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Jiacai Huang,Lei Cui,Xinxin Shi 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.10
A direct torque control method based on fractional order sliding mode variable structure (DTC-FOSMVS) was proposed for the speed control of a permanent magnet synchronous motor (PMSM). The proposed method in which the space vector pulse width modulation (SVPWM) with fixed switch frequency is applied, reduces the torque and flux ripple, and improves the speed control performance. In order to improve the energy efficiency and reduce the demagnetization effect, the DTC-FOSMVS method with dynamic flux reference was designed. The stability of the control method is proved by Lyapunov theory. The performance of the proposed method is verified through experiment which is based on hardware in loop and Simulink/QuaRC real time control software. The experiment results show the robustness and effectiveness of the proposed method.
Cross‑domain health status assessment of three‑phase inverters using improved DANN
Quan Sun,Fei Peng,Hongsheng Li,Jiacai Huang,Guodong Sun 전력전자학회 2023 JOURNAL OF POWER ELECTRONICS Vol.23 No.9
Information and large number of fault labels are required to achieve intelligent health status assessment of three-phase inverters. However, the current signals of inverters cannot be sufficiently collected since open-circuit faults (OCFs) occur briefly, which makes it difficult to determine the OCF mode of the various power switches. A transfer learning model that effectively uses a small amount of sample data to achieve domain adaptation is proposed to address this problem. First, collected fault-sensitive signals are subjected to a continuous wavelet transform (CWT) to obtain two-dimensional image data with more abundant fault feature information. Second, the source domain and target domain features are projected into the same feature space through a domain adversarial neural network (DANN) to achieve multi-domain feature extraction and adaptation. Then, in the feature extraction module of the DANN, the deep residual network (Resnet) structure is used to replace the typical convolutional neural network (CNN) structure. Finally, an intelligent diagnosis network is used to identify the health status of the inverter samples under variable conditions. Experimental results show that the proposed model can accurately and effectively realize the cross-domain health assessment of three-phase inverters in the case of small samples. The accuracy of the proposed model is better than that of other classical transfer learning models.