| 引用本文: | 刘子幸,王子赟,纪志成.基于逆向卡尔曼滤波的电力变换器故障诊断方法[J].电力系统保护与控制,2019,47(9):19-26.[点击复制] |
| LIU Zixing,WANG Ziyun,JI Zhicheng.Inverse Kalman filtering based converter fault diagnosis method[J].Power System Protection and Control,2019,47(9):19-26[点击复制] |
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| 摘要: |
| 针对变换器中因电解电容退化而引起的等效电阻异变的故障诊断问题，提出了一种基于逆向卡尔曼滤波的电力变换器故障诊断方法。将变换器抽象为一类卡尔曼滤波动态方程。将电路元件参数作为卡尔曼滤波的未知状态，利用电路的电压和电流作为已知矩阵，逆向推导卡尔曼滤波递推公式，完成电力变换器的参数辨识和故障诊断。针对变换器正常状态下的参数辨识结果，表明所提出的逆向卡尔曼滤波参数辨识算法具有较高的精度。同时针对变换器故障状态下的故障诊断结果，表明逆向卡尔曼滤波算法也具有很好的跟踪性，能够快速显示故障元件及其参数变化情况。仿真验证了所提出方法的有效性和实用性。 |
| 关键词: 卡尔曼滤波 逆向卡尔曼 电力变换器 故障诊断 电解电容退化 |
| DOI：10.7667/PSPC180803 |
| 投稿时间：2018-07-04修订日期：2018-09-05 |
| 基金项目:国家自然科学基金项目资助(61802150);江苏省自然科学基金杰出青年项目资助(BK20160001);江苏省自然科学基金青年项目资助(BK20170196);中国博士后基金面上项目资助(2018M642161);江苏高校品牌专业建设工程项目资助(PPZY2015A036) |
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| Inverse Kalman filtering based converter fault diagnosis method |
| LIU Zixing,WANG Ziyun,JI Zhicheng |
| (College of Internet of Things, Jiangnan University, Wuxi 214122, China;Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;Engineering Research Center of Internet of Things Technology and Applications of Ministry of Education, Jiangnan University, Wuxi 214122, China;College of Internet of Things, Jiangnan University, Wuxi 214122, China;Engineering Research Center of Internet of Things Technology and Applications of Ministry of Education, Jiangnan University, Wuxi 214122, China) |
| Abstract: |
| To solve the fault diagnosis problem of the equivalent resistance change due to the degradation of electrolytic capacitor in the circuit, a fault diagnosis method based on inverse Kalman filtering is proposed. The converter is abstracted as a dynamic equation of Kalman filtering. It takes the parameters of circuit elements as unknown state of the Kalman filtering and uses the voltage and current of the circuit as the known matrix to inversely derive the recursion formula of Kalman filtering for parameter identification and fault diagnosis of converter. The parameter identification results for normal state of the converter show that the proposed inverse Kalman filtering parameter identification algorithm has higher accuracy. The fault diagnosis results for the circuit fault state show that the inverse Kalman filtering algorithm has good tracking ability, and it can detect faulty elements and estimate their parameters quickly. The simulations validate the effectiveness and practicality of the proposed method. This work is supported by National Natural Science Foundation of China (No. 61802150), Excellent Youth Foundation of Jiangsu Scientific Committee (No. BK20160001), Natural Science Foundation of Jiangsu Province (No. BK20170196), and Brand Specialty Construction Support Project of Jiangsu Province (No. PPZY2015A036). |
| Key words: Kalman filtering inverse Kalman filtering converter fault diagnosis degradation of electrolytic capacitor |
