This page has only limited features, please log in for full access.

Unclaimed
Yuanzhe Zhao
Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai, China

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 16 July 2021 in IEEE Access
Reads 0
Downloads 0

For mechanism analysis and high-performance control of synchronous reluctance machine (SynRM), accurate and reliable parameter identification of nonlinear magnetic model is always required. However, the accuracy and robustness of traditional heuristic algorithms are restricted by incomplete individual performance evaluation and single population evolution mechanism. In this paper, we propose a self-adaptive synergistic optimization (SSO) algorithm for extracting the parameters of the model. A novel synergistic-performance evaluation is first established to classify candidates automatically. Then, a self-organized mechanism is proposed to select optimal evolution strategies designed for classified candidate solutions. Around the current best candidate, the exploration is guaranteed in priority. Meanwhile, a self-adaptive mechanism is introduced to select other candidates to construct more promising evolutionary direction. Thus, achieving a good balance between exploration and exploitation. The parameter estimation performance of SSO algorithm is evaluated through standard datasets of SynRM magnetic model obtained by the finite element analysis. Comprehensive experiment results demonstrate the competitiveness and effectiveness of the proposed SSO algorithm compared with other algorithms, especially in terms of the accuracy and robustness. According to these superiorities, it can be concluded that the proposed algorithms are promising parameter identification methods for SynRM nonlinear magnetic model.

ACS Style

Yuanzhe Zhao; Linjie Ren; Guobin Lin; Zhiming Liao; Siming Liu. Self-Adaptive Synergistic Optimization for Parameters Extraction of Synchronous Reluctance Machine Nonlinear Magnetic Model. IEEE Access 2021, 9, 101741 -101754.

AMA Style

Yuanzhe Zhao, Linjie Ren, Guobin Lin, Zhiming Liao, Siming Liu. Self-Adaptive Synergistic Optimization for Parameters Extraction of Synchronous Reluctance Machine Nonlinear Magnetic Model. IEEE Access. 2021; 9 ():101741-101754.

Chicago/Turabian Style

Yuanzhe Zhao; Linjie Ren; Guobin Lin; Zhiming Liao; Siming Liu. 2021. "Self-Adaptive Synergistic Optimization for Parameters Extraction of Synchronous Reluctance Machine Nonlinear Magnetic Model." IEEE Access 9, no. : 101741-101754.

Journal article
Published: 17 April 2021 in Energies
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (8):2256.

Chicago/Turabian Style

Yuanzhe 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.

Journal article
Published: 14 April 2021 in Sustainability
Reads 0
Downloads 0

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.

ACS Style

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 Style

Linjie 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 Style

Linjie 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.

Journal article
Published: 26 October 2020 in IEEE Access
Reads 0
Downloads 0

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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Yuanzhe 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.

Journal article
Published: 22 September 2020 in IEEE Access
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (99):176575-176587.

Chicago/Turabian Style

Yuanzhe 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.

Journal article
Published: 19 November 2018 in Transportation Systems and Technology
Reads 0
Downloads 0

Aim: This paper proposes constant switching frequency model predictive control (CSF-MPC) for a permanent magnet linear synchronous motor (PMLSM) to improve the steady state and dynamic performance of the drive system. Methods: The conventional finite control set model predictive control (FCS-MPC) can be combined with a pulse width modulation (PWM) modulator due to an effective cost function optimization algorithm which is from the idea of dichotomy. In the algorithm, all the voltage vectors in the constrained vector plane are dynamically selected and calculated through iteration. The whole system including control algorithm and mathematical model of PMLSM is built and tested by simulation using MATLAB/Simulink. Besides, the control algorithm is tested in the FPGA controller through FPGA-in-the-Loop test. Results: With the modern digital processors or control hardware such as digital signal processors (DSPs) or field programmable gate arrays (FPGAs), the algorithm can be easily executed in less than 10-micro second. This is very proper for industrial applications. The proposed control algorithm is implemented on FPGA and tested by FPGA-in-the-Loop method. The proposed control algorithm can improve the performance of drive system greatly. Conclusion: The proposed CSF-MPC for PMLSM not only keeps the same dynamic transient performance as FCS-MPC but also greatly decreases the torque ripple in steady state. Furthermore, CSF-MPC is also robust to parameter variations. Simulation and FPGA-in-the-Loop results illustrate that CSF-MPC has an attractive performance for PMLSM drives.

ACS Style

Zhixun Ma; Yuanzhe Zhao; Yan Sun; Zhiming Liao; Guobin Lin. Constant switching frequency model predictive control for permanent magnet linear synchronous motor. Transportation Systems and Technology 2018, 4, 279 -288.

AMA Style

Zhixun Ma, Yuanzhe Zhao, Yan Sun, Zhiming Liao, Guobin Lin. Constant switching frequency model predictive control for permanent magnet linear synchronous motor. Transportation Systems and Technology. 2018; 4 (3):279-288.

Chicago/Turabian Style

Zhixun Ma; Yuanzhe Zhao; Yan Sun; Zhiming Liao; Guobin Lin. 2018. "Constant switching frequency model predictive control for permanent magnet linear synchronous motor." Transportation Systems and Technology 4, no. 3: 279-288.