Fault Diagnosis of Inverter Power Switches Using Signal-Data Based Machine Learning
Authors:   , Ibem Chukwuemeka Nzekwu 
Published: December 2024

Voltage Source Inverters (VSIs) are pivotal in modern power electronic systems, supporting applications such as adjustable speed drives, power conditioning, and renewable energy integration. They facilitate the conversion of direct current (DC) into alternating current (AC) and ensure consistent output voltage despite load fluctuations. However, the intricate nature and demanding operational conditions of VSIs make them prone to faults, particularly open-circuit and short-circuit faults. Insulated Gate Bipolar Transistors (IGBTs) are especially vulnerable, and their failure can significantly impair system performance and safety. Thus, developing robust and efficient fault diagnosis techniques is deemed critical and essential for whole system robustness.

This thesis focuses on advanced fault diagnosis techniques for power electronic inverters, emphasizing voltage source inverters and multiphase inverter (Nine Switch Inverter (NSI)) topologies. The research aims to address the limitations of existing fault diagnosis methods, which include high false alarm rates, complexity, lack of robustness, and the need for additional hardware. Traditional methods such as model-based, data-based, and signal-based techniques have unique challenges, including dependency on system models, substantial data and computational resource requirements, and difficulties in maintaining robustness across varying operational conditions.

To overcome these challenges, this research proposes new fault diagnosis techniques integrating advanced machine learning models, particularly ensemble bagged tree classifiers, with signal-data based approaches focusing on average and root mean square (RMS) current analyses. By analyzing the three-phase current waveform of the inverter and extracting the average and RMS values, the research develops a robust fault diagnosis method superior to using these parameters individually.

A new normalization method utilizing the average-to-RMS ratio (ARR) is introduced to normalize the output current, addressing the drawbacks of current-based analysis in time-domain analysis, especially at low current levels. This innovative normalization technique ensures reliable fault detection by adjusting current measurements to account for low current conditions, enhancing the sensitivity of the diagnostic system and allowing fault detection even in minimal current scenarios.

The research develops Fault Diagnosis Techniques (FDTs) using the ARR and ensemble bagging for two-level inverter systems under various fault conditions, including open switch faults and gate driver malfunctions. For the two-level inverter system, the FDT can identify single and multiple open switch faults, with the capacity to classify 25 distinct fault scenarios. For gate driver malfunctions, the FDT can identify single and double open switch faults, with the capacity to classify 19 distinct fault scenarios. Additionally, a fault diagnosis technique was developed for the NSI under gate driver malfunctions, capable of identifying single faults with a classification capacity of 10 distinct fault scenarios.

The diagnosis methods demonstrated resilience to false alarms through switching frequency variation and load-changing analysis, achieving a detection time of approximately one-quarter of the fundamental period. The simplicity of the fault diagnosis technique eliminates the need for extra sensors, facilitating ease of implementation and minimizing manufacturing costs.

The proposed fault diagnosis techniques were validated through experiments and simulations. The results confirmed the robustness and effectiveness of the techniques in accurately diagnosing all possible fault scenarios, significantly contributing to the advancement of fault-tolerant inverter technology and enhancing the reliability and efficiency of power electronic systems across various high-power applications.