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Due to the particularity of the synchronous reluctance motor (SynRM) structure, a novel high-performance model predictive torque control (MPTC) method was proposed to reduce the high torque ripple and improve the performance and efficiency of the motor. First, the precise parameters of the SynRM reflecting the magnetic saturation characteristics were calculated using finite element analysis (FEA) data, and the torque and flux linkage maximum torque per ampere (MTPA) trajectory was derived by considering the saturation characteristics. Then, an MPTC model of a SynRM with duty cycle control was established, the MTPA trajectory stored in a look-up table was introduced into the control model, and the duration of the active voltage vector in one control cycle was calculated by evaluating the torque error. Finally, an experimental platform based on a SynRM prototype was built, and various performance comparison experiments were carried out for the proposed MPTC method. The experimental results show that the proposed method could reduce the torque ripple of the motor, the performance of the motor was significantly improved under various working conditions, and its correctness and effectiveness were verified.
Yuanzhe Zhao; Linjie Ren; Zhiming Liao; Guobin Lin. A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor. Energies 2021, 14, 2256 .
AMA StyleYuanzhe Zhao, Linjie Ren, Zhiming Liao, Guobin Lin. A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor. Energies. 2021; 14 (8):2256.
Chicago/Turabian StyleYuanzhe Zhao; Linjie Ren; Zhiming Liao; Guobin Lin. 2021. "A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor." Energies 14, no. 8: 2256.
In rail transit traction, due to the remarkable energy-saving and low-cost characteristics, synchronous reluctance motors (SynRM) may be a potential substitute for traditional AC motors. However, in the parameter extraction of SynRM nonlinear magnetic model, the accuracy and robustness of the metaheuristic algorithm is restricted by the excessive dependence on fitness evaluation. In this paper, a novel probability-driven smart collaborative performance (SCP) is defined to quantify the comprehensive contribution of candidate solution in current population. With the quantitative results of SCP as feedback in-formation, an algorithm updating mechanism with improved evolutionary quality is established. The allocation of computing resources induced by SCP achieves a good balance between exploration and exploitation. Comprehensive experiment results demonstrate better effectiveness of SCP-induced algorithms to the proposed synchronous reluctance machine magnetic model. Accuracy and robustness of the proposed algorithms are ranked first in the comparison result statistics with other well-known algorithms.
Linjie Ren; Guobin Lin; Yuanzhe Zhao; Zhiming Liao. Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model. Sustainability 2021, 13, 4379 .
AMA StyleLinjie Ren, Guobin Lin, Yuanzhe Zhao, Zhiming Liao. Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model. Sustainability. 2021; 13 (8):4379.
Chicago/Turabian StyleLinjie Ren; Guobin Lin; Yuanzhe Zhao; Zhiming Liao. 2021. "Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model." Sustainability 13, no. 8: 4379.
Affected by the magnetic saturation effect and unmodeled dynamics, the parameters of the synchronous reluctance motor (SynRM) are highly nonlinear and time-varying. The resulting unreasonable current loop reference command severely restricts the maximum efficiency and high control performance of SynRM. Therefore, an adaptive non-singular terminal sliding mode control scheme for SynRM drive system is proposed to improve the dynamic performance and robustness. Firstly, an analytical model of flux linkage and inductance that satisfies the energy conversion mechanism is proposed to estimate the required parameters of the control system in real time. Secondly, a novel fast finite time adaptive-gain reaching law is proposed to shorten the arrival time while reducing the chatter near the sliding mode surface. Then, a non-linear disturbance observer is designed to estimate the total disturbance of the system. The asymptotic stability of the system is proved by Lyapunov’s theorem. The experimental results demonstrate that the system has satisfactory dynamic performance and robustness.
Linjie Ren; Guobin Lin; Yuanzhe Zhao; Zhiming Liao; Fei Peng. Adaptive Nonsingular Finite-Time Terminal Sliding Mode Control for Synchronous Reluctance Motor. IEEE Access 2021, PP, 1 -1.
