Open-Circuit Fault Detection Strategy in Grid-Tied NPC Inverters Using Distorted Current and Model Predictive Control

Abstract

Investigating and addressing fault detection is crucial for advancing the reliability, performance, and cost-effectiveness of grid-connected inverter systems, thereby contributing to the stability and efficiency of modern power grids. This study introduces a novel approach for detecting and classifying open-circuit faults (OCFs) in three-level neutral point clamped (3-L-NPC) inverters connected to the grid. The proposed algorithm swiftly identifies faulty switches and clamping diodes using distorted current signals and model predictive control (MPC), eliminating the need for additional hardware or complex computations. By addressing the challenge of identifying the specific switch under grid-connected conditions, the proposed method achieves faster detection and identification of all switches and clamping diodes in less than one fundamental period which is very good compared with recent studies and considering that no extra sensors are used. Furthermore, this work demonstrates the efficacy of MPC in tolerating OCFs in clamping diodes, showcasing its potential to enhance system resilience and performance. The proposed strategy significantly improves the reliability of 3-L-NPC inverters by ensuring prompt and accurate fault detection and classification. Both experimental and simulation results confirm the efficacy of the suggested fault detection and identification approach, emphasizing its practical applicability in real-world grid-tied inverter systems.