the optimal distribution of the fundamental and harmonic currents was achieved by combining with a lookup-table-based method. [Results] The proposed multi-harmonic excitation strategy was verified through finite element simulations and experimental platform. Under identical total harmonic current constraints, the optimized multi-harmonic excitation scheme exhibited superior excitation performance compared with the conventional single-harmonic excitation method. Experimental results demonstrate that the rotor excitation current was increased by approximately 11.5%, which was consistent with the simulation results and confirms the effectiveness of the proposed optimization approach. [Conclusion] The research results indicate that the proposed multi-harmonic cooperative excitation strategy based on an improved genetic algorithm can effectively enhance the excitation capability of the multiphase self-excited synchronous machines without increasing the total harmonic current. This strategy fully utilizes the control degrees of freedom of the multiphase systems, providing an effective solution for efficient excitation control in the medium-to-high speed range.
[Objective] To address the limited excitation efficiency of single-harmonic excitation in multiphase self-excited synchronous machines under medium- and high-speed operating conditions, a multi-harmonic cooperative excitation strategy based on an improved genetic algorithm is proposed. This strategy fully utilizes the high control degrees of freedom of the multiphase machines and increases the rotor excitation current under the condition that the total stator harmonic current is limited. [Methods] First, the harmonic excitation mechanism of an eleven-phase self-excited synchronous machine was analyzed, and a mathematical model describing the relationship between stator harmonic currents and rotor excitation current was established. On this basis, the conventional single-harmonic excitation scheme was extended to a multi-harmonic cooperative excitation strategy, in which the third-, fifth-, seventh-, and ninth-order harmonics were introduced as the main excitation components. Subsequently, with the objective of maximizing the excitation current, a multi-harmonic amplitude allocation optimization model was constructed under the constraint of a constant total harmonic current effective value. An improved genetic algorithm with adaptive mechanisms was adopted to globally optimize the amplitude ratios of different harmonic currents. Finally, the optimal distribution of the fundamental and harmonic currents was achieved by combining with a lookup-table-based method. [Results] The proposed multi-harmonic excitation strategy was verified through finite element simulations and experimental platform. Under identical total harmonic current constraints, the optimized multi-harmonic excitation scheme exhibited superior excitation performance compared with the conventional single-harmonic excitation method. Experimental results demonstrate that the rotor excitation current was increased by approximately 11.5%, which was consistent with the simulation results and confirms the effectiveness of the proposed optimization approach. [Conclusion] The research results indicate that the proposed multi-harmonic cooperative excitation strategy based on an improved genetic algorithm can effectively enhance the excitation capability of the multiphase self-excited synchronous machines without increasing the total harmonic current. This strategy fully utilizes the control degrees of freedom of the multiphase systems, providing an effective solution for efficient excitation control in the medium-to-high speed range.
2026, 53(7):724-732.
DOI: 10.12177/emca.2026.193
Abstract:
[Objective] The electrically excited flux-switching linear motor (EEFSLM) maglev system is a complex nonlinear system characterized by strong coupling and uncertain disturbances. The linear active disturbance rejection control (LADRC) exhibits limitations in maglev applications due to its fixed parameters and insufficient adaptability, making it difficult to reconcile the inherent trade-off between rapid dynamic response and strong disturbance rejection. To address this, this paper proposes a fuzzy active disturbance rejection control (FADRC) strategy to enhance the controller’s parameter self-adaptation capability. [Methods] Firstly, a mathematical model for the magnetic levitation direction was established based on the special structure and operating mechanism of the EEFSLM, and the magnetic levitation force equation and motion equation were derived. Secondly, the parameters of the linear state error feedback (LSEF) control law were tuned using the bandwidth method. To address the insufficient adaptability of fixed parameters, a FADRC controller was designed by introducing a fuzzy inference mechanism to adaptively adjust the LSEF bandwidth online. Finally, simulation analyses of FADRC, LADRC, and traditional proportional-integral (PI) control were conducted on the Matlab/Simulink platform. [Results] Simulation results showed that compared with LADRC and traditional PI control strategies, the FADRC strategy exhibited faster response speed and stronger disturbance rejection capability, demonstrating strong robustness and stability. [Conclusion] The proposed FADRC strategy in this paper significantly improves the control performance of the EEFSLM maglev system and can effectively meet the requirements for stable operation under high-performance conditions.
