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Robust functional analysis for fault detection in power transmission lines

机译：输电线路故障检测的鲁棒功能分析

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摘要

Fault detection methods in power transmission lines aim to detect deviations of the electrical signals from the expected behavior of such signals under normal operating conditions. One approach is to model, as accurately as possible, the expected behavior of the electrical signals under normal operating conditions. Furthermore, even under normal conditions, electrical signals are subject to random noises. Therefore, upper and lower limits must be established. The larger the limits, the harder the fault detection. On the contrary, the narrower the limits the more likely to detect false faults. Functional analysis of power transmission lines was originally proposed to represent the behavior of the electrical signals and to estimate the upper and lower limits under normal operating conditions. Nonetheless, the originally proposed estimates are biased and rely on statistical assumptions that do not hold in practice. This work proposes new methods to estimate the parameters of the functional model and new upper and lower limits that do not rely on specific statistical assumptions. Simulated and real case results show that the proposed robust functional analysis reduces bias and provides more accurate false fault detection rates, as compared to the previous method.
机译：输电线中的故障检测方法旨在检测电信号与正常工作条件下此类信号的预期行为的偏差。一种方法是在正常操作条件下尽可能准确地建模电信号的预期行为。此外，即使在正常条件下，电信号也会受到随机噪声的影响。因此，必须确定上限和下限。限制越大，故障检测就越困难。相反，限制越窄，检测到错误故障的可能性就越大。最初建议对输电线路进行功能分析，以表示电信号的行为并估算正常运行条件下的上限和下限。然而，最初提出的估计数是有偏见的，并依赖于实践中不成立的统计假设。这项工作提出了新方法来估计功能模型的参数以及不依赖特定统计假设的新上限和下限。仿真和实际案例结果表明，与以前的方法相比，所提出的鲁棒功能分析减少了偏差并提供了更准确的错误检测率。

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