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Sensor Fault Diagnosis and Unknown Disturbances Estimation of High Switching Frequency Single-phase PWM Rectifier

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  • Control Theory and Applications
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Abstract

The fault and disturbances estimation has an important role in the modern traction railway system. This paper proposes a unique method for the real-time robust sensor fault detection and unknown bounded disturbances estimation of high switching frequency single-phase pulse width modulation (PWM) rectifier. The new state observer is designed for open-circuit sensors fault for single-phase PWM rectifier. The digital realization method is used to minimize the observer fluctuation in presence of fault with and without external disturbance. The fault diagnosis approach constitutes on threshold establishment with respect to residual generation based on observer and estimator. The impact of uncertainties is reduced and the convergence speed of fault estimation and accuracy is improved by H∞ performance level. The extensive simulations are implemented to validate the effectiveness of the proposed algorithm. From the presented results, the performance of the proposed method is illustrated along with the fault isolation and disturbance estimator.

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Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, P. R. China

    Habib Ullah Khan Jadoon, Deqing Huang, Na Qin & Zifeng Gong

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  1. Habib Ullah Khan Jadoon
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  3. Na Qin
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Correspondence to Na Qin.

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The work was partially supported by the National Natural Science Foundation of China under Grants 61773323, U1934221, 61733015, the Fundamental Research Fund for the Central Universities 2682018CX15, and the Sichuan Science and Technology Program under Grants 2019YFG0345, 2019YJ0210.

Habib Ullah Khan Jadoon received his B.S. degree in electronics engineering from COMSATS IIT Abbottabad, Pakistan, in 2009 and his M.S. degree in Electronics Design from Mid Sweden University, Sundsvall, Sweden in 2014. He is currently pursuing a Ph.D. degree from Southwest Jiaotong University, Chengdu, China. His research interests include fault detection and fault tolerant control,electric traction system, power electronic converters and real time simulation

Deqing Huang received his B.S. and Ph.D. degrees in applied mathematics from the Mathematical College, Sichuan University, Chengdu, China, in 2002 and 2007, respectively. He received the second Ph.D. degree in control engineering from National University of Singapore (NUS). From 2013 to 2016, he was a Research Associate with the Department of Aeronautics, Imperial College London, London, U.K. In January 2016, he joined the Department of Electronic and Information Enginerring, Southwest Jiaotong Univerisity, Chengdu, China as a Professor and the Department Head. His research interests lie in the areas of modern control theory, fluid analysis and control, convex optimization, and robotics.

Na Qin received her B.S. degree in electrical technology from the School of Electrical Engineering, Zhengzhou University, Zhengzhou, China, in 2000, and an M.S. degree in electric power system and automation and a Ph.D. degree in electrical engineering from the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China, in 2003 and 2014, respectively. She is currently an Associate Professor with the School of Electrical Engineering, Southwest Jiaotong University. Her research interests include intelligent information processing, fault diagnosis, pattern recognition, and intelligent systems.

Zifeng Gong received his B.Eng. degree in electrical engineering from Southwest Minzu University, Chengdu, China, 2018. He is currently working toward an M.S. degree in electrical engineering at Southwest Jiaotong University, Chengdu, China. His research interests include modeling, fault diagnosis, and fault tolerant control of electrical traction system.

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Jadoon, H.U.K., Huang, D., Qin, N. et al. Sensor Fault Diagnosis and Unknown Disturbances Estimation of High Switching Frequency Single-phase PWM Rectifier. Int. J. Control Autom. Syst. 19, 2769–2783 (2021). https://doi.org/10.1007/s12555-020-0334-8

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