[Objective] The electrically excited flux-switching linear motor (EEFSLM) maglev system is a complex nonlinear system characterized by strong coupling and uncertain disturbances. The linear active disturbance rejection control (LADRC) exhibits limitations in maglev applications due to its fixed parameters and insufficient adaptability, making it difficult to reconcile the inherent trade-off between rapid dynamic response and strong disturbance rejection. To address this, this paper proposes a fuzzy active disturbance rejection control (FADRC) strategy to enhance the controller’s parameter self-adaptation capability. [Methods] Firstly, a mathematical model for the magnetic levitation direction was established based on the special structure and operating mechanism of the EEFSLM, and the magnetic levitation force equation and motion equation were derived. Secondly, the parameters of the linear state error feedback (LSEF) control law were tuned using the bandwidth method. To address the insufficient adaptability of fixed parameters, a FADRC controller was designed by introducing a fuzzy inference mechanism to adaptively adjust the LSEF bandwidth online. Finally, simulation analyses of FADRC, LADRC, and traditional proportional-integral (PI) control were conducted on the Matlab/Simulink platform. [Results] Simulation results showed that compared with LADRC and traditional PI control strategies, the FADRC strategy exhibited faster response speed and stronger disturbance rejection capability, demonstrating strong robustness and stability. [Conclusion] The proposed FADRC strategy in this paper significantly improves the control performance of the EEFSLM maglev system and can effectively meet the requirements for stable operation under high-performance conditions.
2026, 53(7):733-743.
DOI: 10.12177/emca.2026.191
Abstract:
[Objective] To address the challenge that the traditional lumped-parameter thermal network (LPTN) struggles to characterize the temperature gradient between the winding insulation layer and impregnating varnish, this paper proposes a transient temperature rise calculation method. This method couples conformal mapping with an LPTN to analytically derive the equivalent thermal conductivity of the windings. [Methods] Firstly, a 10 kW surface-mounted permanent magnet synchronous motor was taken as the research object. The conformal mapping method was utilized to transform the arc-shaped isothermal boundary of the copper conductors into a straight-line boundary, thereby mapping the complex non-uniform thermal field inside the winding into a flat-plate thermal field, and the equivalent thermal conductivity of the winding was solved analytically. Then, an equivalent thermal network model was constructed based on the obtained equivalent conduction thermal resistance of the winding, and the copper loss was iteratively updated by considering the influence of the winding resistivity changing with temperature. Finally, the reliability of the analytical model was verified by comparing with finite element analysis (FEA) and temperature rise experiments. [Results] The simulation and experimental results showed that the error between the LPTN and FEA for the winding temperature rise was 1.94%, which proved the high calculation accuracy of the thermal network model in predicting the winding thermal characteristics. The errors between the LPTN model and FEA/experimental results for housing temperature rise were found to be 3.20% and 6.56% respectively, which further validated the effectiveness of the proposed model.[Conclusion] The winding homogenization modeling method based on the conformal mapping method proposed in this paper can effectively reflect the thermal characteristics of the winding while ensuring the calculation efficiency, and can be extended to the thermal analysis of other motors. In addition, the analysis of the influence of the stator inner and outer diameter ratio on the transient temperature rise of the motor shows that the transient temperature rise of the motor can be effectively suppressed by reasonably allocating the proportion of copper consumption and iron consumption.