AMA StyleLinjie Ren, Guobin Lin, Yuanzhe Zhao, Zhiming Liao, Fei Peng. Adaptive Nonsingular Finite-Time Terminal Sliding Mode Control for Synchronous Reluctance Motor. IEEE Access. 2021; PP (99):1-1.
Chicago/Turabian StyleLinjie Ren; Guobin Lin; Yuanzhe Zhao; Zhiming Liao; Fei Peng. 2021. "Adaptive Nonsingular Finite-Time Terminal Sliding Mode Control for Synchronous Reluctance Motor." IEEE Access PP, no. 99: 1-1.
To reveal the penetration characteristics of high-frequency harmonics of the traction network to the three-phase 380 V power system in the traction substation (TSS), the harmonic equivalent circuit model and harmonic penetration mathematical model of the two-phase to three-phase Scott-T transformer are established. Through the power quality measurement and harmonic analysis, the results indicate that the high-frequency harmonics in the traction network will severely penetrate the three-phase 380 V power system, which will cause almost the same degree of harmonic distortion, verifying the correctness of the harmonic penetration model. To filter high-frequency harmonics in TSS, a novel structure of high-pass filter (HPF) is proposed that features with high-pass characteristic at high frequencies while high-impedance at fundamental frequency. Furthermore, a set of three-phase experimental device is developed, and a long-term filtering experiment in the TSS is performed. The experimental results show that the novel HPF experimental device can effectively filter out high-frequency harmonics of the three-phase 380 V power system, and have almost no loss and reactive power. The feasibility and effectiveness of the suppression scheme based on the novel HPF are verified.
Yuanzhe Zhao; Linjie Ren; Guobin Lin; Fei Peng. Research on the Harmonics Penetration Characteristics of the Traction Network to Three-phase 380 V Power System of the Traction Substation and Suppression Scheme. IEEE Access 2020, 8, 1 -1.
AMA StyleYuanzhe Zhao, Linjie Ren, Guobin Lin, Fei Peng. Research on the Harmonics Penetration Characteristics of the Traction Network to Three-phase 380 V Power System of the Traction Substation and Suppression Scheme. IEEE Access. 2020; 8 ():1-1.
Chicago/Turabian StyleYuanzhe Zhao; Linjie Ren; Guobin Lin; Fei Peng. 2020. "Research on the Harmonics Penetration Characteristics of the Traction Network to Three-phase 380 V Power System of the Traction Substation and Suppression Scheme." IEEE Access 8, no. : 1-1.
Modern low-speed maglev trains typically use multi-node decentralized levitation control modules, which results in a complex levitation control system with coupling interaction. Conducting systematic levitation condition awareness of the levitation control system is still a promising challenge. In this paper, under the hypothesis of levitation residuals following normal distribution, a levitation condition awareness architecture for the levitation control system is proposed based on data-driven random matrix analysis. The proposed architecture consists of an engineering procedure followed by a cascaded mathematical procedure. In the decentralized engineering procedure, the data-driven modeling for individual levitation control modules is achieved by nonlinear autoregressive modeling with an exogenous input neural network, and the unknown parameters are identified by a modified combinatorial genetic algorithm. On this basis, high-dimensional analysis of streaming residual random matrices for the levitation control system is conducted aided by large-dimensional random matrix theory, and the control limits of the constructed indicators are well-designed using the theorical distributions. Based on the comparative analysis of the experimental datasets, the proposed awareness architecture is verified to show the effectiveness of the systematic condition evaluation of the levitation system, and incipient train-guideway interaction vibration abnormalities can be detected in a timely manner.
Yuanzhe Zhao; Fei Peng; Linjie Ren; Guobin Lin; Junqi Xu. A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis. IEEE Access 2020, 8, 176575 -176587.
AMA StyleYuanzhe Zhao, Fei Peng, Linjie Ren, Guobin Lin, Junqi Xu. A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis. IEEE Access. 2020; 8 (99):176575-176587.
Chicago/Turabian StyleYuanzhe Zhao; Fei Peng; Linjie Ren; Guobin Lin; Junqi Xu. 2020. "A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis." IEEE Access 8, no. 99: 176575-176587.