# A Standardized Framework for Machine Learning in Power System Protection

arXiv: https://arxiv.org/abs/2608.20181 · HTML: https://mlsanity.com/paper/2608.20181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 69 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [Accounting for Variance in Machine Learning Benchmarks](https://mlsanity.com/paper/2103.03098/) (2021)
- [Towards a Rigorous Evaluation of Time-series Anomaly Detection](https://mlsanity.com/paper/2109.05257/) (2021)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management](https://mlsanity.com/paper/2509.09053/) (2025)
- [Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection](https://mlsanity.com/paper/2505.15560/) (2025)
- [Robustness Evaluation of Machine Learning Models for Fault Classification and Localization In Power System Protection](https://mlsanity.com/paper/2512.15385/) (2025)
- [Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection](https://mlsanity.com/paper/2510.00831/) (2025)
- [PROTECT-90: A Fault Dataset for Power System Protection](https://mlsanity.com/paper/2606.24298/) (2026)
- [A large synthetic dataset for machine learning applications in power transmission grids](https://mlsanity.com/paper/2410.03365/) (2024)
- [Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids](https://mlsanity.com/paper/2605.17256/) (2026)
- [Fault Inception Detection in Real-World Disturbance Data for Power System Protection](https://mlsanity.com/paper/2606.23111/) (2026)
- [Transformer is All You Need: Attention-Based Anomaly Detection and Classification in Inverter-Rich Power Systems](https://mlsanity.com/paper/2607.13537/) (2026)
- [Feature Selection for Fault Prediction in Distribution Systems](https://mlsanity.com/paper/2603.25274/) (2026)

## Built on by (most cited first, 1 of 1)

- [EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection](https://mlsanity.com/paper/2609.28149/) (2026)

## Abstract (arXiv)

Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting. Protection task, physical scope, measurements, timing, targets, preprocessing, and validation often vary jointly and remain incompletely specified. This paper proposes a standardization-oriented framework that treats evaluation design as part of the scientific contribution. It defines seven required study dimensions: protection objective, physical scope, observability, timing and decision windows, targets and sample validity, validation protocol, and evaluation outputs. The framework is instantiated in a bounded case study on the public PROTECT-90 electromagnetic-transient benchmark, comprising 9022 simulated episodes from a 90 kV double-line topology, for onset-conditioned fault classification and localization. Under centralized sensing, simulation-metadata-aligned 20 ms windows, and episode-grouped validation, a multi-layer perceptron (MLP) achieved a five-fold mean macro-averaged F1 score of 0.991 +/- 0.001 for classification and a localization mean absolute error of 10.20 +/- 0.25% of line length (mean +/- std across episode-grouped folds). Extending the decision horizon to 50 ms preserved this task-dependent performance asymmetry, while reduced observability approximately doubled the MLP localization error but had little effect on classification. A synchronized two-ended conventional locator outperformed the learning locators under its richer clean information set, and measurement degradation showed that clean predictive performance did not determine robustness. The framework turns evaluation assumptions into explicit, reproducible evidence and provides a basis for more comparable, auditable evaluation and future certification-oriented assessment of machine-learning protection functions.