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Signal Processing and Robust Statistics for Fault Detection in Photovoltaic Arrays.

机译:用于光伏阵列故障检测的信号处理和鲁棒统计。

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摘要

Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays. Among several types of detection algorithms considered, only the MCD shows high performance on both types of faults.
机译:光伏(PV)是重要且迅速发展的研究领域。随着电力系统监视和通信技术(统称为“智能电网”)的出现,存在将信号处理技术应用于光伏阵列监视和控制的机会。在本文中,考虑了一个监视系统,该系统可以实时测量每个PV模块的电压和电流。描述了一种故障检测算法,该算法被公式化为聚类问题,并使用鲁棒最小协方差决定因素(MCD)估计器解决;评估了其在电弧和接地故障的模拟实例上的性能。发现该算法对PV阵列中常见的许多类型的故障表现良好。在考虑的几种类型的检测算法中,只有MCD在两种类型的故障上均表现出高性能。

著录项

  • 作者

    Braun, Henry.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Alternative Energy.;Engineering Electronics and Electrical.;Engineering General.;Statistics.
  • 学位 M.S.
  • 年度 2012
  • 页码 73 p.
  • 总页数 73
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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