A High-Accuracy-Light-AI Data-Driven Diagnosis Method for Open-Circuit Faults in Single-Phase PWM Rectifiers
Qingli Deng, Bin Gou, Xinglai Ge, Chunxu Lin, Dong Xie, Xiaoyun Feng
Abstract
Data-driven solutions, such as artificial intelligence (AI), are more upright and effectual for fault diagnosis in the power converter system. To decrease computing load and improve the diagnosis speed of the intelligent algorithm, a high-accuracy-light-AI data-driven open-circuit diagnosis method is proposed to diagnose single or multiple insulated-gate bipolar transistor (IGBT) open-circuit faults in the single-phase pulsewidth modulation (PWM) rectifiers. First, the fault characteristics of three fault scenarios are analyzed to seek the most critical features and provide prior knowledge for developing the intelligent diagnostic algorithm. Then, a fast-learning algorithm named random vector function link (RVFL) network is utilized to extract the mapping knowledge between the well-selected features and fault modes. To balance the testing accuracy and calculation time of the diagnostic algorithm, the RVFL parameters are tuned to decrease the computing burden. After that, a decision-making framework is designed for identifying the faults in real time. Finally, the effectiveness of the proposed fault diagnosis method is thoroughly verified by extensive experimental tests. A substantial comparison to state-of-the-art techniques shows the proposed scheme’s superiority in diagnostic accuracy, computing time, and diagnosis time.