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    张晓华

    • 教授     博士生导师   硕士生导师
    • 性别:男
    • 毕业院校:哈尔滨工业大学
    • 学位:博士
    • 所在单位:电气工程学院
    • 学科:电力电子与电力传动
    • 办公地点:大连市高新区凌工路 2 号电气楼 405 室
    • 联系方式:xh_zhang@dlut.edu.cn
    • 电子邮箱:xh_zhang@dlut.edu.cn

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    采用复合特征提取和SVM的三电平STATCOM故障诊断方法

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    发表时间:2022-10-09

    发表刊物:电机与控制学报

    卷号:23

    期号:2

    页面范围:53-61

    ISSN号:1007-449X

    摘要:Focusing on the issue of open-circuit fault diagnosis of power switches in the neutral point clamped(NPC) three-level STATCOM, this paper proposes a hybrid fault feature extraction technique. By analyzing the change law of output current after power switch open-circuit fault of the three-level STATACOM, the normalized average current feature extraction method was applied on the basis of wavelet packet energy spectrum feature extraction method. The normalized average values of the three-phase currents were calculated in one cycle. Fault signal indicators were extracted from both frequency domain and time domain, and then the fault feature vectors were constructed. Support vector machine(SVM) was adopted to classify the faults. The training samples were obtained by changing the load and then the SVM was trained by using grid search method. The proposed technique was verified by using Matlab offline simulations and dSPACE real-time simulations, respectively. The results show that comparing with the single wavelet packet energy spectrum feature extraction technique, the hybrid feature extraction technique increases the accuracy of fault diagnosis by 38.88% and 49.43%, respectively, and reduces the diagnostic time by 7.38% and 6.61%, respectively. ? 2019, Harbin University of Science and Technology Publication. All right reserved.

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