One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark

Emad Abukhousa; Abdulaziz Qwbaiban; Saman Zonouz; A. P. Sakis Meliopoulos

eess.SYOct 2, 2026

One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark

Authors: Emad Abukhousa, Abdulaziz Qwbaiban, Saman Zonouz, A. P. Sakis Meliopoulos

Organizations: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia · Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), KFUPM, Dhahran 31261, Saudi Arabia

Abstract

Open electromagnetic-transient datasets are beginning to make reproducible learning-based protection studies possible, but the practical use of these datasets still requires application-level benchmarks that define timing, sensing, and validation assumptions. This paper presents an initial application benchmark on the recently released PROTECT-90 dataset for two protection-oriented tasks: fault-type classification and discrete faulted-line identification. A compact one-dimensional convolutional neural network (CNN) is evaluated using post-inception windows of 0.25, 0.5, 1, and 2 cycles under strict episode-wise splitting. A non-convolutional multilayer perceptron (MLP) is also trained as an architecture-control baseline. The results show that both tasks are nearly saturated under full observability, with one-cycle test accuracies of 99.84% for fault type and 100.00% for line identification. The main performance variation appears under reduced observability: current-only inputs preserve line identification accuracy at 100.00%, whereas voltage-only inputs reduce line identification accuracy to 53.09% with the CNN and 50.57% with the MLP. This indicates that the limiting factor is measurement information rather than neural architecture. Additional stratified checks show stable performance across topology states and fault-resistance bins, while CPU inference contributes only 0.528 ms to the one-cycle total decision time of 20.53 ms.

Figures & tables

Fig. 1: Single-event early-decision example using a 0.25-cycle post-fault window. The top panel shows the full 1-s current waveform, the middle panel shows the zoomed waveform segment fed to the classifier, and the bottom panel shows the predicted class.

Task	Window	Accuracy	Macro-F1
Fault type	0.25 cyc.	$99.84\%$	$99.84\%$
Fault type	0.5 cyc.	$99.67\%$	$99.67\%$
Fault type	1 cyc.	$99.84\%$	$99.84\%$
Fault type	2 cyc.	$99.92\%$	$99.92\%$
Line ID	0.25 cyc.	$99.92\%$	$99.90\%$
Line ID	0.5 cyc.	$99.92\%$	$99.90\%$

TABLE I: Early-window CNN benchmark on the held-out test set.

Fig. 2: Test accuracy versus post-fault observation window. Accuracy is already near saturated at sub-cycle windows for both tasks under full 48-channel observability.

Fig. 3: Representative one-cycle classification examples from PROTECT-90. Each column corresponds to a different fault event. The top row shows the full 1-s current waveform, the middle row shows the zoomed waveform segment around fault inception, and the bottom row shows the classifier output.

Channel subset	Ch.	Fault type	Line ID
All voltage + current	48	$99.84\%$	$100.00\%$
Current only	24	$99.84\%$	$100.00\%$
Voltage only	24	$99.59\%$	$53.09\%$
Single relay location	6	$99.11\%$	$65.12\%$
Random subset	24	$99.59\%$	$99.02\%$

TABLE II: One-cycle CNN accuracy under different channel subsets.

Task	Channel subset	CNN	MLP
Fault type	All 48 channels	$99.84\%$	$99.84\%$
Fault type	Current only	$99.84\%$	$100.00\%$
Fault type	Voltage only	$99.59\%$	$99.19\%$
Line ID	All 48 channels	$100.00\%$	$99.76\%$
Line ID	Current only	$100.00\%$	$100.00\%$
Line ID	Voltage only	$53.09\%$	$50.57\%$

TABLE III: CNN and MLP one-cycle test accuracy under selected channel subsets.

Check	Fault type	Line ID / time
Topology states	$99.68$ – $100.00\%$	$100.00\%$
Fault resistance bins	$99.35$ – $100.00\%$	$100.00\%$
0.25-cycle timing	$0.594$ ms infer.	$5.59$ ms total
1-cycle timing	$0.528$ ms infer.	$20.53$ ms total
2-cycle timing	$0.813$ ms infer.	$40.81$ ms total

TABLE IV: Secondary one-cycle checks and CPU timing.

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PROTECT-90: A Fault Dataset for Power System Protection

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1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany · Institute of Electrical Energy Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany · Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden, Amberg, Germany

Benchmark Design Synthetic Benchmark Generation Power Systems

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The increasing interest in data-driven methods for power system protection is accompanied by a lack of standardized, publicly available high-voltage waveform datasets that enable transparent and reproducible evaluation. To address this gap, this paper introduces the PROTECT-90 dataset, an open electromagnetic transient (EMT)-simulated reference benchmark for high-voltage fault studies with consistent digital-fault-recorder-like measurements, publicly released with this work. The dataset comprises 9,022 physically consistent short-circuit simulation episodes generated on a standardized 90 kV double-line topology with systematically documented domain randomization of grid operating points, line parameters, and fault conditions. For each episode, synchronized three-phase voltage and current waveforms are recorded at eight measurement locations and released together with structured, machine-readable metadata describing fault type, fault location, inception time, and operating conditions. All modeling assumptions, parameter ranges, and data-generation procedures are explicitly documented to ensure transparency and cross-study comparability. By combining physically grounded EMT simulation, balanced scenario coverage, and open accessibility, PROTECT-90 establishes a standardized foundation for reproducible benchmarking of protection-oriented signal processing and learning-based methods.

Abstract

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