Designing state perception and fault diagnosis platforms for traction converters based on scenario reproduction

The development of different fields, such as renewable energy generation, high-voltage direct current transmission, and high-power alternating current drive systems, has increased the demand for converters. However, current course teaching rarely involves the principles of novel circuits and the reliability of converters. As the core component of the traction drive system for electric locomotives, the state perception and fault diagnosis of traction converters are crucial for the safe operation of electric locomotives. Therefore, this study analyzes the electric locomotive traction converter to construct a hardware-in-the-loop (HIL) simulation platform for state perception and fault diagnosis based on scenario reproduction, aiming to enhance students’ ability to connect theory with practice.

This platform has two main components: a real-time scenario-based traction drive simulation system and an online state perception and fault diagnosis system. The circuit module of the traction drive simulation system is built in the RT-LAB (OP5600) simulator and its host computer, which is responsible for simulating the hardware circuit topology of real-world scenarios. The control module of the traction drive simulation system uses the dSPACE controller to implement the control strategies of the traction drive model. The dSPACE controller comprises the MicroLabBox simulator and its host computer. The online state perception and fault diagnosis system was developed using the OMAP-L138 (DSP) hardware development environment, which compiles data acquisition and online diagnostic algorithms. The traction drive simulation system reproduces scenarios based on the real circuit topology model and outputs signals to the fault diagnosis system, which further receives these signals, completes state perception and fault localization, and sends protection strategies back to the traction drive simulation system. The platform effectiveness was validated by stimulating the converter control of the traction drive system and fault tracing of the inverter output overcurrent faults. The specific methods are as follows: ① the main circuit topology of the traction drive system is built in RT-LAB, which sends the signals of the motor speed, two-phase stator current, and DC-link voltage to the dSPACE controller and the DSP state perception and fault diagnosis system. ② The dSPACE controller simulates the direct torque control strategy and sends switching signals to RT-LAB, forming a closed-loop control system. ③ The DSP diagnostic system analyzes the statistical features of the DC-link voltage, motor speed, two-phase stator current, and the operating conditions; triggers event states to match fault types; and outputs corrective signals to the dSPACE controller.

Experiments were conducted for three scenarios: normal operating conditions, speed sensor faults, and traction motor faults. The experimental results demonstrate that the platform exhibits effectiveness in circuit model simulation and control strategy implementation and can reproduce various fault scenarios and perform online fault diagnosis.

This study introduces a HIL simulation platform comprising a monitoring host, dual real-time simulators (i.e., RT-LAB and dSPACE), and a DSP online diagnosis system. The concept of modular design is applied across all components of the platform, ensuring adaptability and scalability for various experiments and applications. The effectiveness and feasibility of the proposed platform were verified by simulating the converter control of the traction drive system and fault tracing of the inverter output overcurrent faults. The application of this platform will improve students’ engineering practice skills.