基于瞬时幅值的光伏系统电流传感器微小故障检测及估计
作者:
作者单位:

1.合肥工业大学;2.重庆大学;3.重庆交通大学

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通讯作者:

中图分类号:

TP206.3

基金项目:

国家自然科学基金项目(61803140, 61633005),中国博士后科学基金面上项目(2020M682474),中央高校基本科研项目(JZ2019HGTB0090),牵引动力国家重点实验室开放课题(TPL1908),输配电装备及系统安全与新技术国家重点实验室开放课题(2007DA105127).


Incipient Fault Diagnosis and Estimation for Current Senors of PV system Based on Instantaneous Amplitude
Author:
Affiliation:

1.Hefei University of Technology;2.Chongiqng University;3.Chongiqng Jiaotong University

Fund Project:

National Natural Science Foundation of China (61803140,61673076), China Postdoctoral Science Foundation(2020M682474), Fundamental Research Funds for the Central Universities (JZ2019HGTB0090), Opening Foundation of State Key Laboratory of Traction Power (TPL1908),Visiting Scholarship of State Key Laboratory of Power Transmission Equipment and System Security and New Technology(Chongqing University) (2007DA105127).

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    摘要:

    电流传感器是光伏系统中用于系统控制和状态监测的重要元件,然而受运行环境影响,电流传感器易 出现性能退化,影响系统运行安全。为了准确检测和估计出电流传感器微小故障,本文提出了基于瞬时幅值 的传感器微小故障检测和估计方法。首先,建立了基于瞬时幅值的电流传感器微小故障模型,利用Hilbert变 换(HT)估计相电流瞬时幅值,将测量的三相正弦电流转换为相互独立的三维直流信号分量。其次,利用快速 移动窗主成分分析(FWMPCA)对三维直流信号组成的数据矩阵进行特征提取,获得主元和残差子空间向量的 概率密度分布函数。然后,利用Kullback-Leibler(KL)距离定量度量实际运行数据相对于无故障运行数据的微 小变化。在此基础上,设置故障检测阈值,构建故障幅值估计模型,实现电流传感器微小故障检测和估计。最 后,利用RT-LAB实验平台验证了本文所提方法的有效性。

    Abstract:

    Current sensors are the important component for system control and state monitoring in PV systems. However, due to the influence of operation environment, the current sensor performance degradation often occurs, which a ects the operation safety of system. To detect and estimate the current sensor incipient faults, a novel incipient fault detection and estimation method based on instantaneous amplitude is proposed. First, the incipient fault model of current sensor based on instantaneous amplitude is established, the Hilbert transform (HT) algorithm is utilized to estimate instantaneous amplitudes of the three-phase currents, which makes the sinusoidal current signal transformed into three-dimensional direct current signal. Then the fast moving window principle component analysis (FMWPCA) is used to extract the features of the data matrix composed by three-dimensional direct current signal, and the probability density distribution functions of principal component and residual subspace vector are obtained. Subsequently, the Kullback-Leibler (KL) divergence is used to quantitatively measure the small change of actual operation data distribution relative to fault free operation data. Based on above, the fault detection threshold is set and the theoretical model of fault amplitude estimation is constructed to realize the incipient fault detection and estimation of current sensors. Finally, the e ectiveness of the proposed method is verified by RT-LAB platform.

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历史
  • 收稿日期:2020-09-10
  • 最后修改日期:2020-12-23
  • 录用日期:2021-01-11
  • 在线发布日期: 2021-02-04
  • 出版日期: