FPGA‐based hardware‐in‐the‐loop real‐time simulation implementation for high‐speed train electrical traction system

Guo, Xiaojun; Tang, Yiguo; Wu, Mingkang; Zhang, Ziyu; Yuan, Jiaqi

doi:10.1049/iet-epa.2019.0655

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…The fault types of three-phase AC asynchronous squirrel-cage motors in industrial applications mainly include stator insulation faults (37%), rotor broken bar faults (12%), bearing faults (41%), and other faults (10%) [4]. The high voltage stress generated by the inverter PWM (pulse width modulation) voltage accelerates the degradation of the traction motor insulation system [5][6][7][8]. The inter-turn insulation is the weakest part of the asynchronous traction motor insulation system [9].…”

Section: Introductionmentioning

confidence: 99%

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM

Liu

et al. 2023

Energies

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EMU (electric multiple unit) traction motors are powered by converters whose output voltage increases the voltage stress borne by the insulation system, making the ITSC (inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power is proposed in the article to evaluate the non-metallic ITSC faults extent. The apFFT (all-phase FFT) time-shift phase difference correction with double Hanning windows is used to calculate fault features to train the SVM (support vector machine) fault diagnosis model whose hyper-parameters C and g are optimized using grid search methods. The experimental verification was carried out on the EMU electric traction simulation experimental platform. According to the fault extent index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training dataset and 93.33% on the test dataset. There was no misclassification between normal and serious ITSC fault samples.

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Section: Introductionmentioning

confidence: 99%

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM

Liu

et al. 2023

Energies

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“…The insulation system is the "heart" of the traction motor. The traction motor is powered by a traction converter, and the inverter generally uses SVPWM (space vector pulse width modulation) and square wave power supply to the traction motor for different control stages [3,4]. The stator ITSC fault of the induction motor accounts for 37% in industry application, and it is more destructive than rotor bar breakage, air gap eccentricity, and bearing faults [5,6].…”

Section: Introductionmentioning

confidence: 99%

Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

Wang

et al. 2023

Energies

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The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%.

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“…For example in [3] and [4] HIL platforms are employed to study the effects of traction converters in the network. In [5] a FPGA-based HIL platform is presented and used to simulate a power traction system. Other studies like [6] and [7] focus on improving fault detectability and diagnosis, especially in converters, using HIL simulations.…”

mentioning

confidence: 99%

Hardware-in-the-Loop Platform for Virtual Certification of Traction Systems for Railway

Herrero,

Mendez,

Caballo

et al. 2024

IEEE Access

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The railway industry is continuously evolving, necessitating the development of more intricate and dependable software algorithms. To meet these requirements, the creation of new validation tools has become increasingly crucial, with a specific focus on Hardware-In-the-Loop (HIL) simulations. HIL simulations are invaluable during the product development process as they improve system resilience, performance, and overall product value. This study details the design of a HIL platform that can validate the software created for the Traction Control Unit (TCU) of a complex traction system. By producing real interfaces in real-time, this platform offers a practical simulation environment for examining the TCU software. The HIL platform built in this research provides comprehensive test coverage of the TCU software, a feat that is often unattainable through on-train testing. Moreover, the platform provides greater tuning and debugging capabilities, allowing for a virtual certification of the TCU software, which can offer greater security and assurances of success when encountering official tests. Thus, this paper demonstrates how profitable a HIL platform that models the whole traction system can be in order to debug and certificate its TCU software.

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FPGA‐based hardware‐in‐the‐loop real‐time simulation implementation for high‐speed train electrical traction system

Cited by 11 publications

References 25 publications

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM

Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

Hardware-in-the-Loop Platform for Virtual Certification of Traction Systems for Railway

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