发表机构
Georgia Institute of Technology; King Fahd University of Petroleum and Minerals（佐治亚理工学院; 法赫德国王石油与矿产大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文在IEEE 9节点系统上比较采样值域与相量域表示对流式故障分类的影响，发现采样值表示（尤其40样本MLP）能更早正确分类且准确率高，而一个周期的相量表示延迟无补偿优势。
AI 中文摘要
输电线路故障分类是一项时间关键的继电保护功能，近年来的机器学习方法直接从电压和电流测量值中分类故障类型。此类分类器的输入可以是原始测量的采样值窗口（保留次周期结构），也可以是相量域序分量（物理上可解释，但需要因果的一个周期观测窗口才能获得相量估计）。本文通过标准同步WSCC/IEEE 9节点系统上两种表示对流式故障族分类的受控比较，衡量了该观测窗口的成本，即一个周期的代价。比较保持模拟系统、测量通道、十一类事件族分类法、训练过程和流式决策层不变，并使用六种配置：40样本和80样本的采样值输入以及一个周期的相量输入，每种配置分别用多层感知器（MLP）和一维卷积神经网络（CNN）评估。所有六种配置均成功分类每个事件，但采样值表示在每个模型对中更早达到正确类别。40样本采样值MLP取得最强结果，故障窗口准确率99.34%，正常准确率99.79%，误报率0.21%，漏报率0.65%，平均首次正确延迟2.81毫秒，并在帕累托意义上支配其他五种配置。在80样本处的匹配窗口比较证实，时序优势源于表示本身而非窗口长度。在这个干净的同步基准上，一个周期的相量表示并未返回足以抵消其延迟的准确性或稳定性收益。
英文摘要
Transmission-line fault classification is a time-critical protection function, and recent machine-learning methods classify fault type directly from voltage and current measurements. The input to such a classifier can be supplied as sampled-value windows of raw measurements, which preserve sub-cycle structure, or as phasor-domain sequence components, which are physically interpretable but require a causal one-cycle observation window before a phasor estimate is available. This paper measures the cost of that observation window, the price of a cycle, through a controlled comparison of the two representations for streaming fault-family classification on the standard synchronous WSCC/IEEE 9-bus system. The comparison holds the simulated system, the measured channels, the eleven-class event-family taxonomy, the training procedure, and the streaming decision layer constant, and uses six configurations: 40- and 80-sample sampled-value inputs and one-cycle phasor inputs, each evaluated with a multilayer perceptron (MLP) and a one-dimensional convolutional neural network (CNN). All six configurations classify every event successfully, yet the sampled-value representation reaches the correct class earlier in every model pair. The 40-sample sampled-value MLP attains the strongest result, with 99.34% fault-window accuracy, 99.79% normal accuracy, a 0.21% false-fault rate, a 0.65% false-normal rate, and a 2.81 ms mean first-correct delay, and it Pareto-dominates the other five configurations. A matched-window comparison at eighty samples confirms that the timing advantage follows from the representation rather than from the window length. On this clean synchronous benchmark, the one-cycle phasor representation does not return an accuracy or stability benefit sufficient to offset its delay.
