Kou Lei, Liu Chuang, Cai Guo-Wei, Zhang Zhe, Zhou Jia-Ning, Wang Xue-Mei
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China.
Department of Electrical Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
ISA Trans. 2020 Jun;101:399-407. doi: 10.1016/j.isatra.2020.01.023. Epub 2020 Jan 23.
Three-phase PWM rectifiers are adopted extensively in industry because of their excellent properties and potential advantages. However, while the IGBT has an open-circuit fault, the system does not crash suddenly, the performance will be reduced for instance voltages fluctuation and current harmonics. A fault diagnosis method based on deep feedforward network with transient synthetic features is proposed to reduce the dependence on the fault mathematical models in this paper, which mainly uses the transient phase current to train the deep feedforward network classifier. Firstly, the features of fault phase current are analyzed in this paper. Secondly, the historical fault data after feature synthesis is employed to train the deep feedforward network classifier, and the average fault diagnosis accuracy can reach 97.85% for transient synthetic fault data, the classifier trained by the transient synthetic features obtained more than 1% gain in performance compared with original transient features. Finally, the online fault diagnosis experiments show that the method can accurately locate the fault IGBTs, and the final diagnosis result is determined by multiple groups results, which has the ability to increase the accuracy and reliability of the diagnosis results.
三相 PWM 整流器因其优异的性能和潜在优势在工业中得到广泛应用。然而，当绝缘栅双极型晶体管（IGBT）出现开路故障时，系统不会突然崩溃，但性能会下降，例如电压波动和电流谐波。本文提出了一种基于具有瞬态合成特征的深度前馈网络的故障诊断方法，以减少对故障数学模型的依赖，该方法主要利用瞬态相电流来训练深度前馈网络分类器。首先，本文分析了故障相电流的特征。其次，利用特征合成后的历史故障数据训练深度前馈网络分类器，对于瞬态合成故障数据，平均故障诊断准确率可达 97.85%，与原始瞬态特征相比，由瞬态合成特征训练的分类器性能提升超过 1%。最后，在线故障诊断实验表明，该方法能够准确地定位故障 IGBT，最终诊断结果由多组结果确定，具有提高诊断结果准确性和可靠性的能力。
