Fault Diagnosis and Tolerance Control of Five-Level Nested NPP Converter Using Wavelet Packet and LSTM

The five-level nested neutral-point-pilot (NPP) topology, as a new structure for converters, bears the advantages of a high power density, robustness, and flexibility and is therefore suitable for high-voltage and high-power applications. For a multilevel converter, as the number of power electronic switches increases, the risk of switch failure increases, together with the complexity of fault detection and tolerance control. The requirements for a higher operational stability and reliability continue to grow. However, studies on fault tolerance for multilevel converters are limited. In this paper, a fault diagnosis and tolerance solution for a fivelevel nested NPP converter is proposed. For the fault diagnosis, a deep learning method integrating the wavelet packet transform and long short-term memory is presented. Both open- and shortcircuit switch failures can be precisely detected and located without the requirement of a large sample set. Two software-based control strategies for fault tolerance are adopted, and low-cost hardware reconfigurations are also implemented to prevent failure expansion and ensure continuous operation. Furthermore, the voltages of dclink capacitors and flying capacitors are effectively balanced with the improved algorithm even when a failure occurs. Finally, the effectiveness of the fault-tolerant strategy are proven by simulations and experiments.