The Price of a Cycle: A Phasor -vs- Sampled-Value Comparison for Streaming Fault Classification on the IEEE 9-Bus System

Abstract

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.