[Objective] To address the challenge that the traditional lumped-parameter thermal network (LPTN) struggles to characterize the temperature gradient between the winding insulation layer and impregnating varnish, this paper proposes a transient temperature rise calculation method. This method couples conformal mapping with an LPTN to analytically derive the equivalent thermal conductivity of the windings. [Methods] Firstly, a 10 kW surface-mounted permanent magnet synchronous motor was taken as the research object. The conformal mapping method was utilized to transform the arc-shaped isothermal boundary of the copper conductors into a straight-line boundary, thereby mapping the complex non-uniform thermal field inside the winding into a flat-plate thermal field, and the equivalent thermal conductivity of the winding was solved analytically. Then, an equivalent thermal network model was constructed based on the obtained equivalent conduction thermal resistance of the winding, and the copper loss was iteratively updated by considering the influence of the winding resistivity changing with temperature. Finally, the reliability of the analytical model was verified by comparing with finite element analysis (FEA) and temperature rise experiments. [Results] The simulation and experimental results showed that the error between the LPTN and FEA for the winding temperature rise was 1.94%, which proved the high calculation accuracy of the thermal network model in predicting the winding thermal characteristics. The errors between the LPTN model and FEA/experimental results for housing temperature rise were found to be 3.20% and 6.56% respectively, which further validated the effectiveness of the proposed model.[Conclusion] The winding homogenization modeling method based on the conformal mapping method proposed in this paper can effectively reflect the thermal characteristics of the winding while ensuring the calculation efficiency, and can be extended to the thermal analysis of other motors. In addition, the analysis of the influence of the stator inner and outer diameter ratio on the transient temperature rise of the motor shows that the transient temperature rise of the motor can be effectively suppressed by reasonably allocating the proportion of copper consumption and iron consumption.
2026, 53(7):744-753.
DOI: 10.12177/emca.2026.192
Abstract:
[Objective] To address the issue of multi-motor coordination failure in armored vehicle observation platforms caused by strong disturbances and sudden load impacts under complex off-road conditions, this paper proposes a control strategy based on active disturbance rejection control (ADRC) and Gaussian adaptive deviation coupling. [Methods] ADRC was introduced into the bottom speed loop of the single-axis motor, and both internal parameter variations and external instantaneous shocks were treated as a total disturbance for active compensation, thereby enhancing the anti-disturbance capability and response speed of the individual motor. To address the rigidity of traditional fixed-gain cooperative control and its tendency to cause inter-motor coupling, an adaptive cooperative regulator based on a Gaussian function was designed. During routine spatial motion of the platform, a smooth dead zone was constructed to accommodate the natural phase lag of asymmetric movement. Conversely, upon sudden strong load impact, the step characteristics of the Gaussian function were leveraged to instantaneously excite high cooperative stiffness, achieving dynamic proportional coordination and deceleration yielding among the multiple axes. [Results] Simulation results showed that, compared with traditional fixed-gain methods, the transient maximum cooperative error was reduced by 60% under equivalent variable load conditions, and the time for the system to recover stability after disturbance was shortened by 40% to 66.7%. [Conclusion] The control strategy proposed in this paper effectively reconciles the smoothness of the system during steady-state operation with its disturbance rejection robustness under extreme impact conditions, providing a reliable theoretical and practical reference for the cooperative control of high-dynamic parallel stabilization platforms.
[Objective] To address the issue of multi-motor coordination failure in armored vehicle observation platforms caused by strong disturbances and sudden load impacts under complex off-road conditions, this paper proposes a control strategy based on active disturbance rejection control (ADRC) and Gaussian adaptive deviation coupling. [Methods] ADRC was introduced into the bottom speed loop of the single-axis motor, and both internal parameter variations and external instantaneous shocks were treated as a total disturbance for active compensation, thereby enhancing the anti-disturbance capability and response speed of the individual motor. To address the rigidity of traditional fixed-gain cooperative control and its tendency to cause inter-motor coupling, an adaptive cooperative regulator based on a Gaussian function was designed. During routine spatial motion of the platform, a smooth dead zone was constructed to accommodate the natural phase lag of asymmetric movement. Conversely, upon sudden strong load impact, the step characteristics of the Gaussian function were leveraged to instantaneously excite high cooperative stiffness, achieving dynamic proportional coordination and deceleration yielding among the multiple axes. [Results] Simulation results showed that, compared with traditional fixed-gain methods, the transient maximum cooperative error was reduced by 60% under equivalent variable load conditions, and the time for the system to recover stability after disturbance was shortened by 40% to 66.7%. [Conclusion] The control strategy proposed in this paper effectively reconciles the smoothness of the system during steady-state operation with its disturbance rejection robustness under extreme impact conditions, providing a reliable theoretical and practical reference for the cooperative control of high-dynamic parallel stabilization platforms.
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沪ICP备16038578号-3
Electric Machines & Control Application ® 2026
Supported by:Beijing E-Tiller Technology Development Co., Ltd.
沪ICP备16038578号-3
Electric Machines & Control Application ® 2026
Supported by:Beijing E-Tiller Technology Development Co., Ltd.
