# A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management

arXiv: https://arxiv.org/abs/2509.09053 · HTML: https://mlsanity.com/paper/2509.09053/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 186 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Learning with Known Operators reduces Maximum Training Error Bounds](https://mlsanity.com/paper/1907.01992/) (2019)
- [Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future](https://mlsanity.com/paper/2108.04543/) (2021)
- [A novel time-frequency Transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings](https://mlsanity.com/paper/2104.09079/) (2021)
- [Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward](https://mlsanity.com/paper/2309.10788/) (2023)
- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection](https://mlsanity.com/paper/2505.15560/) (2025)
- [Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids](https://mlsanity.com/paper/2003.02422/) (2020)
- [A Hybrid Intelligent System for Protection of Transmission Lines Connected to PV Farms based on Linear Trends](https://mlsanity.com/paper/2406.13194/) (2024)

## Built on by (most cited first, 9 of 9)

- [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)
- [Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration](https://mlsanity.com/paper/2601.10044/) (2026)
- [PROTECT-90: A Fault Dataset for Power System Protection](https://mlsanity.com/paper/2606.24298/) (2026)
- [A Standardized Framework for Machine Learning in Power System Protection](https://mlsanity.com/paper/2608.20181/) (2026)
- [Real-Time Surrogate Modeling for Fast Transient Prediction in Inverter-Based Microgrids Using CNN and LightGBM](https://mlsanity.com/paper/2603.29255/) (2026)
- [Offline Reinforcement Learning for Distribution-Grid Protection](https://mlsanity.com/paper/2609.24703/) (2026)
- [EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection](https://mlsanity.com/paper/2609.28149/) (2026)
- [One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark](https://mlsanity.com/paper/2610.04155/) (2026)

## Abstract (arXiv)

The integration of renewable and distributed energy resources reshapes modern power systems, challenging conventional protection schemes. This scoping review synthesizes recent literature on machine learning (ML) applications in power system protection and disturbance management, following the PRISMA for Scoping Reviews framework. Based on over 100 publications, three key objectives are addressed: (i) assessing the scope of ML research in protection tasks; (ii) evaluating ML performance across diverse operational scenarios; and (iii) identifying methods suitable for evolving grid conditions. ML models often demonstrate high accuracy on simulated datasets; however, their performance under real-world conditions remains insufficiently validated. The existing literature is fragmented, with inconsistencies in methodological rigor, dataset quality, and evaluation metrics. This lack of standardization hampers the comparability of results and limits the generalizability of findings. To address these challenges, this review introduces a ML-oriented taxonomy for protection tasks, resolves key terminological inconsistencies, and advocates for standardized reporting practices. It further provides guidelines for comprehensive dataset documentation, methodological transparency, and consistent evaluation protocols, aiming to improve reproducibility and enhance the practical relevance of research outcomes. Critical gaps remain, including the scarcity of real-world validation, insufficient robustness testing, and limited consideration of deployment feasibility. Future research should prioritize public benchmark datasets, realistic validation methods, and advanced ML architectures. These steps are essential to move ML-based protection from theoretical promise to practical deployment in increasingly dynamic and decentralized power systems.