电器与能效管理技术 ›› 2023, Vol. 0 ›› Issue (10): 61-69.doi: 10.16628/j.cnki.2095-8188.2023.10.010

• 检测与试验 • 上一篇    下一篇

基于改进奇异值分解去噪算法的光伏系统串联电弧故障检测方法

陈欣凯, 鲍光海   

  1. 福州大学 电气工程与自动化学院, 福建 福州 350108
  • 收稿日期:2023-04-05 出版日期:2023-10-30 发布日期:2023-11-23
  • 作者简介:陈欣凯(1998—),男,硕士研究生,研究方向为电器及其系统智能化与故障诊断。|鲍光海(1977—),男,教授,研究方向为电器及其系统智能化与故障诊断。
  • 基金资助:
    福建省科技计划资助项目(2023H0007)

Series Arc Fault Detection Method for Photovoltaic System Based on Improved Singular Value Decomposition Denoising Algorithm

CHEN Xinkai, BAO Guanghai   

  1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
  • Received:2023-04-05 Online:2023-10-30 Published:2023-11-23

摘要:

针对构建Hankel矩阵的奇异值分解去噪算法运算速度慢的问题,提出改进的奇异值分解去噪算法。所提算法增加Hankel矩阵行与行之间数据点的延迟步长以减小轨迹矩阵加快运算速度,并结合奇异值能量差分谱法和奇异值能量均值法获取奇异值有效阶次以保证良好去噪效果。在此基础上,提出基于改进奇异值分解去噪算法的光伏系统串联电弧故障检测方法。首先,利用改进奇异值分解去噪算法去除噪声;其次,利用FFT和小波变换对信号进行分析,选取谐波幅值之和、细节系数d2d3的能量作为特征量。最后,通过线性支持向量机(LSVM)实现电弧检测。多次实验表明所提方法准确率高。

关键词: 光伏系统, 电弧故障, 改进的奇异值分解, 特征量, 支持向量机

Abstract:

Aiming at the problem that the calculation speed of the singular value decomposition denoising algorithm for constructing Hankel matrix is slow, an improved singular value decomposition denoising algorithm is proposed. The algorithm increases the delay step of data points between rows of Hankel matrix to reduce the attractor track matrix andaccelerate the calculation speed. Besides, the effective order of singular values is obtained by combining the difference spectrum method of singular value energy and the mean value method of singular value energy to ensure good denoising effect. On this basis, a series arc fault detection method of photovoltaic system based on improved singular value decomposition denoising algorithm is proposed. Firstly, the improved singular value decomposition denoising algorithm is used to remove the noise. Secondly, the FFT and wavelet transform are used to analyze the signal. The sum of harmonic amplitude values and the energy of wavelet detail coefficients d2 and d3 are selected as the feature vectors. Finally, the linear support vector machine (LSVM) is used to realize arc detection. The experimental results show that the propsed method has high accuracy.

Key words: photovoltaic system, arc fault, improved singular value decomposition, feature vector, support vector machine (SVM)

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