Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK.
State Grid Sichuan Electric Power Research Institute of China, Chengdu 610094, China.
Sensors (Basel). 2022 Jan 4;22(1):362. doi: 10.3390/s22010362.
Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions. In addition, very little attention has been paid to the importance of developing fast and accurate techniques for the real-life application of open-circuit failures of IGBT fault diagnosis. To achieve high classification accuracy and reduced computation time, a fault diagnosis framework with a combination of the AC-side three-phase current, and the upper and lower bridges' currents of the MMCs to automatically classify health conditions of MMCs is proposed. In this framework, the principal component analysis (PCA) is used for feature extraction. Then, two classification algorithms-multiclass support vector machine (SVM) based on error-correcting output codes (ECOC) and multinomial logistic regression (MLR)-are used for classification. The effectiveness of the proposed framework is validated by a two-terminal simulation model of the MMC-high-voltage direct current (HVDC) transmission power system using PSCAD/EMTDC software. The simulation results demonstrate that the proposed framework is highly effective in diagnosing the health conditions of MMCs compared to recently published results.
在模块化多电平换流器（MMC）中，绝缘栅双极晶体管（IGBT）开路故障模式是最常见的故障之一。已经提出了基于阈值参数的几种用于 MMC 故障诊断的技术，但很少有研究考虑人工智能（AI）技术。使用阈值存在为不同的工作条件选择合适的阈值的困难。此外，很少有人关注为 IGBT 开路故障诊断的实际应用开发快速准确的技术的重要性。为了实现高分类精度和减少计算时间，提出了一种故障诊断框架，该框架结合了 MMC 的交流侧三相电流以及上下桥臂的电流，以自动分类 MMC 的健康状况。在该框架中，使用主成分分析（PCA）进行特征提取。然后，使用两种分类算法 - 基于纠错输出码（ECOC）的多类支持向量机（SVM）和多项逻辑回归（MLR） - 进行分类。使用 PSCAD/EMTDC 软件的 MMC-高压直流（HVDC）输电系统的两端仿真模型验证了所提出框架的有效性。仿真结果表明，与最近公布的结果相比，所提出的框架在诊断 MMC 的健康状况方面非常有效。
